[{"id": "skill:anthropics/skills/skills/academy-guide", "name": "academy-guide", "kind": "skill", "official": true, "desc": "Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: \"how do I\", \"how can I\", \"getting started with\", \"what can Claude do\", \"teach me\", \"learn to use\"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask", "repo": "https://github.com/anthropics/skills/tree/HEAD/skills/academy-guide", "install": ["npx skills add anthropics/skills --skill academy-guide"], "stars": 194218, "pushed": "2026-11-06T22:32:32Z", "license": null, "plat": "claude academy", "live": "Not checked (runs locally)"}, {"id": "skill:anthropics/skills/skills/claude-api", "name": "claude-api", "kind": "skill", "official": true, "desc": "Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer ", "repo": "https://github.com/anthropics/skills/tree/HEAD/skills/claude-api", "install": ["npx skills add anthropics/skills --skill claude-api"], "stars": 194218, "pushed": "2026-11-06T22:32:32Z", "license": null, "plat": "claude api", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:open-webui/open-webui", "name": "open-webui", "kind": "mcp", "official": null, "desc": "User-friendly AI Interface (Supports Ollama, OpenAI API, ...)", "repo": "https://github.com/open-webui/open-webui", "install": [], "stars": 158670, "pushed": null, "license": null, "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "skill:thedotmack/claude-mem/plugin/skills/ccs-align", "name": "ccs-align", "kind": "skill", "official": false, "desc": "Run the CCS Align seat's hourly breathing cycle — prove the local claude-mem worker is healthy, pull needle observations through search → timeline → get_observations, land them in a seat-owned middle cache via atomic grab → append → filter exclude-marks → replace, manage exclude marks, and walk house → project → seat rules to detect conflicts (SHADOW_HOUSE, DENY_ALLOW, DRIFT, CLOCK_HEADER) with an append-only rules-report.md. Use when asked to run CCS Align, breathe the alignment seat, refresh t", "repo": "https://github.com/thedotmack/claude-mem/tree/HEAD/plugin/skills/ccs-align", "install": ["npx skills add thedotmack/claude-mem --skill ccs-align"], "stars": 116897, "pushed": "2026-11-13T18:56:42Z", "license": "Apache-2.0", "plat": "claude-mem", "live": "Not checked (runs locally)"}, {"id": "skill:thedotmack/claude-mem/plugin/skills/mode-creator", "name": "mode-creator", "kind": "skill", "official": false, "desc": "Interactively create, install, activate, and verify custom claude-mem modes, including domain-specific observation types, concept tags, optional Telegram alerts, bot setup, worker restart, and startup-context verification. Use this whenever someone asks to customize what claude-mem remembers, create or change a mode, track domain-specific notes, add observation types or tags, or send Telegram notifications for particular memories—even if they do not use the word \"mode.", "repo": "https://github.com/thedotmack/claude-mem/tree/HEAD/plugin/skills/mode-creator", "install": ["npx skills add thedotmack/claude-mem --skill mode-creator"], "stars": 116897, "pushed": "2026-11-13T18:56:42Z", "license": "Apache-2.0", "plat": "claude-mem", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:google-gemini/gemini-cli", "name": "gemini-cli", "kind": "mcp", "official": null, "desc": "An open-source AI agent that brings the power of Gemini directly into your terminal.", "repo": "https://github.com/google-gemini/gemini-cli", "install": [], "stars": 108834, "pushed": null, "license": null, "plat": "gemini", "live": "Not checked (runs locally)"}, {"id": "skill:nexu-io/open-design/design-templates/html-ppt-hermes-cyber-terminal", "name": "html-ppt-hermes-cyber-terminal", "kind": "skill", "official": false, "desc": "OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.", "repo": "https://github.com/nexu-io/open-design/tree/HEAD/design-templates/html-ppt-hermes-cyber-terminal", "install": ["npx skills add nexu-io/open-design --skill html-ppt-hermes-cyber-terminal"], "stars": 107550, "pushed": "2026-11-14T01:09:21Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:nexu-io/open-design/design-templates/html-ppt-presenter-mode-reveal", "name": "html-ppt-presenter-mode", "kind": "skill", "official": false, "desc": "OpenDesign live demo: from a one-line prompt to runnable design in a single session — the flow, live. Built as a decision-grade AI literacy deck for developers, prospects, community.", "repo": "https://github.com/nexu-io/open-design/tree/HEAD/design-templates/html-ppt-presenter-mode-reveal", "install": ["npx skills add nexu-io/open-design --skill html-ppt-presenter-mode"], "stars": 107550, "pushed": "2026-11-14T01:09:21Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:nexu-io/open-design/design-templates/html-ppt-tech-sharing", "name": "html-ppt-tech-sharing", "kind": "skill", "official": false, "desc": "OpenDesign internals: how the agent stream, sandbox, and artifacts work — an engineering deep-dive talk. Built as a decision-grade AI literacy deck for engineers, dev community.", "repo": "https://github.com/nexu-io/open-design/tree/HEAD/design-templates/html-ppt-tech-sharing", "install": ["npx skills add nexu-io/open-design --skill html-ppt-tech-sharing"], "stars": 107550, "pushed": "2026-11-14T01:09:21Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:cheaper-inference/OmniRoute", "name": "OmniRoute", "kind": "mcp", "official": null, "desc": "Never stop coding. Free MIT AI gateway: one endpoint, 375 providers (150+ free), 1,400+ models Kimi, Claude, GPT, Gemini, GLM, DeepSeek, MiniMax. Works with Claude Code, Codex, Cursor, OpenCode, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, Desktop", "repo": "https://github.com/cheaper-inference/OmniRoute", "install": [], "stars": 87505, "pushed": null, "license": null, "plat": "claude code, codex, cursor, opencode, cl", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/ai-ml-api-automation", "name": "ai-ml-api-automation", "kind": "skill", "official": false, "desc": "Automate AI ML API tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/ai-ml-api-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill ai-ml-api-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "ai ml api", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/anthropic-administrator-automation", "name": "anthropic-administrator-automation", "kind": "skill", "official": false, "desc": "Automate Anthropic Admin tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/anthropic-administrator-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill anthropic-administrator-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/anthropic_administrator-automation", "name": "anthropic_administrator-automation", "kind": "skill", "official": false, "desc": "Automate Anthropic Admin tasks via Rube MCP (Composio): API keys, usage, workspaces, and organization management. Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/anthropic_administrator-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill anthropic_administrator-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/bigml-automation", "name": "bigml-automation", "kind": "skill", "official": false, "desc": "Automate Bigml tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/bigml-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill bigml-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "bigml", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/claid-ai-automation", "name": "claid-ai-automation", "kind": "skill", "official": false, "desc": "Automate Claid AI tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/claid-ai-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill claid-ai-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "claid ai", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/convolo-ai-automation", "name": "convolo-ai-automation", "kind": "skill", "official": false, "desc": "Automate Convolo AI tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/convolo-ai-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill convolo-ai-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "convolo ai", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/customgpt-automation", "name": "customgpt-automation", "kind": "skill", "official": false, "desc": "Automate Customgpt tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/customgpt-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill customgpt-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "customgpt", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/datarobot-automation", "name": "datarobot-automation", "kind": "skill", "official": false, "desc": "Automate Datarobot tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/datarobot-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill datarobot-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "datarobot", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/extracta-ai-automation", "name": "extracta-ai-automation", "kind": "skill", "official": false, "desc": "Automate Extracta AI tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/extracta-ai-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill extracta-ai-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "extracta ai", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/deepspot-m", "name": "deepspot-m", "kind": "skill", "official": false, "desc": "Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/deepspot-m", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill deepspot-m"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "deepspot-m", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/diffdock", "name": "diffdock", "kind": "skill", "official": false, "desc": "Predicts protein-small-molecule binding poses with DiffDock and DiffDock-L from PDB or sequence plus SMILES/SDF/MOL2. Covers batch docking, pose triage, confidence interpretation, and validation. Use for molecular docking and virtual-screening pose generation, not binding-affinity prediction.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/diffdock", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill diffdock"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "diffdock", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/esm", "name": "esm", "kind": "skill", "official": false, "desc": "Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding. Applies to local model inference and Biohub hosted clients, including former Forge workflows; distinguishes the separate legacy fair-esm distribution.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/esm", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill esm"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "biohub esm", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/genomic-intelligence", "name": "genomic-intelligence", "kind": "skill", "official": false, "desc": "Predicts regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene s", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/genomic-intelligence", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill genomic-intelligence"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "genomic intelligence", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/hugging-science", "name": "hugging-science", "kind": "skill", "official": false, "desc": "Discovers and evaluates scientific datasets, models, methodology posts, and Spaces through the Hugging Science catalog. Used when selecting scientific ML resources in biology, chemistry, genomics, materials, climate, physics, astronomy, medicine, mathematics, protein design, single-cell analysis, or PDE modeling, and when checking their actual datasets, Transformers, native-runtime, Inference Providers, or Gradio interfaces.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/hugging-science", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill hugging-science"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "hugging science", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/shap", "name": "shap", "kind": "skill", "official": false, "desc": "Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/shap", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill shap"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "shap", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/stable-baselines3", "name": "stable-baselines3", "kind": "skill", "official": false, "desc": "Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/stable-baselines3", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill stable-baselines3"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "stable baselines3", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/transformers", "name": "transformers", "kind": "skill", "official": false, "desc": "Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/transformers", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill transformers"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/waypoint-bio", "name": "waypoint-bio", "kind": "skill", "official": false, "desc": "Supports work with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the `waypoint` CLI from the `waypoint-bio` package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tabl", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/waypoint-bio", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill waypoint-bio"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "outpost bio", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:mudler/LocalAI", "name": "LocalAI", "kind": "mcp", "official": null, "desc": "LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.", "repo": "https://github.com/mudler/LocalAI", "install": [], "stars": 50368, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-ai-product-evaluation-ops/skills/advanced-evaluation", "name": "advanced-evaluation", "kind": "skill", "official": false, "desc": "Design LLM-as-judge rubrics, direct scores and pairwise comparisons. Use for judge models, evals, position bias and calibration against human labels.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-ai-product-evaluation-ops/skills/advanced-evaluation", "install": ["npx skills add sickn33/agentic-awesome-skills --skill advanced-evaluation"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-ai-product-evaluation-ops/skills/ai-wrapper-product", "name": "ai-wrapper-product", "kind": "skill", "official": false, "desc": "Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc. ) into focused tools people will pay for. Not just \"ChatGPT but different\" - products that solve specific problems with AI.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-ai-product-evaluation-ops/skills/ai-wrapper-product", "install": ["npx skills add sickn33/agentic-awesome-skills --skill ai-wrapper-product"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-devops-cloud/skills/azure-ai-openai-dotnet", "name": "azure-ai-openai-dotnet", "kind": "skill", "official": false, "desc": "Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-devops-cloud/skills/azure-ai-openai-dotnet", "install": ["npx skills add sickn33/agentic-awesome-skills --skill azure-ai-openai-dotnet"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "azure openai", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-ai-product-evaluation-ops/skills/hugging-face-evaluation", "name": "hugging-face-evaluation", "kind": "skill", "official": false, "desc": "Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-ai-product-evaluation-ops/skills/hugging-face-evaluation", "install": ["npx skills add sickn33/agentic-awesome-skills --skill hugging-face-evaluation"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/langfuse", "name": "langfuse", "kind": "skill", "official": false, "desc": "Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/langfuse", "install": ["npx skills add sickn33/agentic-awesome-skills --skill langfuse"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "langfuse", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/llm-app-patterns", "name": "llm-app-patterns", "kind": "skill", "official": false, "desc": "Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/llm-app-patterns", "install": ["npx skills add sickn33/agentic-awesome-skills --skill llm-app-patterns"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/mcp-builder", "name": "mcp-builder", "kind": "skill", "official": false, "desc": "Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/mcp-builder", "install": ["npx skills add sickn33/agentic-awesome-skills --skill mcp-builder"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/mcp-tool-developer", "name": "mcp-tool-developer", "kind": "skill", "official": false, "desc": "Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/mcp-tool-developer", "install": ["npx skills add sickn33/agentic-awesome-skills --skill mcp-tool-developer"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/prompt-caching", "name": "prompt-caching", "kind": "skill", "official": false, "desc": "Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/prompt-caching", "install": ["npx skills add sickn33/agentic-awesome-skills --skill prompt-caching"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/prompt-engineering", "name": "prompt-engineering", "kind": "skill", "official": false, "desc": "Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/prompt-engineering", "install": ["npx skills add sickn33/agentic-awesome-skills --skill prompt-engineering"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/rag-engineer", "name": "rag-engineer", "kind": "skill", "official": false, "desc": "Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/rag-engineer", "install": ["npx skills add sickn33/agentic-awesome-skills --skill rag-engineer"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-agent-mcp-builder/skills/rag-implementation", "name": "rag-implementation", "kind": "skill", "official": false, "desc": "RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-agent-mcp-builder/skills/rag-implementation", "install": ["npx skills add sickn33/agentic-awesome-skills --skill rag-implementation"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:agent-sdk-dev", "name": "agent-sdk-dev", "kind": "claude-plugin", "official": null, "desc": "Development kit for working with the Claude Agent SDK", "repo": "https://github.com/anthropics/claude-plugins-official", "install": ["claude plugin install agent-sdk-dev@claude-plugins-official"], "stars": 47064, "pushed": "2026-11-13T01:14:19Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Kong/kong", "name": "kong", "kind": "mcp", "official": null, "desc": "🦍 The API and AI Gateway", "repo": "https://github.com/Kong/kong", "install": [], "stars": 44378, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/checkpoint-promotion", "name": "checkpoint-promotion", "kind": "skill", "official": false, "desc": "Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a tuned model ships, or when a promoted model needs re-gating against updated goldens.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/checkpoint-promotion", "install": ["npx skills add wshobson/agents --skill checkpoint-promotion"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/dataset-curation", "name": "dataset-curation", "kind": "skill", "official": false, "desc": "Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/dataset-curation", "install": ["npx skills add wshobson/agents --skill dataset-curation"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/eval-harness-first", "name": "eval-harness-first", "kind": "skill", "official": false, "desc": "Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/eval-harness-first", "install": ["npx skills add wshobson/agents --skill eval-harness-first"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/plugin-eval/skills/evaluation-methodology", "name": "evaluation-methodology", "kind": "skill", "official": false, "desc": "PluginEval quality methodology, covering dimensions, rubrics, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when setting score thresholds for your marketplace, or when explaining quality badges to external partners like Neon.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/plugin-eval/skills/evaluation-methodology", "install": ["npx skills add wshobson/agents --skill evaluation-methodology"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/finetuning-method-selection", "name": "finetuning-method-selection", "kind": "skill", "official": false, "desc": "Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/finetuning-method-selection", "install": ["npx skills add wshobson/agents --skill finetuning-method-selection"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/grpo-rlvr-training", "name": "grpo-rlvr-training", "kind": "skill", "official": false, "desc": "Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/grpo-rlvr-training", "install": ["npx skills add wshobson/agents --skill grpo-rlvr-training"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-application-dev/skills/llm-evaluation", "name": "llm-evaluation", "kind": "skill", "official": false, "desc": "Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/llm-evaluation", "install": ["npx skills add wshobson/agents --skill llm-evaluation"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/lora-qlora-recipes", "name": "lora-qlora-recipes", "kind": "skill", "official": false, "desc": "Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/lora-qlora-recipes", "install": ["npx skills add wshobson/agents --skill lora-qlora-recipes"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/machine-learning-ops/skills/ml-pipeline-workflow", "name": "ml-pipeline-workflow", "kind": "skill", "official": false, "desc": "Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/machine-learning-ops/skills/ml-pipeline-workflow", "install": ["npx skills add wshobson/agents --skill ml-pipeline-workflow"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/preference-optimization", "name": "preference-optimization", "kind": "skill", "official": false, "desc": "Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/preference-optimization", "install": ["npx skills add wshobson/agents --skill preference-optimization"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/quantized-export", "name": "quantized-export", "kind": "skill", "official": false, "desc": "Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization format for a target device, or when an exported model fails its smoke test.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/quantized-export", "install": ["npx skills add wshobson/agents --skill quantized-export"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-application-dev/skills/rag-implementation", "name": "rag-implementation", "kind": "skill", "official": false, "desc": "Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/rag-implementation", "install": ["npx skills add wshobson/agents --skill rag-implementation"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/machine-learning-ops/skills/recsys-pipeline-architect", "name": "recsys-pipeline-architect", "kind": "skill", "official": false, "desc": "Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks \"the top K items for a (user, context)\" — content feeds, search ranking, RAG rerankers, task prioritizers, notification triage, ad selection.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/machine-learning-ops/skills/recsys-pipeline-architect", "install": ["npx skills add wshobson/agents --skill recsys-pipeline-architect"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/dgx-spark-ops/skills/spark-environment-setup", "name": "spark-environment-setup", "kind": "skill", "official": false, "desc": "Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/dgx-spark-ops/skills/spark-environment-setup", "install": ["npx skills add wshobson/agents --skill spark-environment-setup"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/dgx-spark-ops/skills/spark-memory-thermal-ops", "name": "spark-memory-thermal-ops", "kind": "skill", "official": false, "desc": "Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/dgx-spark-ops/skills/spark-memory-thermal-ops", "install": ["npx skills add wshobson/agents --skill spark-memory-thermal-ops"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/dgx-spark-ops/skills/spark-training-gotchas", "name": "spark-training-gotchas", "kind": "skill", "official": false, "desc": "Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/dgx-spark-ops/skills/spark-training-gotchas", "install": ["npx skills add wshobson/agents --skill spark-training-gotchas"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/trace-to-training-data", "name": "trace-to-training-data", "kind": "skill", "official": false, "desc": "Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/trace-to-training-data", "install": ["npx skills add wshobson/agents --skill trace-to-training-data"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-finetuning/skills/vision-sft", "name": "vision-sft", "kind": "skill", "official": false, "desc": "Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-finetuning/skills/vision-sft", "install": ["npx skills add wshobson/agents --skill vision-sft"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/ai-prompt-engineering-safety-review", "name": "ai-prompt-engineering-safety-review", "kind": "skill", "official": false, "desc": "Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/ai-prompt-engineering-safety-review", "install": ["npx skills add github/awesome-copilot --skill ai-prompt-engineering-safety-review"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/arize-ai-provider-integration", "name": "arize-ai-provider-integration", "kind": "skill", "official": false, "desc": "Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/arize-ai-provider-integration", "install": ["npx skills add github/awesome-copilot --skill arize-ai-provider-integration"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/arize-annotation", "name": "arize-annotation", "kind": "skill", "official": false, "desc": "Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/arize-annotation", "install": ["npx skills add github/awesome-copilot --skill arize-annotation"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/arize-evaluator", "name": "arize-evaluator", "kind": "skill", "official": false, "desc": "Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/arize-evaluator", "install": ["npx skills add github/awesome-copilot --skill arize-evaluator"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/arize-experiment", "name": "arize-experiment", "kind": "skill", "official": false, "desc": "Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/arize-experiment", "install": ["npx skills add github/awesome-copilot --skill arize-experiment"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/arize-instrumentation", "name": "arize-instrumentation", "kind": "skill", "official": false, "desc": "Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/arize-instrumentation", "install": ["npx skills add github/awesome-copilot --skill arize-instrumentation"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/arize-prompt-optimization", "name": "arize-prompt-optimization", "kind": "skill", "official": false, "desc": "Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/arize-prompt-optimization", "install": ["npx skills add github/awesome-copilot --skill arize-prompt-optimization"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/arize-trace", "name": "arize-trace", "kind": "skill", "official": false, "desc": "Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/arize-trace", "install": ["npx skills add github/awesome-copilot --skill arize-trace"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/create-llms", "name": "create-llms", "kind": "skill", "official": false, "desc": "Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/create-llms", "install": ["npx skills add github/awesome-copilot --skill create-llms"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "llms.txt", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/integrate-context-matic", "name": "integrate-context-matic", "kind": "skill", "official": false, "desc": "Discovers and integrates third-party APIs using the context-matic MCP server. Uses `fetch_api` to find available API SDKs, `ask` for integration guidance, `model_search` and `endpoint_search` for SDK details. Use when the user asks to integrate a third-party API, add an API client, implement features with an external API, or work with any third-party API or SDK.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/integrate-context-matic", "install": ["npx skills add github/awesome-copilot --skill integrate-context-matic"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "context-matic", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/java-mcp-server-generator", "name": "java-mcp-server-generator", "kind": "skill", "official": false, "desc": "Use this skill when the user wants to create a Model Context Protocol (MCP) server in Java or in a Spring Boot app. Also use it when the user wants AI agents, such as GitHub Copilot or Claude, to use Java code, an API, or a database. Use it even if the user does not say \"MCP\". The skill makes a Maven or Gradle project with the MCP Java SDK 2.x, tools, JUnit tests, and client configuration. Do not use it for Quarkus/Quarkiverse MCP servers (use the `quarkus-mcp-server-sse` instruction), for an MC", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/java-mcp-server-generator", "install": ["npx skills add github/awesome-copilot --skill java-mcp-server-generator"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/mcp-cli", "name": "mcp-cli", "kind": "skill", "official": false, "desc": "Interface for MCP (Model Context Protocol) servers via CLI. Use when you need to interact with external tools, APIs, or data sources through MCP servers, list available MCP servers/tools, or call MCP tools from command line.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/mcp-cli", "install": ["npx skills add github/awesome-copilot --skill mcp-cli"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:mukul975/Anthropic-Cybersecurity-Skills/skills/detecting-data-and-model-poisoning", "name": "detecting-data-and-model-poisoning", "kind": "skill", "official": false, "desc": "Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs", "repo": "https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/HEAD/skills/detecting-data-and-model-poisoning", "install": ["npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-data-and-model-poisoning"], "stars": 36105, "pushed": "2026-08-31T04:32:44Z", "license": "Apache-2.0", "plat": "ibm's adversarial robustness toolbox", "live": "Not checked (runs locally)"}, {"id": "skill:mukul975/Anthropic-Cybersecurity-Skills/skills/detecting-indirect-prompt-injection", "name": "detecting-indirect-prompt-injection", "kind": "skill", "official": false, "desc": "Detect and defend against indirect prompt injection hidden in web pages, documents, and images consumed by an agent, via content extraction (HTML/PDF/OCR), normalization, and scanning with LLM Guard's PromptInjection scanner or Hugging Face Prompt Guard 2. Use when an agent ingests untrusted external content and you need to screen it for injected instructions before the LLM processes it.", "repo": "https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/HEAD/skills/detecting-indirect-prompt-injection", "install": ["npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-indirect-prompt-injection"], "stars": 36105, "pushed": "2026-08-31T04:32:44Z", "license": "Apache-2.0", "plat": "llm guard", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/agents-langchain", "name": "agents-langchain", "kind": "skill", "official": false, "desc": "Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/agents-langchain", "install": ["npx skills add davila7/claude-code-templates --skill agents-langchain"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/agents-llamaindex", "name": "agents-llamaindex", "kind": "skill", "official": false, "desc": "Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/agents-llamaindex", "install": ["npx skills add davila7/claude-code-templates --skill agents-llamaindex"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/business-marketing/ai-product", "name": "ai-product", "kind": "skill", "official": false, "desc": "Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.  This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/business-marketing/ai-product", "install": ["npx skills add davila7/claude-code-templates --skill ai-product"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/business-marketing/ai-wrapper-product", "name": "ai-wrapper-product", "kind": "skill", "official": false, "desc": "Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/business-marketing/ai-wrapper-product", "install": ["npx skills add davila7/claude-code-templates --skill ai-wrapper-product"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/claude-code-guide", "name": "claude-code-guide", "kind": "skill", "official": false, "desc": "Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies \"Thinking\" keywords, debugging techniques, and best practices for interacting with the agent.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/claude-code-guide", "install": ["npx skills add davila7/claude-code-templates --skill claude-code-guide"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/distributed-training-accelerate", "name": "distributed-training-accelerate", "kind": "skill", "official": false, "desc": "Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-accelerate", "install": ["npx skills add davila7/claude-code-templates --skill distributed-training-accelerate"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "pytorch", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/distributed-training-deepspeed", "name": "distributed-training-deepspeed", "kind": "skill", "official": false, "desc": "Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-deepspeed", "install": ["npx skills add davila7/claude-code-templates --skill distributed-training-deepspeed"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "deepspeed", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/distributed-training-megatron-core", "name": "distributed-training-megatron-core", "kind": "skill", "official": false, "desc": "Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-megatron-core", "install": ["npx skills add davila7/claude-code-templates --skill distributed-training-megatron-core"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "nvidia megatron-core", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/distributed-training-pytorch-fsdp", "name": "distributed-training-pytorch-fsdp", "kind": "skill", "official": false, "desc": "Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-pytorch-fsdp", "install": ["npx skills add davila7/claude-code-templates --skill distributed-training-pytorch-fsdp"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "pytorch fsdp", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning", "name": "distributed-training-pytorch-lightning", "kind": "skill", "official": false, "desc": "High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-pytorch-lightning", "install": ["npx skills add davila7/claude-code-templates --skill distributed-training-pytorch-lightning"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "pytorch", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/distributed-training-ray-train", "name": "distributed-training-ray-train", "kind": "skill", "official": false, "desc": "Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/distributed-training-ray-train", "install": ["npx skills add davila7/claude-code-templates --skill distributed-training-ray-train"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "ray", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-knowledge-distillation", "name": "emerging-techniques-knowledge-distillation", "kind": "skill", "official": false, "desc": "Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/emerging-techniques-knowledge-distillation", "install": ["npx skills add davila7/claude-code-templates --skill emerging-techniques-knowledge-distillation"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-long-context", "name": "emerging-techniques-long-context", "kind": "skill", "official": false, "desc": "Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/emerging-techniques-long-context", "install": ["npx skills add davila7/claude-code-templates --skill emerging-techniques-long-context"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-model-merging", "name": "emerging-techniques-model-merging", "kind": "skill", "official": false, "desc": "Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/emerging-techniques-model-merging", "install": ["npx skills add davila7/claude-code-templates --skill emerging-techniques-model-merging"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-model-pruning", "name": "emerging-techniques-model-pruning", "kind": "skill", "official": false, "desc": "Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/emerging-techniques-model-pruning", "install": ["npx skills add davila7/claude-code-templates --skill emerging-techniques-model-pruning"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-moe-training", "name": "emerging-techniques-moe-training", "kind": "skill", "official": false, "desc": "Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/emerging-techniques-moe-training", "install": ["npx skills add davila7/claude-code-templates --skill emerging-techniques-moe-training"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "deepspeed", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/emerging-techniques-speculative-decoding", "name": "emerging-techniques-speculative-decoding", "kind": "skill", "official": false, "desc": "Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/emerging-techniques-speculative-decoding", "install": ["npx skills add davila7/claude-code-templates --skill emerging-techniques-speculative-decoding"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/evaluation-bigcode-evaluation-harness", "name": "evaluation-bigcode-evaluation-harness", "kind": "skill", "official": false, "desc": "Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/evaluation-bigcode-evaluation-harness", "install": ["npx skills add davila7/claude-code-templates --skill evaluation-bigcode-evaluation-harness"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/evaluation-lm-evaluation-harness", "name": "evaluation-lm-evaluation-harness", "kind": "skill", "official": false, "desc": "Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/evaluation-lm-evaluation-harness", "install": ["npx skills add davila7/claude-code-templates --skill evaluation-lm-evaluation-harness"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/evaluation-nemo-evaluator", "name": "evaluation-nemo-evaluator", "kind": "skill", "official": false, "desc": "Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/evaluation-nemo-evaluator", "install": ["npx skills add davila7/claude-code-templates --skill evaluation-nemo-evaluator"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/fine-tuning-axolotl", "name": "fine-tuning-axolotl", "kind": "skill", "official": false, "desc": "Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/fine-tuning-axolotl", "install": ["npx skills add davila7/claude-code-templates --skill fine-tuning-axolotl"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "axolotl", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/fine-tuning-llama-factory", "name": "fine-tuning-llama-factory", "kind": "skill", "official": false, "desc": "Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/fine-tuning-llama-factory", "install": ["npx skills add davila7/claude-code-templates --skill fine-tuning-llama-factory"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "llama-factory", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/fine-tuning-peft", "name": "fine-tuning-peft", "kind": "skill", "official": false, "desc": "Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/fine-tuning-peft", "install": ["npx skills add davila7/claude-code-templates --skill fine-tuning-peft"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "peft", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/fine-tuning-unsloth", "name": "fine-tuning-unsloth", "kind": "skill", "official": false, "desc": "Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/fine-tuning-unsloth", "install": ["npx skills add davila7/claude-code-templates --skill fine-tuning-unsloth"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "unsloth", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/gemini", "name": "gemini", "kind": "skill", "official": false, "desc": "Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/gemini", "install": ["npx skills add davila7/claude-code-templates --skill gemini"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "gemini", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/gemini-api-agent-platform", "name": "gemini-api-agent-platform", "kind": "skill", "official": false, "desc": "Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK for enterprise AI applications. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/gemini-api-agent-platform", "install": ["npx skills add davila7/claude-code-templates --skill gemini-api-agent-platform"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "gemini", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/inference-serving-llama-cpp", "name": "inference-serving-llama-cpp", "kind": "skill", "official": false, "desc": "Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/inference-serving-llama-cpp", "install": ["npx skills add davila7/claude-code-templates --skill inference-serving-llama-cpp"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "llama-cpp", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/inference-serving-sglang", "name": "inference-serving-sglang", "kind": "skill", "official": false, "desc": "Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/inference-serving-sglang", "install": ["npx skills add davila7/claude-code-templates --skill inference-serving-sglang"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "sglang", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/inference-serving-tensorrt-llm", "name": "inference-serving-tensorrt-llm", "kind": "skill", "official": false, "desc": "Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/inference-serving-tensorrt-llm", "install": ["npx skills add davila7/claude-code-templates --skill inference-serving-tensorrt-llm"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/inference-serving-vllm", "name": "inference-serving-vllm", "kind": "skill", "official": false, "desc": "Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/inference-serving-vllm", "install": ["npx skills add davila7/claude-code-templates --skill inference-serving-vllm"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/llm-ops", "name": "llm-ops", "kind": "skill", "official": false, "desc": "LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/llm-ops", "install": ["npx skills add davila7/claude-code-templates --skill llm-ops"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/model-architecture-litgpt", "name": "model-architecture-litgpt", "kind": "skill", "official": false, "desc": "Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/model-architecture-litgpt", "install": ["npx skills add davila7/claude-code-templates --skill model-architecture-litgpt"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "litgpt", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/model-architecture-mamba", "name": "model-architecture-mamba", "kind": "skill", "official": false, "desc": "State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/model-architecture-mamba", "install": ["npx skills add davila7/claude-code-templates --skill model-architecture-mamba"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "mamba", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/model-architecture-nanogpt", "name": "model-architecture-nanogpt", "kind": "skill", "official": false, "desc": "Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/model-architecture-nanogpt", "install": ["npx skills add davila7/claude-code-templates --skill model-architecture-nanogpt"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "nanogpt", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/model-architecture-rwkv", "name": "model-architecture-rwkv", "kind": "skill", "official": false, "desc": "RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/model-architecture-rwkv", "install": ["npx skills add davila7/claude-code-templates --skill model-architecture-rwkv"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "rwkv", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/model-architecture-torchtitan", "name": "model-architecture-torchtitan", "kind": "skill", "official": false, "desc": "Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/model-architecture-torchtitan", "install": ["npx skills add davila7/claude-code-templates --skill model-architecture-torchtitan"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "torchtitan", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/multimodal-clip", "name": "multimodal-clip", "kind": "skill", "official": false, "desc": "OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/multimodal-clip", "install": ["npx skills add davila7/claude-code-templates --skill multimodal-clip"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/multimodal-llava", "name": "multimodal-llava", "kind": "skill", "official": false, "desc": "Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/multimodal-llava", "install": ["npx skills add davila7/claude-code-templates --skill multimodal-llava"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "vicuna/llama", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/multimodal-segment-anything", "name": "multimodal-segment-anything", "kind": "skill", "official": false, "desc": "Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/multimodal-segment-anything", "install": ["npx skills add davila7/claude-code-templates --skill multimodal-segment-anything"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "segment-anything", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/multimodal-stable-diffusion", "name": "multimodal-stable-diffusion", "kind": "skill", "official": false, "desc": "State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/multimodal-stable-diffusion", "install": ["npx skills add davila7/claude-code-templates --skill multimodal-stable-diffusion"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "stable diffusion", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/multimodal-whisper", "name": "multimodal-whisper", "kind": "skill", "official": false, "desc": "OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/multimodal-whisper", "install": ["npx skills add davila7/claude-code-templates --skill multimodal-whisper"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/openai-docs", "name": "openai-docs", "kind": "skill", "official": false, "desc": "Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations (for example: Codex, Responses API, Chat Completions, Apps SDK, Agents SDK, Realtime, model capabilities or limits); prioritize OpenAI docs MCP tools and restrict any fallback browsing to official OpenAI domains.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/openai-docs", "install": ["npx skills add davila7/claude-code-templates --skill openai-docs"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/optimization-awq", "name": "optimization-awq", "kind": "skill", "official": false, "desc": "Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/optimization-awq", "install": ["npx skills add davila7/claude-code-templates --skill optimization-awq"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "awq", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/optimization-bitsandbytes", "name": "optimization-bitsandbytes", "kind": "skill", "official": false, "desc": "Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/optimization-bitsandbytes", "install": ["npx skills add davila7/claude-code-templates --skill optimization-bitsandbytes"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "bitsandbytes", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/optimization-flash-attention", "name": "optimization-flash-attention", "kind": "skill", "official": false, "desc": "Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/optimization-flash-attention", "install": ["npx skills add davila7/claude-code-templates --skill optimization-flash-attention"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "flash attention", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/optimization-gguf", "name": "optimization-gguf", "kind": "skill", "official": false, "desc": "GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/optimization-gguf", "install": ["npx skills add davila7/claude-code-templates --skill optimization-gguf"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "gguf", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/optimization-gptq", "name": "optimization-gptq", "kind": "skill", "official": false, "desc": "Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/optimization-gptq", "install": ["npx skills add davila7/claude-code-templates --skill optimization-gptq"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "gptq", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/optimization-hqq", "name": "optimization-hqq", "kind": "skill", "official": false, "desc": "Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/optimization-hqq", "install": ["npx skills add davila7/claude-code-templates --skill optimization-hqq"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "hqq", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-grpo-rl-training", "name": "post-training-grpo-rl-training", "kind": "skill", "official": false, "desc": "Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-grpo-rl-training", "install": ["npx skills add davila7/claude-code-templates --skill post-training-grpo-rl-training"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "trl", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-miles", "name": "post-training-miles", "kind": "skill", "official": false, "desc": "Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-miles", "install": ["npx skills add davila7/claude-code-templates --skill post-training-miles"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "miles", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-openrlhf", "name": "post-training-openrlhf", "kind": "skill", "official": false, "desc": "High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-openrlhf", "install": ["npx skills add davila7/claude-code-templates --skill post-training-openrlhf"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "openrlhf", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-simpo", "name": "post-training-simpo", "kind": "skill", "official": false, "desc": "Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-simpo", "install": ["npx skills add davila7/claude-code-templates --skill post-training-simpo"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "simpo", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-slime", "name": "post-training-slime", "kind": "skill", "official": false, "desc": "Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-slime", "install": ["npx skills add davila7/claude-code-templates --skill post-training-slime"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "slime", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-torchforge", "name": "post-training-torchforge", "kind": "skill", "official": false, "desc": "Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-torchforge", "install": ["npx skills add davila7/claude-code-templates --skill post-training-torchforge"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "torchforge", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-trl-fine-tuning", "name": "post-training-trl-fine-tuning", "kind": "skill", "official": false, "desc": "Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-trl-fine-tuning", "install": ["npx skills add davila7/claude-code-templates --skill post-training-trl-fine-tuning"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "trl", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/post-training-verl", "name": "post-training-verl", "kind": "skill", "official": false, "desc": "Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/post-training-verl", "install": ["npx skills add davila7/claude-code-templates --skill post-training-verl"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "verl", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/rag-sentence-transformers", "name": "rag-sentence-transformers", "kind": "skill", "official": false, "desc": "Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/rag-sentence-transformers", "install": ["npx skills add davila7/claude-code-templates --skill rag-sentence-transformers"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "sentence transformers", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/safety-alignment-constitutional-ai", "name": "safety-alignment-constitutional-ai", "kind": "skill", "official": false, "desc": "Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/safety-alignment-constitutional-ai", "install": ["npx skills add davila7/claude-code-templates --skill safety-alignment-constitutional-ai"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "constitutional ai", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/safety-alignment-llamaguard", "name": "safety-alignment-llamaguard", "kind": "skill", "official": false, "desc": "Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/safety-alignment-llamaguard", "install": ["npx skills add davila7/claude-code-templates --skill safety-alignment-llamaguard"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "llama guard", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/safety-alignment-nemo-guardrails", "name": "safety-alignment-nemo-guardrails", "kind": "skill", "official": false, "desc": "NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/safety-alignment-nemo-guardrails", "install": ["npx skills add davila7/claude-code-templates --skill safety-alignment-nemo-guardrails"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "nemo guardrails", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/tokenization-huggingface-tokenizers", "name": "tokenization-huggingface-tokenizers", "kind": "skill", "official": false, "desc": "Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/tokenization-huggingface-tokenizers", "install": ["npx skills add davila7/claude-code-templates --skill tokenization-huggingface-tokenizers"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "huggingface tokenizers", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/tokenization-sentencepiece", "name": "tokenization-sentencepiece", "kind": "skill", "official": false, "desc": "Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/tokenization-sentencepiece", "install": ["npx skills add davila7/claude-code-templates --skill tokenization-sentencepiece"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "sentencepiece", "live": "Not checked (runs locally)"}, {"id": "skill:topoteretes/cognee/.agents/skills/cognee-integrations", "name": "cognee-integrations", "kind": "skill", "official": false, "desc": "Use when the user wants to connect cognee to external services — switching LLM or embedding providers (OpenAI, Azure, Gemini, Anthropic, Ollama, OpenRouter), changing databases (Postgres, PGVector, Neo4j, Neptune, Turso), S3 storage, or the MCP server for IDE integration.", "repo": "https://github.com/topoteretes/cognee/tree/HEAD/.agents/skills/cognee-integrations", "install": ["npx skills add topoteretes/cognee --skill cognee-integrations"], "stars": 33299, "pushed": "2026-11-13T23:04:08Z", "license": "Apache-2.0", "plat": "cognee", "live": "Not checked (runs locally)"}, {"id": "skill:googleworkspace/cli/skills/gws-modelarmor", "name": "gws-modelarmor", "kind": "skill", "official": false, "desc": "Google Model Armor: Filter user-generated content for safety.", "repo": "https://github.com/googleworkspace/cli/tree/HEAD/skills/gws-modelarmor", "install": ["npx skills add googleworkspace/cli --skill gws-modelarmor"], "stars": 32308, "pushed": "2026-11-03T01:37:22Z", "license": "Apache-2.0", "plat": "google model armor", "live": "Not checked (runs locally)"}, {"id": "skill:googleworkspace/cli/skills/gws-modelarmor-create-template", "name": "gws-modelarmor-create-template", "kind": "skill", "official": false, "desc": "Google Model Armor: Create a new Model Armor template.", "repo": "https://github.com/googleworkspace/cli/tree/HEAD/skills/gws-modelarmor-create-template", "install": ["npx skills add googleworkspace/cli --skill gws-modelarmor-create-template"], "stars": 32308, "pushed": "2026-11-03T01:37:22Z", "license": "Apache-2.0", "plat": "google model armor", "live": "Not checked (runs locally)"}, {"id": "skill:googleworkspace/cli/skills/gws-modelarmor-sanitize-prompt", "name": "gws-modelarmor-sanitize-prompt", "kind": "skill", "official": false, "desc": "Google Model Armor: Sanitize a user prompt through a Model Armor template.", "repo": "https://github.com/googleworkspace/cli/tree/HEAD/skills/gws-modelarmor-sanitize-prompt", "install": ["npx skills add googleworkspace/cli --skill gws-modelarmor-sanitize-prompt"], "stars": 32308, "pushed": "2026-11-03T01:37:22Z", "license": "Apache-2.0", "plat": "google model armor", "live": "Not checked (runs locally)"}, {"id": "skill:googleworkspace/cli/skills/gws-modelarmor-sanitize-response", "name": "gws-modelarmor-sanitize-response", "kind": "skill", "official": false, "desc": "Google Model Armor: Sanitize a model response through a Model Armor template.", "repo": "https://github.com/googleworkspace/cli/tree/HEAD/skills/gws-modelarmor-sanitize-response", "install": ["npx skills add googleworkspace/cli --skill gws-modelarmor-sanitize-response"], "stars": 32308, "pushed": "2026-11-03T01:37:22Z", "license": "Apache-2.0", "plat": "google model armor", "live": "Not checked (runs locally)"}, {"id": "skill:nanocoai/nanoclaw/.claude/skills/add-atomic-chat-tool", "name": "add-atomic-chat-tool", "kind": "skill", "official": false, "desc": "Add Atomic Chat MCP server so the container agent can call local models served by the Atomic Chat desktop app via its OpenAI-compatible API.", "repo": "https://github.com/nanocoai/nanoclaw/tree/HEAD/.claude/skills/add-atomic-chat-tool", "install": ["npx skills add nanocoai/nanoclaw --skill add-atomic-chat-tool"], "stars": 31114, "pushed": "2026-11-13T20:45:07Z", "license": "MIT", "plat": "nanoclaw", "live": "Not checked (runs locally)"}, {"id": "skill:nanocoai/nanoclaw/.claude/skills/add-codex", "name": "add-codex", "kind": "skill", "official": false, "desc": "Use Codex (OpenAI's codex app-server) as a full agent provider — planning, tool orchestration, MCP tools, server-side history, session resume — alongside or instead of Claude. ChatGPT subscription or OpenAI API key, vault-only via the selected gateway. Per-group via `ncl groups config update --provider codex`. Distinct from using OpenAI as an MCP tool (where Claude remains the planner).", "repo": "https://github.com/nanocoai/nanoclaw/tree/HEAD/.claude/skills/add-codex", "install": ["npx skills add nanocoai/nanoclaw --skill add-codex"], "stars": 31114, "pushed": "2026-11-13T20:45:07Z", "license": "MIT", "plat": "nanoclaw", "live": "Not checked (runs locally)"}, {"id": "skill:nanocoai/nanoclaw/.claude/skills/add-cursor", "name": "add-cursor", "kind": "skill", "official": false, "desc": "Use Cursor (local Agent SDK) as a full agent provider — planning, tool orchestration, MCP tools, session resume — alongside or instead of Claude. Cursor-account sign-in or API key, held by the credential gateway. Per-group via `ncl groups config update --provider cursor`. Distinct from using Cursor as a cloud agent (those run on Cursor VMs and are not this provider).", "repo": "https://github.com/nanocoai/nanoclaw/tree/HEAD/.claude/skills/add-cursor", "install": ["npx skills add nanocoai/nanoclaw --skill add-cursor"], "stars": 31114, "pushed": "2026-11-13T20:45:07Z", "license": "MIT", "plat": "nanoclaw", "live": "Not checked (runs locally)"}, {"id": "skill:nanocoai/nanoclaw/.claude/skills/add-ollama-provider", "name": "add-ollama-provider", "kind": "skill", "official": false, "desc": "Route a NanoClaw agent group to a local Ollama model instead of the Anthropic API. Ollama speaks the Anthropic API natively (v1/messages), so no provider code changes are needed — just env var overrides and a model setting. Use when the user wants to run their agent locally, cut API costs, or experiment with open-weight models. See docs/ollama.md for background.", "repo": "https://github.com/nanocoai/nanoclaw/tree/HEAD/.claude/skills/add-ollama-provider", "install": ["npx skills add nanocoai/nanoclaw --skill add-ollama-provider"], "stars": 31114, "pushed": "2026-11-13T20:45:07Z", "license": "MIT", "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "skill:nanocoai/nanoclaw/.claude/skills/add-ollama-tool", "name": "add-ollama-tool", "kind": "skill", "official": false, "desc": "Add Ollama MCP server so the container agent can call local models and optionally manage the Ollama model library.", "repo": "https://github.com/nanocoai/nanoclaw/tree/HEAD/.claude/skills/add-ollama-tool", "install": ["npx skills add nanocoai/nanoclaw --skill add-ollama-tool"], "stars": 31114, "pushed": "2026-11-13T20:45:07Z", "license": "MIT", "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "skill:nanocoai/nanoclaw/.claude/skills/add-opencode", "name": "add-opencode", "kind": "skill", "official": false, "desc": "Use OpenCode as an agent provider. OpenRouter, OpenAI, Google, DeepSeek, etc. via OpenCode config — not the Anthropic Agent SDK. Per group via `ncl groups config update --provider opencode`; host passes OPENCODE_* and XDG mount when spawning containers.", "repo": "https://github.com/nanocoai/nanoclaw/tree/HEAD/.claude/skills/add-opencode", "install": ["npx skills add nanocoai/nanoclaw --skill add-opencode"], "stars": 31114, "pushed": "2026-11-13T20:45:07Z", "license": "MIT", "plat": "opencode", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor", "name": "chief-ai-officer-advisor", "kind": "skill", "official": false, "desc": "Use for AI strategy decisions: API vs fine-tune vs build, EU AI Act or US state risk classification, when self-hosting pays off, or AI hiring order. Runs model_buildvsbuy_calculator.py, ai_risk_classifier.py and ai_cost_economics.py.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor", "install": ["npx skills add alirezarezvani/claude-skills --skill chief-ai-officer-advisor"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/engineering/claude-coach/skills/claude-coach", "name": "claude-coach", "kind": "skill", "official": false, "desc": "Use when the user wants to get better at using Claude ('coach me', 'teach me Claude tricks', 'make me a power user'). Rates their prompts with prompt_rater.py and surfaces one relevant power-user tip per turn after activation.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering/claude-coach/skills/claude-coach", "install": ["npx skills add alirezarezvani/claude-skills --skill claude-coach"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/engineering/kubernetes-operator/skills/kubernetes-operator", "name": "kubernetes-operator", "kind": "skill", "official": false, "desc": "Use when building a Kubernetes operator or CRD (controller-runtime, kubebuilder, operator-sdk, KOPF), before writing the controller. Validates CRDs with crd_validator.py, lints reconcile loops with reconcile_lint.py and scores OperatorHub capability level.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering/kubernetes-operator/skills/kubernetes-operator", "install": ["npx skills add alirezarezvani/claude-skills --skill kubernetes-operator"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/engineering/llm-cost-optimizer/skills/llm-cost-optimizer", "name": "llm-cost-optimizer", "kind": "skill", "official": false, "desc": "Use whenever an LLM feature is being built or its costs come up (model choice, prompt caching, token limits, per-feature spend). Sets routing, caching, max_tokens and cost logging before launch rather than after the bill.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering/llm-cost-optimizer/skills/llm-cost-optimizer", "install": ["npx skills add alirezarezvani/claude-skills --skill llm-cost-optimizer"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/engineering/prompt-governance/skills/prompt-governance", "name": "prompt-governance", "kind": "skill", "official": false, "desc": "Use when prompts run in production and need versioning, a registry, A/B tests, regression checks or an eval pipeline. Sets up the release process for prompts; single-prompt quality: senior-prompt-engineer.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering/prompt-governance/skills/prompt-governance", "install": ["npx skills add alirezarezvani/claude-skills --skill prompt-governance"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/engineering-team/skills/senior-computer-vision", "name": "senior-computer-vision", "kind": "skill", "official": false, "desc": "Computer vision models: object detection, segmentation, training a custom model, and optimizing or deploying inference (YOLO, DETR, SAM, ONNX, TensorRT). Use whenever the user wants to detect objects in images or video, train on their own labeled images, or speed up a slow vision model.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering-team/skills/senior-computer-vision", "install": ["npx skills add alirezarezvani/claude-skills --skill senior-computer-vision"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/engineering-team/skills/senior-ml-engineer", "name": "senior-ml-engineer", "kind": "skill", "official": false, "desc": "Use for putting ML models or LLM features into production: deployment, MLOps, drift monitoring, RAG pipelines, LLM API cost and retries. Deploys with model_deployment_pipeline.py, monitors with ml_monitoring_suite.py and builds RAG with rag_system_builder.py.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering-team/skills/senior-ml-engineer", "install": ["npx skills add alirezarezvani/claude-skills --skill senior-ml-engineer"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:openai/skills/skills/.curated/chatgpt-apps", "name": "chatgpt-apps", "kind": "skill", "official": false, "desc": "Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.", "repo": "https://github.com/openai/skills/tree/HEAD/skills/.curated/chatgpt-apps", "install": ["npx skills add openai/skills --skill chatgpt-apps"], "stars": 28730, "pushed": "2026-10-28T17:31:41Z", "license": null, "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelcontextprotocol/python-sdk", "name": "python-sdk", "kind": "mcp", "official": null, "desc": "The official Python SDK for Model Context Protocol servers and clients", "repo": "https://github.com/modelcontextprotocol/python-sdk", "install": [], "stars": 24865, "pushed": null, "license": null, "plat": "model context protocol", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/bigquery-ai-ml", "name": "bigquery-ai-ml", "kind": "skill", "official": false, "desc": "Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, generate d", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/bigquery-ai-ml", "install": ["npx skills add google/skills --skill bigquery-ai-ml"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "bigquery", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/developing-genkit-dart", "name": "developing-genkit-dart", "kind": "skill", "official": false, "desc": "Provides guidance on developing AI-powered applications using the Genkit Dart SDK. Use when the user asks to build AI agents in Dart, use Genkit flows, or integrate LLMs into Dart/Flutter applications. Don't use for Genkit in JavaScript/TypeScript, Go, or Python.", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/developing-genkit-dart", "install": ["npx skills add google/skills --skill developing-genkit-dart"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "genkit", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/developing-genkit-go", "name": "developing-genkit-go", "kind": "skill", "official": false, "desc": "Provides guidance on developing AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers. Don't use for Genkit in JavaScript/TypeScript, Python, or Dart.", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/developing-genkit-go", "install": ["npx skills add google/skills --skill developing-genkit-go"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "genkit", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/developing-genkit-js", "name": "developing-genkit-js", "kind": "skill", "official": false, "desc": "Provides guidance on developing AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems. Don't use for Genkit in Python, Go, or Dart.", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/developing-genkit-js", "install": ["npx skills add google/skills --skill developing-genkit-js"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "genkit", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/gemini-api", "name": "gemini-api", "kind": "skill", "official": false, "desc": "Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/gemini-api", "install": ["npx skills add google/skills --skill gemini-api"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "gemini api", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/gemini-live-api", "name": "gemini-live-api", "kind": "skill", "official": false, "desc": "Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving `ClientMessage`/`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image/video generation, or fine-tuning — ", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/gemini-live-api", "install": ["npx skills add google/skills --skill gemini-live-api"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "gemini liveapi", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/gke-inference", "name": "gke-inference", "kind": "skill", "official": false, "desc": "Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG with Cloud SQL/AlloyDB (use google-cloud-solution-rag-enterprise-search-gke-sqldb), or batch/HPC (use gke-batch-hpc).", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/gke-inference", "install": ["npx skills add google/skills --skill gke-inference"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "google kubernetes engine (gke)", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/platform-devices/litert-google-tensor", "name": "litert-google-tensor", "kind": "skill", "official": false, "desc": "Compiles LiteRT (.tflite) models ahead-of-time for Google Tensor TPUs (Tensor G5 on Pixel 10, Tensor G6 on Pixel 11) locally on Linux x86_64 or on Google Colab (--colab on macOS, Windows, or ARM hosts), benchmarks them on a USB-attached Pixel with LiteRT's benchmark_model (NPU latency, peak memory, TPU tile time, TPU energy, optional CPU baseline), and packages compiled models for an Android Gradle app as a Google Play AI pack with the Tensor NPU runtime module and a Kotlin loader, applies it to", "repo": "https://github.com/google/skills/tree/HEAD/skills/platform-devices/litert-google-tensor", "install": ["npx skills add google/skills --skill litert-google-tensor"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "google tensor", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:rocketride-org/rocketride-server", "name": "rocketride-server", "kind": "mcp", "official": null, "desc": "High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8+ vector databases, and agent orchestration, all from your IDE. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.", "repo": "https://github.com/rocketride-org/rocketride-server", "install": [], "stars": 20037, "pushed": null, "license": null, "plat": "rocketride-server", "live": "Not checked (runs locally)"}, {"id": "skill:wanshuiyin/Auto-claude-code-research-in-sleep/skills/auto-review-loop-llm", "name": "auto-review-loop-llm", "kind": "skill", "official": false, "desc": "Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with \"auto review loop llm\" or \"llm review\".", "repo": "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/auto-review-loop-llm", "install": ["npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-llm"], "stars": 19162, "pushed": "2026-11-11T07:39:57Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wanshuiyin/Auto-claude-code-research-in-sleep/skills/auto-review-loop-minimax", "name": "auto-review-loop-minimax", "kind": "skill", "official": false, "desc": "Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with \"auto review loop minimax\" or \"minimax review\".", "repo": "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/auto-review-loop-minimax", "install": ["npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-minimax"], "stars": 19162, "pushed": "2026-11-11T07:39:57Z", "license": "MIT", "plat": "minimax", "live": "Not checked (runs locally)"}, {"id": "skill:wanshuiyin/Auto-claude-code-research-in-sleep/skills/paper-write", "name": "paper-write", "kind": "skill", "official": false, "desc": "Draft LaTeX paper section by section from an outline. Use when user says \\\"写论文\\\", \\\"write paper\\\", \\\"draft LaTeX\\\", \\\"开始写\\\", or wants to generate LaTeX content from a paper plan.", "repo": "https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/HEAD/skills/paper-write", "install": ["npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-write"], "stars": 19162, "pushed": "2026-11-11T07:39:57Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:muratcankoylan/Agent-Skills-for-Context-Engineering/skills/advanced-evaluation", "name": "advanced-evaluation", "kind": "skill", "official": false, "desc": "This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.", "repo": "https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/tree/HEAD/skills/advanced-evaluation", "install": ["npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill advanced-evaluation"], "stars": 18347, "pushed": "2026-10-12T02:06:40Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:muratcankoylan/Agent-Skills-for-Context-Engineering/examples/book-sft-pipeline", "name": "book-sft-pipeline", "kind": "skill", "official": false, "desc": "This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.", "repo": "https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/tree/HEAD/examples/book-sft-pipeline", "install": ["npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill book-sft-pipeline"], "stars": 18347, "pushed": "2026-10-12T02:06:40Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:muratcankoylan/Agent-Skills-for-Context-Engineering/examples/interleaved-thinking/generated_skills/comprehensive-research-agent", "name": "comprehensive-research-agent", "kind": "skill", "official": false, "desc": "Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls", "repo": "https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/tree/HEAD/examples/interleaved-thinking/generated_skills/comprehensive-research-agent", "install": ["npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill comprehensive-research-agent"], "stars": 18347, "pushed": "2026-10-12T02:06:40Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:miuuyy/codex-chatgpt-web", "name": "codex-chatgpt-web", "kind": "mcp", "official": null, "desc": "Use ChatGPT Web (including Pro) as a native model in Codex — with context, tools, streaming and images, without using Codex quota.", "repo": "https://github.com/miuuyy/codex-chatgpt-web", "install": [], "stars": 15729, "pushed": null, "license": null, "plat": "chatgpt web", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/10-optimization/awq", "name": "awq-quantization", "kind": "skill", "official": false, "desc": "Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/awq", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill awq-quantization"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "awq", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/07-safety-alignment/constitutional-ai", "name": "constitutional-ai", "kind": "skill", "official": false, "desc": "Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/07-safety-alignment/constitutional-ai", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill constitutional-ai"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/01-model-architecture/torchtitan", "name": "distributed-llm-pretraining-torchtitan", "kind": "skill", "official": false, "desc": "Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/01-model-architecture/torchtitan", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill distributed-llm-pretraining-torchtitan"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/11-evaluation/bigcode-evaluation-harness", "name": "evaluating-code-models", "kind": "skill", "official": false, "desc": "Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/11-evaluation/bigcode-evaluation-harness", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/11-evaluation/lm-evaluation-harness", "name": "evaluating-llms-harness", "kind": "skill", "official": false, "desc": "Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/11-evaluation/lm-evaluation-harness", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill evaluating-llms-harness"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/trl-fine-tuning", "name": "fine-tuning-with-trl", "kind": "skill", "official": false, "desc": "Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/trl-fine-tuning", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill fine-tuning-with-trl"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/10-optimization/gguf", "name": "gguf-quantization", "kind": "skill", "official": false, "desc": "GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/gguf", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill gguf-quantization"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "llama.cpp", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/10-optimization/gptq", "name": "gptq", "kind": "skill", "official": false, "desc": "Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/gptq", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill gptq"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "gptq", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/grpo-rl-training", "name": "grpo-rl-training", "kind": "skill", "official": false, "desc": "Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/grpo-rl-training", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill grpo-rl-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/10-optimization/hqq", "name": "hqq-quantization", "kind": "skill", "official": false, "desc": "Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/hqq", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill hqq-quantization"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "hqq", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/02-tokenization/huggingface-tokenizers", "name": "huggingface-tokenizers", "kind": "skill", "official": false, "desc": "Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/02-tokenization/huggingface-tokenizers", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill huggingface-tokenizers"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/01-model-architecture/litgpt", "name": "implementing-llms-litgpt", "kind": "skill", "official": false, "desc": "Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/01-model-architecture/litgpt", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill implementing-llms-litgpt"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "litgpt", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/19-emerging-techniques/knowledge-distillation", "name": "knowledge-distillation", "kind": "skill", "official": false, "desc": "Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/19-emerging-techniques/knowledge-distillation", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill knowledge-distillation"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/14-agents/langchain", "name": "langchain", "kind": "skill", "official": false, "desc": "Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/14-agents/langchain", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill langchain"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/12-inference-serving/llama-cpp", "name": "llama-cpp", "kind": "skill", "official": false, "desc": "Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/12-inference-serving/llama-cpp", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill llama-cpp"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "llama.cpp", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/07-safety-alignment/llamaguard", "name": "llamaguard", "kind": "skill", "official": false, "desc": "Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/07-safety-alignment/llamaguard", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaguard"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/14-agents/llamaindex", "name": "llamaindex", "kind": "skill", "official": false, "desc": "Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/14-agents/llamaindex", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaindex"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/19-emerging-techniques/long-context", "name": "long-context", "kind": "skill", "official": false, "desc": "Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/19-emerging-techniques/long-context", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill long-context"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/01-model-architecture/mamba", "name": "mamba-architecture", "kind": "skill", "official": false, "desc": "State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/01-model-architecture/mamba", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill mamba-architecture"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "mamba", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/miles", "name": "miles-rl-training", "kind": "skill", "official": false, "desc": "Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/miles", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill miles-rl-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/19-emerging-techniques/model-merging", "name": "model-merging", "kind": "skill", "official": false, "desc": "Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/19-emerging-techniques/model-merging", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/19-emerging-techniques/model-pruning", "name": "model-pruning", "kind": "skill", "official": false, "desc": "Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/19-emerging-techniques/model-pruning", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-pruning"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/19-emerging-techniques/moe-training", "name": "moe-training", "kind": "skill", "official": false, "desc": "Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/19-emerging-techniques/moe-training", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill moe-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/01-model-architecture/nanogpt", "name": "nanogpt", "kind": "skill", "official": false, "desc": "Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/01-model-architecture/nanogpt", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill nanogpt"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "gpt", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/05-data-processing/nemo-curator", "name": "nemo-curator", "kind": "skill", "official": false, "desc": "GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/05-data-processing/nemo-curator", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill nemo-curator"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/11-evaluation/nemo-evaluator", "name": "nemo-evaluator-sdk", "kind": "skill", "official": false, "desc": "Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/11-evaluation/nemo-evaluator", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill nemo-evaluator-sdk"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "nvidia nemo", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/07-safety-alignment/nemo-guardrails", "name": "nemo-guardrails", "kind": "skill", "official": false, "desc": "NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/07-safety-alignment/nemo-guardrails", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill nemo-guardrails"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/04-mechanistic-interpretability/nnsight", "name": "nnsight-remote-interpretability", "kind": "skill", "official": false, "desc": "Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/nnsight", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill nnsight-remote-interpretability"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/openrlhf", "name": "openrlhf-training", "kind": "skill", "official": false, "desc": "High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/openrlhf", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill openrlhf-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/10-optimization/flash-attention", "name": "optimizing-attention-flash", "kind": "skill", "official": false, "desc": "Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/flash-attention", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill optimizing-attention-flash"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "flashattention", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/03-fine-tuning/peft", "name": "peft-fine-tuning", "kind": "skill", "official": false, "desc": "Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/03-fine-tuning/peft", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill peft-fine-tuning"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/07-safety-alignment/prompt-guard", "name": "prompt-guard", "kind": "skill", "official": false, "desc": "Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/07-safety-alignment/prompt-guard", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill prompt-guard"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/04-mechanistic-interpretability/pyvene", "name": "pyvene-interventions", "kind": "skill", "official": false, "desc": "Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/pyvene", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill pyvene-interventions"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/10-optimization/bitsandbytes", "name": "quantizing-models-bitsandbytes", "kind": "skill", "official": false, "desc": "Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/bitsandbytes", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill quantizing-models-bitsandbytes"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "bitsandbytes", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/05-data-processing/ray-data", "name": "ray-data", "kind": "skill", "official": false, "desc": "Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/05-data-processing/ray-data", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill ray-data"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/01-model-architecture/rwkv", "name": "rwkv-architecture", "kind": "skill", "official": false, "desc": "RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/01-model-architecture/rwkv", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill rwkv-architecture"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "rwkv", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/15-rag/sentence-transformers", "name": "sentence-transformers", "kind": "skill", "official": false, "desc": "Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/15-rag/sentence-transformers", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentence-transformers"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/02-tokenization/sentencepiece", "name": "sentencepiece", "kind": "skill", "official": false, "desc": "Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/02-tokenization/sentencepiece", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill sentencepiece"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/12-inference-serving/vllm", "name": "serving-llms-vllm", "kind": "skill", "official": false, "desc": "Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/12-inference-serving/vllm", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill serving-llms-vllm"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "vllm", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/12-inference-serving/sglang", "name": "sglang", "kind": "skill", "official": false, "desc": "Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/12-inference-serving/sglang", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill sglang"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "sglang", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/simpo", "name": "simpo-training", "kind": "skill", "official": false, "desc": "Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/simpo", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill simpo-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/slime", "name": "slime-rl-training", "kind": "skill", "official": false, "desc": "Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/slime", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill slime-rl-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/04-mechanistic-interpretability/saelens", "name": "sparse-autoencoder-training", "kind": "skill", "official": false, "desc": "Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/saelens", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill sparse-autoencoder-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/19-emerging-techniques/speculative-decoding", "name": "speculative-decoding", "kind": "skill", "official": false, "desc": "Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/19-emerging-techniques/speculative-decoding", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill speculative-decoding"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/12-inference-serving/tensorrt-llm", "name": "tensorrt-llm", "kind": "skill", "official": false, "desc": "Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/12-inference-serving/tensorrt-llm", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill tensorrt-llm"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "tensorrt", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/torchforge", "name": "torchforge-rl-training", "kind": "skill", "official": false, "desc": "Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/torchforge", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill torchforge-rl-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/08-distributed-training/megatron-core", "name": "training-llms-megatron", "kind": "skill", "official": false, "desc": "Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/08-distributed-training/megatron-core", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill training-llms-megatron"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "megatron", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/04-mechanistic-interpretability/transformer-lens", "name": "transformer-lens-interpretability", "kind": "skill", "official": false, "desc": "Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/04-mechanistic-interpretability/transformer-lens", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill transformer-lens-interpretability"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/03-fine-tuning/unsloth", "name": "unsloth", "kind": "skill", "official": false, "desc": "Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/03-fine-tuning/unsloth", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill unsloth"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/06-post-training/verl", "name": "verl-rl-training", "kind": "skill", "official": false, "desc": "Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/06-post-training/verl", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill verl-rl-training"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:MiniMax-AI/skills/skills/minimax-multimodal-toolkit", "name": "minimax-multimodal-toolkit", "kind": "skill", "official": false, "desc": "Use mmx to generate text, images, video, and speech, and to transcribe audio, via the MiniMax AI platform. Use when the user wants to create media content, chat with MiniMax models, transcribe audio to text, perform web search, or manage MiniMax API resources from the terminal.", "repo": "https://github.com/MiniMax-AI/skills/tree/HEAD/skills/minimax-multimodal-toolkit", "install": ["npx skills add MiniMax-AI/skills --skill minimax-multimodal-toolkit"], "stars": 13830, "pushed": "2026-11-10T15:35:17Z", "license": "MIT", "plat": "minimax ai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelcontextprotocol/typescript-sdk", "name": "typescript-sdk", "kind": "mcp", "official": null, "desc": "The official TypeScript SDK for Model Context Protocol servers and clients", "repo": "https://github.com/modelcontextprotocol/typescript-sdk", "install": [], "stars": 13770, "pushed": null, "license": null, "plat": "model context protocol", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Portkey-AI/gateway", "name": "gateway", "kind": "mcp", "official": null, "desc": "A blazing fast AI Gateway with integrated guardrails. Route to 1,600+ LLMs, 50+ AI Guardrails with 1 fast & friendly API.", "repo": "https://github.com/Portkey-AI/gateway", "install": [], "stars": 13380, "pushed": null, "license": null, "plat": "ai gateway", "live": "Not checked (runs locally)"}, {"id": "skill:Jeffallan/claude-skills/skills/fine-tuning-expert", "name": "fine-tuning-expert", "kind": "skill", "official": false, "desc": "Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model t", "repo": "https://github.com/Jeffallan/claude-skills/tree/HEAD/skills/fine-tuning-expert", "install": ["npx skills add Jeffallan/claude-skills --skill fine-tuning-expert"], "stars": 12355, "pushed": "2026-11-11T08:34:48Z", "license": "MIT", "plat": "llms", "live": "Not checked (runs locally)"}, {"id": "skill:Jeffallan/claude-skills/skills/rag-architect", "name": "rag-architect", "kind": "skill", "official": false, "desc": "Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.", "repo": "https://github.com/Jeffallan/claude-skills/tree/HEAD/skills/rag-architect", "install": ["npx skills add Jeffallan/claude-skills --skill rag-architect"], "stars": 12355, "pushed": "2026-11-11T08:34:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:tadata-org/fastapi_mcp", "name": "fastapi_mcp", "kind": "mcp", "official": null, "desc": "Expose your FastAPI endpoints as Model Context Protocol (MCP) tools, with Auth!", "repo": "https://github.com/tadata-org/fastapi_mcp", "install": [], "stars": 12031, "pushed": null, "license": null, "plat": "model context protocol", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:LaurieWired/GhidraMCP", "name": "GhidraMCP", "kind": "mcp", "official": null, "desc": "MCP Server for Ghidra", "repo": "https://github.com/LaurieWired/GhidraMCP", "install": [], "stars": 11932, "pushed": null, "license": null, "plat": "ghidra", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:BeehiveInnovations/pal-mcp-server", "name": "pal-mcp-server", "kind": "mcp", "official": null, "desc": "The power of Claude Code / GeminiCLI / CodexCLI + [Gemini / OpenAI / OpenRouter / Azure / Grok / Ollama / Custom Model / All Of The Above] working as one.", "repo": "https://github.com/BeehiveInnovations/pal-mcp-server", "install": [], "stars": 11839, "pushed": null, "license": null, "plat": "claude, gemini, openai, etc", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.getagentseal/codeburn", "name": "CodeBurn", "kind": "mcp", "official": null, "desc": "AI coding spend, tokens and plan quotas across Claude Code, Codex, Cursor and 40+ more tools", "repo": "https://github.com/getagentseal/codeburn", "install": ["npm:codeburn"], "stars": 11784, "pushed": "2026-11-09T17:28:51Z", "license": "MIT", "plat": "ai coding tools", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/hf-cli", "name": "hf-cli", "kind": "skill", "official": false, "desc": "Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; config", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/hf-cli", "install": ["npx skills add huggingface/skills --skill hf-cli"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/hf-mcp/skills/hf-mcp", "name": "hf-mcp", "kind": "skill", "official": false, "desc": "Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.", "repo": "https://github.com/huggingface/skills/tree/HEAD/hf-mcp/skills/hf-mcp", "install": ["npx skills add huggingface/skills --skill hf-mcp"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/hf-mem", "name": "hf-mem", "kind": "skill", "official": false, "desc": "Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/hf-mem", "install": ["npx skills add huggingface/skills --skill hf-mem"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/huggingface-best", "name": "huggingface-best", "kind": "skill", "official": false, "desc": "Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: \"best model for X\", \"what model should I use for\", \"top models for [task]\", \"which model runs on my laptop/machine/device\", \"recommend a model for\", \"what LLM should I use for\", \"compare models for\", \"what's state of the art for\", or any question about choosing an AI model for a specific use case. Always use this ski", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/huggingface-best", "install": ["npx skills add huggingface/skills --skill huggingface-best"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/huggingface-community-evals", "name": "huggingface-community-evals", "kind": "skill", "official": false, "desc": "Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/huggingface-community-evals", "install": ["npx skills add huggingface/skills --skill huggingface-community-evals"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/huggingface-llm-trainer", "name": "huggingface-llm-trainer", "kind": "skill", "official": false, "desc": "Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks ", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/huggingface-llm-trainer", "install": ["npx skills add huggingface/skills --skill huggingface-llm-trainer"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/huggingface-local-models", "name": "huggingface-local-models", "kind": "skill", "official": false, "desc": "Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/huggingface-local-models", "install": ["npx skills add huggingface/skills --skill huggingface-local-models"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:huggingface-skills", "name": "huggingface-skills", "kind": "claude-plugin", "official": null, "desc": "Build, train, evaluate, and use open source AI models, datasets, and spaces.", "repo": "https://github.com/huggingface/skills.git", "install": ["claude plugin install huggingface-skills@claude-plugins-official"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/huggingface-tool-builder", "name": "huggingface-tool-builder", "kind": "skill", "official": false, "desc": "Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/huggingface-tool-builder", "install": ["npx skills add huggingface/skills --skill huggingface-tool-builder"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/huggingface-trackio", "name": "huggingface-trackio", "kind": "skill", "official": false, "desc": "Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/huggingface-trackio", "install": ["npx skills add huggingface/skills --skill huggingface-trackio"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "trackio", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/huggingface-vision-trainer", "name": "huggingface-vision-trainer", "kind": "skill", "official": false, "desc": "Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Tr", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/huggingface-vision-trainer", "install": ["npx skills add huggingface/skills --skill huggingface-vision-trainer"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/train-sentence-transformers", "name": "train-sentence-transformers", "kind": "skill", "official": false, "desc": "Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection,", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/train-sentence-transformers", "install": ["npx skills add huggingface/skills --skill train-sentence-transformers"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/transformers-js", "name": "transformers-js", "kind": "skill", "official": false, "desc": "Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/transformers-js", "install": ["npx skills add huggingface/skills --skill transformers-js"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:huggingface/skills/skills/trl-training", "name": "trl-training", "kind": "skill", "official": false, "desc": "Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.", "repo": "https://github.com/huggingface/skills/tree/HEAD/skills/trl-training", "install": ["npx skills add huggingface/skills --skill trl-training"], "stars": 11341, "pushed": "2026-11-11T09:45:07Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:mcp-use/mcp-use", "name": "mcp-use", "kind": "mcp", "official": null, "desc": "The fullstack MCP framework to develop MCP Apps for ChatGPT / Claude & MCP Servers for AI Agents.", "repo": "https://github.com/mcp-use/mcp-use", "install": [], "stars": 10965, "pushed": null, "license": null, "plat": "chatgpt / claude", "live": "Not checked (runs locally)"}, {"id": "skill:android/skills/device-ai/litert/litert-lm", "name": "litert-lm", "kind": "skill", "official": false, "desc": "Builds an Android app that runs an open text LLM on the device, on the CPU or the GPU, with the LiteRT-LM Kotlin API (com.google.ai.edge.litertlm:litertlm-android). Use this skill when creating a chat, summarization, or extraction feature with a .litertlm model from Hugging Face litert-community (Gemma, Qwen, Llama, Phi), including the Gradle dependency, the GPU manifest entries, getting the model file onto the device, Engine initialization off the main thread, a streamed multi-turn Conversation", "repo": "https://github.com/android/skills/tree/HEAD/device-ai/litert/litert-lm", "install": ["npx skills add android/skills --skill litert-lm"], "stars": 10503, "pushed": "2026-11-11T06:04:32Z", "license": "Apache-2.0", "plat": "android", "live": "Not checked (runs locally)"}, {"id": "skill:android/skills/device-ai/litert/litert-runtime", "name": "litert-runtime", "kind": "skill", "official": false, "desc": "Configures and implements LiteRT (.tflite) on-device inference on Android in Kotlin using the CompiledModel API on CPU and GPU. Use this skill for any Android LiteRT or .tflite task, including Gradle setup, image classification or vision/audio/embedding helpers, TensorBuffer reuse, CPU memory optimization, Bitmap preprocessing, AndroidViewModel GPU-to-CPU fallback with warm-up on a single-thread dispatcher, LiteRT in Google Play services (play-services-litert), migrating from legacy org.tensorfl", "repo": "https://github.com/android/skills/tree/HEAD/device-ai/litert/litert-runtime", "install": ["npx skills add android/skills --skill litert-runtime"], "stars": 10503, "pushed": "2026-11-11T06:04:32Z", "license": "Apache-2.0", "plat": "android", "live": "Not checked (runs locally)"}, {"id": "skill:android/skills/device-ai/ml-kit-genai-prompt-api", "name": "ml-kit-genai-prompt-api", "kind": "skill", "official": false, "desc": "Analyzes Android codebases to implement ML Kit GenAI Prompt API. Use this skill to send natural language requests on-device to Gemini Nano, use structured output with Prompt API, implement prefix caching, optimize the current prompt, or apply best practices.\"", "repo": "https://github.com/android/skills/tree/HEAD/device-ai/ml-kit-genai-prompt-api", "install": ["npx skills add android/skills --skill ml-kit-genai-prompt-api"], "stars": 10503, "pushed": "2026-11-11T06:04:32Z", "license": "Apache-2.0", "plat": "android", "live": "Not checked (runs locally)"}, {"id": "skill:nexu-io/openmotion/.agents/skills/add-byok-provider", "name": "add-byok-provider", "kind": "skill", "official": false, "desc": "Add or maintain a built-in BYOK media provider in the OpenMotion repository, including official API research, credentials, model discovery, generation adapters, editor configuration, provider references and verification. Use for contributor requests to integrate a supplier; ordinary media generation uses the existing production guidance.", "repo": "https://github.com/nexu-io/openmotion/tree/HEAD/.agents/skills/add-byok-provider", "install": ["npx skills add nexu-io/openmotion --skill add-byok-provider"], "stars": 9863, "pushed": "2026-11-14T04:35:22Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelcontextprotocol/modelcontextprotocol", "name": "modelcontextprotocol", "kind": "mcp", "official": null, "desc": "Specification and documentation for the Model Context Protocol", "repo": "https://github.com/modelcontextprotocol/modelcontextprotocol", "install": [], "stars": 9692, "pushed": null, "license": null, "plat": "model context protocol", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:maximhq/bifrost", "name": "bifrost", "kind": "mcp", "official": null, "desc": "Open-source, high-performance AI gateway that works as your LLM gateway, MCP gateway and agent gateway. One OpenAI-compatible API for 10,000+ models, with failover, load balancing, semantic caching, virtual keys, budgets and MCP Code Mode. Works with any SDK or agent and adds under 200µs of overhead", "repo": "https://github.com/maximhq/bifrost", "install": [], "stars": 9556, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:anbeime/skill/skills/expert-prompt-migrator", "name": "expert-prompt-migrator", "kind": "skill", "official": false, "desc": "将任意专家/角色提示词规范化为可移植格式并迁移到任意 AI（DeepSeek、豆包、Kimi、Claude、Ollama 等）的实用工具。内置「清理平台语法 / 单专家单任务 / 补背景信息」三大原则、五段式提示词结构、迁移自检清单，以及 6 个原创领域专家示例（文案/简历/数据/法律/财务/英语）。", "repo": "https://github.com/anbeime/skill/tree/HEAD/skills/expert-prompt-migrator", "install": ["npx skills add anbeime/skill --skill expert-prompt-migrator"], "stars": 9543, "pushed": "2026-11-14T02:12:26Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:mark3labs/mcp-go", "name": "mcp-go", "kind": "mcp", "official": null, "desc": "A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools.", "repo": "https://github.com/mark3labs/mcp-go", "install": [], "stars": 9238, "pushed": null, "license": null, "plat": "go", "live": "Not checked (runs locally)"}, {"id": "skill:deanpeters/Product-Manager-Skills/plugins/pm-develop/skills/ai-shaped-readiness-advisor", "name": "ai-shaped-readiness-advisor", "kind": "skill", "official": false, "desc": "Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to build.", "repo": "https://github.com/deanpeters/Product-Manager-Skills/tree/HEAD/plugins/pm-develop/skills/ai-shaped-readiness-advisor", "install": ["npx skills add deanpeters/Product-Manager-Skills --skill ai-shaped-readiness-advisor"], "stars": 8014, "pushed": "2026-11-13T15:19:58Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:lijigang/ljg-skills/skills/ljg-word", "name": "ljg-word", "kind": "skill", "official": false, "desc": "Deep-dive English word mastery tool. Deconstructs a single English word into core semantics and epiphany. Use when user asks to explain/master a specific English word.", "repo": "https://github.com/lijigang/ljg-skills/tree/HEAD/skills/ljg-word", "install": ["npx skills add lijigang/ljg-skills --skill ljg-word"], "stars": 7740, "pushed": "2026-11-02T09:23:57Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:lijigang/ljg-skills/skills/ljg-writes", "name": "ljg-writes", "kind": "skill", "official": false, "desc": "写作引擎。把一个观点写成可理解、可迁移、经得住反例的 1000-1500 字中文文章。USE WHEN 写文章 OR 优化思想内容 OR 展开观点 OR 改写成逻辑递进的中文。NOT FOR 普通摘要、事实查询或结构风洞（用 ljg-structure）。", "repo": "https://github.com/lijigang/ljg-skills/tree/HEAD/skills/ljg-writes", "install": ["npx skills add lijigang/ljg-skills --skill ljg-writes"], "stars": 7740, "pushed": "2026-11-02T09:23:57Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:irinabuht12-oss/marketing-skills/skills/ai-visibility-audit", "name": "ai-visibility-audit", "kind": "skill", "official": false, "desc": "Audit how visible your brand is inside AI answers (ChatGPT, Claude, Gemini, Perplexity, AI Overviews). Claude builds a prompt panel for your category, scores where you show up vs competitors, and turns the gaps into a prioritized fix list. Platform: AI visibility.", "repo": "https://github.com/irinabuht12-oss/marketing-skills/tree/HEAD/skills/ai-visibility-audit", "install": ["npx skills add irinabuht12-oss/marketing-skills --skill ai-visibility-audit"], "stars": 7542, "pushed": "2026-10-27T08:57:53Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:irinabuht12-oss/marketing-skills/skills/brand-answer-monitoring", "name": "brand-answer-monitoring", "kind": "skill", "official": false, "desc": "Track how AI assistants describe your brand over time and catch wrong, outdated, or damaging claims. Claude builds your monitoring prompt set, compares runs week over week, and drafts the correction plan when answers drift. Platform: AI visibility.", "repo": "https://github.com/irinabuht12-oss/marketing-skills/tree/HEAD/skills/brand-answer-monitoring", "install": ["npx skills add irinabuht12-oss/marketing-skills --skill brand-answer-monitoring"], "stars": 7542, "pushed": "2026-10-27T08:57:53Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:irinabuht12-oss/marketing-skills/skills/citation-source-gap-finder", "name": "citation-source-gap-finder", "kind": "skill", "official": false, "desc": "Find the exact pages AI assistants cite when answering questions in your category, and identify which ones you can get into. Claude clusters the cited sources, scores each by attainability, and outputs an outreach/content plan. Platform: AI visibility.", "repo": "https://github.com/irinabuht12-oss/marketing-skills/tree/HEAD/skills/citation-source-gap-finder", "install": ["npx skills add irinabuht12-oss/marketing-skills --skill citation-source-gap-finder"], "stars": 7542, "pushed": "2026-10-27T08:57:53Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:irinabuht12-oss/marketing-skills/skills/content-aeo-optimizer", "name": "content-aeo-optimizer", "kind": "skill", "official": false, "desc": "Rewrite existing pages so AI assistants can extract, quote, and cite them. Claude restructures your content into answer-ready blocks — direct answers, comparison tables, stats with sources, schema — without wrecking the human reading experience. Platform: AI visibility.", "repo": "https://github.com/irinabuht12-oss/marketing-skills/tree/HEAD/skills/content-aeo-optimizer", "install": ["npx skills add irinabuht12-oss/marketing-skills --skill content-aeo-optimizer"], "stars": 7542, "pushed": "2026-10-27T08:57:53Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:internet-court/internet-court-skill/vendored/0g/0g-compute", "name": "0g-compute", "kind": "skill", "official": false, "desc": "0G Compute Network guide for decentralized AI inference, fine-tuning, and GPU services. Covers chatbots, image generation, speech-to-text, SDK integration (0g-serving-broker), processResponse API, broker.inference methods, CLI commands (0g-compute-cli), and account management. Use this skill for any 0G compute, 0G AI, or decentralized GPU question.", "repo": "https://github.com/internet-court/internet-court-skill/tree/HEAD/vendored/0g/0g-compute", "install": ["npx skills add internet-court/internet-court-skill --skill 0g-compute"], "stars": 7120, "pushed": "2026-10-26T12:55:11Z", "license": "NOASSERTION", "plat": "0g compute", "live": "Not checked (runs locally)"}, {"id": "skill:internet-court/internet-court-skill/vendored/altlayer/altllm-portal-api-keys", "name": "altllm-portal-api-keys", "kind": "skill", "official": false, "desc": "Use this skill when the user asks to list, create, inspect, update, disable, re-enable, or revoke AltLLM Portal API keys for external agents or applications. Do NOT use for wallet login, billing history, or payment links.", "repo": "https://github.com/internet-court/internet-court-skill/tree/HEAD/vendored/altlayer/altllm-portal-api-keys", "install": ["npx skills add internet-court/internet-court-skill --skill altllm-portal-api-keys"], "stars": 7120, "pushed": "2026-10-26T12:55:11Z", "license": "NOASSERTION", "plat": "altllm portal", "live": "Not checked (runs locally)"}, {"id": "skill:internet-court/internet-court-skill/vendored/altlayer/altllm-portal-auth", "name": "altllm-portal-auth", "kind": "skill", "official": false, "desc": "Use this skill when the user asks to log in or out with a wallet session, fetch a wallet sign-in challenge, verify an externally signed challenge, or troubleshoot AltLLM Portal wallet login for the local altllm CLI. Do NOT use for API key management, billing history, or payment links.", "repo": "https://github.com/internet-court/internet-court-skill/tree/HEAD/vendored/altlayer/altllm-portal-auth", "install": ["npx skills add internet-court/internet-court-skill --skill altllm-portal-auth"], "stars": 7120, "pushed": "2026-10-26T12:55:11Z", "license": "NOASSERTION", "plat": "altllm portal", "live": "Not checked (runs locally)"}, {"id": "skill:internet-court/internet-court-skill/vendored/altlayer/altllm-portal-cli", "name": "altllm-portal-cli", "kind": "skill", "official": false, "desc": "Use this umbrella skill when the request spans multiple AltLLM Portal CLI domains, or when you need to navigate the local altllm CLI in this repository across auth, API keys, billing history, NOWPayments payment links, and related x402 Portal top-up guidance.", "repo": "https://github.com/internet-court/internet-court-skill/tree/HEAD/vendored/altlayer/altllm-portal-cli", "install": ["npx skills add internet-court/internet-court-skill --skill altllm-portal-cli"], "stars": 7120, "pushed": "2026-10-26T12:55:11Z", "license": "NOASSERTION", "plat": "altllm portal", "live": "Not checked (runs locally)"}, {"id": "skill:internet-court/internet-court-skill/vendored/antseed/antseed-connect", "name": "antseed-connect", "kind": "skill", "official": false, "desc": "Connect coding agents, AI SDKs, and LLM tools to the AntSeed buyer proxy. Use when configuring Claude Code, Codex, OpenCode, Pi, OpenClaw, Hermes, GenLayer Studio, Vercel AI SDK, LangChain, or raw HTTP to route inference through AntSeed at localhost:8377.", "repo": "https://github.com/internet-court/internet-court-skill/tree/HEAD/vendored/antseed/antseed-connect", "install": ["npx skills add internet-court/internet-court-skill --skill antseed-connect"], "stars": 7120, "pushed": "2026-10-26T12:55:11Z", "license": "NOASSERTION", "plat": "antseed", "live": "Not checked (runs locally)"}, {"id": "skill:internet-court/internet-court-skill/vendored/chaingpt/chaingpt", "name": "chaingpt", "kind": "skill", "official": false, "desc": "Build with the ChainGPT Web3 AI developer platform. Full API/SDK reference and project scaffolding for: Web3 AI Chatbot & LLM, AI NFT Generator, Smart Contract Generator, Smart Contract Auditor, AI Crypto News, AgenticOS Twitter agents, and Solidity LLM. Use when building blockchain apps, Web3 chatbots, NFT tools, smart contract tools, crypto news feeds, AI agents, or integrating any ChainGPT API. Triggers: chaingpt, web3 ai, nft generator, smart contract audit, crypto news api, agenticos, solid", "repo": "https://github.com/internet-court/internet-court-skill/tree/HEAD/vendored/chaingpt/chaingpt", "install": ["npx skills add internet-court/internet-court-skill --skill chaingpt"], "stars": 7120, "pushed": "2026-10-26T12:55:11Z", "license": "NOASSERTION", "plat": "chaingpt", "live": "Not checked (runs locally)"}, {"id": "skill:internet-court/internet-court-skill/vendored/near/near-ai-cloud", "name": "near-ai-cloud", "kind": "skill", "official": false, "desc": "NEAR AI Cloud private inference and verification. Use when integrating NEAR AI Cloud API for verifiable private AI inference, verifying model or gateway TEE attestation (NVIDIA NRAS, Intel TDX), verifying chat message signatures, implementing end-to-end encrypted chat, or using the OpenAI-compatible API with NEAR AI Cloud.", "repo": "https://github.com/internet-court/internet-court-skill/tree/HEAD/vendored/near/near-ai-cloud", "install": ["npx skills add internet-court/internet-court-skill --skill near-ai-cloud"], "stars": 7120, "pushed": "2026-10-26T12:55:11Z", "license": "NOASSERTION", "plat": "near ai cloud", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jacob-bd/gemini-notebook-mcp-cli", "name": "gemini-notebook-mcp-cli", "kind": "mcp", "official": null, "desc": "Programmatic access to Gemini Notebook - via command-line interface (CLI), Model Context Protocol (MCP) server, and AI agent skills.", "repo": "https://github.com/jacob-bd/gemini-notebook-mcp-cli", "install": [], "stars": 6512, "pushed": null, "license": null, "plat": "gemini notebook", "live": "Not checked (runs locally)"}, {"id": "skill:ThinkInAIXYZ/deepchat/.agents/skills/add-provider", "name": "add-provider", "kind": "skill", "official": false, "desc": "Add a DeepChat LLM provider through explicit reviewed source changes. Use when a developer asks Codex to add a provider, provider profile, upstream provider config, model catalog mapping, provider auth behavior, or a special provider adapter in this repository.", "repo": "https://github.com/ThinkInAIXYZ/deepchat/tree/HEAD/.agents/skills/add-provider", "install": ["npx skills add ThinkInAIXYZ/deepchat --skill add-provider"], "stars": 6390, "pushed": "2026-11-13T03:04:31Z", "license": "Apache-2.0", "plat": "deepchat", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ThinkInAIXYZ/deepchat", "name": "deepchat", "kind": "mcp", "official": null, "desc": "🐬DeepChat - A smart assistant that connects powerful AI to your personal world", "repo": "https://github.com/ThinkInAIXYZ/deepchat", "install": [], "stars": 6390, "pushed": "2026-11-13T03:04:31Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:ThinkInAIXYZ/deepchat/resources/skills/deepchat-cli", "name": "deepchat-cli", "kind": "skill", "official": false, "desc": "Use DeepChat's bundled CLI control plane for model inference, image/video/speech generation, transcription, OCR, artifact inspection, public configuration, Skills, and MCP operations. Activate when a user asks to invoke DeepChat capabilities that are not already exposed as a more specific tool, compare models, run a benchmark, inspect DeepChat runtime state, or manage DeepChat through the CLI.", "repo": "https://github.com/ThinkInAIXYZ/deepchat/tree/HEAD/resources/skills/deepchat-cli", "install": ["npx skills add ThinkInAIXYZ/deepchat --skill deepchat-cli"], "stars": 6390, "pushed": "2026-11-13T03:04:31Z", "license": "Apache-2.0", "plat": "deepchat", "live": "Not checked (runs locally)"}, {"id": "skill:ThinkInAIXYZ/deepchat/.agents/skills/deepchat-sdd", "name": "deepchat-sdd", "kind": "skill", "official": false, "desc": "Use before substantial DeepChat code, configuration, documentation, test, build, feature, issue, refactor, or architecture changes that need a durable RFC and an explicit execution path. Skip trivial style fixes, small localized logic changes, routine docs edits, and simple bugs unless the developer asks for SDD. Use plan.md as the only separate tracker when needed, default to implementation-first validation, and ask before optional GitHub issue sync unless the developer explicitly requested syn", "repo": "https://github.com/ThinkInAIXYZ/deepchat/tree/HEAD/.agents/skills/deepchat-sdd", "install": ["npx skills add ThinkInAIXYZ/deepchat --skill deepchat-sdd"], "stars": 6390, "pushed": "2026-11-13T03:04:31Z", "license": "Apache-2.0", "plat": "deepchat", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:lemonade-sdk/lemonade", "name": "lemonade", "kind": "mcp", "official": null, "desc": "Lemonade helps users discover and run local AI apps by serving optimized LLMs right from their own GPUs and NPUs. Join our discord: https://discord.gg/5xXzkMu8Zk", "repo": "https://github.com/lemonade-sdk/lemonade", "install": [], "stars": 6212, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:dotnet/skills/plugins/dotnet-ai/skills/technology-selection", "name": "technology-selection", "kind": "skill", "official": false, "desc": "Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector searc", "repo": "https://github.com/dotnet/skills/tree/HEAD/plugins/dotnet-ai/skills/technology-selection", "install": ["npx skills add dotnet/skills --skill technology-selection"], "stars": 6155, "pushed": "2026-11-13T20:11:12Z", "license": "MIT", "plat": "ml.net", "live": "Not checked (runs locally)"}, {"id": "skill:humanlayer/skills/plugins/improve-claude-md/skills/improve-claude-md", "name": "improve-claude-md", "kind": "skill", "official": false, "desc": "improve a CLAUDE.md file using <important if> blocks to improve instruction adherence", "repo": "https://github.com/humanlayer/skills/tree/HEAD/plugins/improve-claude-md/skills/improve-claude-md", "install": ["npx skills add humanlayer/skills --skill improve-claude-md"], "stars": 5773, "pushed": "2026-11-01T21:13:11Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:kajisho5/ffmpeg-skill/.claude/skills/mcp-server-design", "name": "building-python-mcp-servers", "kind": "skill", "official": false, "desc": "Builds robust Python MCP (Model Context Protocol) servers with FastMCP — tool design, error contracts, event-loop-safe blocking work, subprocess/CLI wrapping, single-file vs packaged distribution, global-state-free testing, and prompt-injection awareness. Use when writing an MCP server, exposing a tool or CLI to an LLM client, debugging tool registration/packaging, or testing MCP tools.", "repo": "https://github.com/kajisho5/ffmpeg-skill/tree/HEAD/.claude/skills/mcp-server-design", "install": ["npx skills add kajisho5/ffmpeg-skill --skill building-python-mcp-servers"], "stars": 5372, "pushed": "2026-11-13T12:36:28Z", "license": "MIT", "plat": "python", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.pollinations/pollinations", "name": "Pollinations", "kind": "mcp", "official": null, "desc": "Access Pollinations models and API capabilities through agent tools.", "repo": "https://github.com/pollinations/pollinations", "install": ["https://gen.pollinations.ai/mcp/pollinations"], "stars": 5294, "pushed": "2026-11-13T02:50:28Z", "license": "MIT", "plat": "pollinations", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "skill:mcncarl/yichen-skills/yichen-chatgpt-web-research", "name": "yichen-chatgpt-web-research", "kind": "skill", "official": false, "desc": "Use the user's already signed-in official ChatGPT website account, especially GPT-5.5 Pro / ChatGPT Pro, to perform product research, market research, competitor research, second-opinion analysis, or any request that says ChatGPT 官网, ChatGPT 网页版, GPT-5.5 Pro, 5.5 Pro, 用官网调研, 产品调研, 市场调研, or avoid API cost. Prefer the Chrome plugin/extension route; if it is unavailable or cannot attach, fall back to Computer Use on the real visible Chrome window/profile. This skill must control the real ChatGPT we", "repo": "https://github.com/mcncarl/yichen-skills/tree/HEAD/yichen-chatgpt-web-research", "install": ["npx skills add mcncarl/yichen-skills --skill yichen-chatgpt-web-research"], "stars": 5261, "pushed": "2026-11-01T14:41:34Z", "license": "NOASSERTION", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:mcncarl/yichen-skills/yichen-codex-chatgpt", "name": "yichen-codex-chatgpt", "kind": "skill", "official": false, "desc": "Use the signed-in ChatGPT website in Chat UI Pro mode, never Work mode, to research, architect, and review one local project through a configured Secure Tunnel and read-only MCP, while Codex alone edits and tests locally. Trigger on explicit $yichen-codex-chatgpt, 让 ChatGPT 设计后由 Codex 写代码, ChatGPT 架构并审查 Codex, or requests for a Codex and ChatGPT collaboration loop. Pure research stays on the official ChatGPT website; code and hybrid work follow ChatGPT PLAN to Codex execution to ChatGPT REVIEW. ", "repo": "https://github.com/mcncarl/yichen-skills/tree/HEAD/yichen-codex-chatgpt", "install": ["npx skills add mcncarl/yichen-skills --skill yichen-codex-chatgpt"], "stars": 5261, "pushed": "2026-11-01T14:41:34Z", "license": "NOASSERTION", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:mcncarl/yichen-skills/plugins/yichen-grok-consult/skills/yichen-grok-consult", "name": "yichen-grok-consult", "kind": "skill", "official": false, "desc": "Consult or search with xAI Grok from a GPT-led Codex conversation without switching the main model. Use when the user asks Grok to answer, review, challenge, compare, provide a second opinion, search the web, search X/Twitter, find recent or fixed-date posts, decode timestamps embedded in X status IDs, research current topics, or invoke Grok while staying in the current GPT conversation.", "repo": "https://github.com/mcncarl/yichen-skills/tree/HEAD/plugins/yichen-grok-consult/skills/yichen-grok-consult", "install": ["npx skills add mcncarl/yichen-skills --skill yichen-grok-consult"], "stars": 5261, "pushed": "2026-11-01T14:41:34Z", "license": "NOASSERTION", "plat": "grok", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:google/mcp", "name": "mcp", "kind": "mcp", "official": null, "desc": "Google 💚 MCP", "repo": "https://github.com/google/mcp", "install": [], "stars": 4709, "pushed": null, "license": null, "plat": "google", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:u14app/deep-research", "name": "deep-research", "kind": "mcp", "official": null, "desc": "Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.", "repo": "https://github.com/u14app/deep-research", "install": [], "stars": 4686, "pushed": null, "license": null, "plat": "mcp server", "live": "Not checked (runs locally)"}, {"id": "skill:google-gemini/gemini-skills/skills/gemini-api-cli", "name": "gemini-api-cli", "kind": "skill", "official": false, "desc": "Call the Gemini API from the shell with gemini-api. Use for media generation (images, TTS, music, video), file and YouTube analysis, audio/video transcription, Deep Research, and sandboxed agents.", "repo": "https://github.com/google-gemini/gemini-skills/tree/HEAD/skills/gemini-api-cli", "install": ["npx skills add google-gemini/gemini-skills --skill gemini-api-cli"], "stars": 4482, "pushed": "2026-11-12T18:08:59Z", "license": "Apache-2.0", "plat": "gemini-api-cli", "live": "Not checked (runs locally)"}, {"id": "skill:google-gemini/gemini-skills/skills/gemini-api-dev", "name": "gemini-api-dev", "kind": "skill", "official": false, "desc": "Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice design, voice replication, streaming responses, background research tasks, function calling, structured output, calibrated decisions (yes/no, choice, rubric scoring), or migrating from the old generateContent API. Covers SDK usage and best practices for Gemini models and agents in Python and TypeScript.", "repo": "https://github.com/google-gemini/gemini-skills/tree/HEAD/skills/gemini-api-dev", "install": ["npx skills add google-gemini/gemini-skills --skill gemini-api-dev"], "stars": 4482, "pushed": "2026-11-12T18:08:59Z", "license": "Apache-2.0", "plat": "gemini-api-dev", "live": "Not checked (runs locally)"}, {"id": "skill:google-gemini/gemini-skills/skills/gemini-live-api-dev", "name": "gemini-live-api-dev", "kind": "skill", "official": false, "desc": "Use this skill when building real-time, bidirectional streaming applications with the Gemini Live API, or migrating legacy Live models (2.0/2.5/3.1) to Gemini 3.8 Live. Covers WebSocket-based audio/video/text streaming, voice activity detection (VAD), background reasoning (extended thinking), asynchronous function calling, session management, ephemeral tokens, live transcription, and live translation. SDKs covered - google-genai (Python), @google/genai (JavaScript/TypeScript).", "repo": "https://github.com/google-gemini/gemini-skills/tree/HEAD/skills/gemini-live-api-dev", "install": ["npx skills add google-gemini/gemini-skills --skill gemini-live-api-dev"], "stars": 4482, "pushed": "2026-11-12T18:08:59Z", "license": "Apache-2.0", "plat": "gemini-live-api-dev", "live": "Not checked (runs locally)"}, {"id": "skill:google-gemini/gemini-skills/skills/gemini-omni-flash-api", "name": "gemini-omni-flash-api", "kind": "skill", "official": false, "desc": "Use this skill for generative video editing, text-to-video, image- and video-referenced video generation, first-frame-to-video, first-and-last-frame transitions, and video extensions using Gemini Omni 1.2 Flash (gemini-omni-1.2-flash) via the official google-genai SDK. Includes workflows for pre-processing/optimizing high-resolution or long source videos with ffmpeg, stripping audio for full sound regeneration, and handling turn-by-turn video editing and parallel execution.", "repo": "https://github.com/google-gemini/gemini-skills/tree/HEAD/skills/gemini-omni-flash-api", "install": ["npx skills add google-gemini/gemini-skills --skill gemini-omni-flash-api"], "stars": 4482, "pushed": "2026-11-12T18:08:59Z", "license": "Apache-2.0", "plat": "gemini-omni-flash-api", "live": "Not checked (runs locally)"}, {"id": "skill:davidondrej/skills/skills/codex-subagent", "name": "codex-subagent", "kind": "skill", "official": false, "desc": "Launch OpenAI Codex CLI as a subagent (ChatGPT subscription auth, no API key). Use when delegating a self-contained coding task to Codex from another agent — parallel implementation work, a second opinion, or an independent verification pass.", "repo": "https://github.com/davidondrej/skills/tree/HEAD/skills/codex-subagent", "install": ["npx skills add davidondrej/skills --skill codex-subagent"], "stars": 4309, "pushed": "2026-11-14T02:00:36Z", "license": "MIT", "plat": "openai codex", "live": "Not checked (runs locally)"}, {"id": "skill:davidondrej/skills/skills/fable-safe-prompt", "name": "fable-safe-prompt", "kind": "skill", "official": false, "desc": "Make minimal prompt edits to reduce false-positive refusals from Fable. Use only when the user explicitly invokes /fable-safe-prompt.", "repo": "https://github.com/davidondrej/skills/tree/HEAD/skills/fable-safe-prompt", "install": ["npx skills add davidondrej/skills --skill fable-safe-prompt"], "stars": 4309, "pushed": "2026-11-14T02:00:36Z", "license": "MIT", "plat": "fable", "live": "Not checked (runs locally)"}, {"id": "skill:davidondrej/skills/skills/kimi-review", "name": "kimi-review", "kind": "skill", "official": false, "desc": "Launch an independent Kimi K3.1 Pro Max code review and return it verbatim, falling back across harnesses and routes until one works. Use for /kimi-review, or when the user or another agent asks for a Kimi review.", "repo": "https://github.com/davidondrej/skills/tree/HEAD/skills/kimi-review", "install": ["npx skills add davidondrej/skills --skill kimi-review"], "stars": 4309, "pushed": "2026-11-14T02:00:36Z", "license": "MIT", "plat": "kimi", "live": "Not checked (runs locally)"}, {"id": "skill:davidondrej/skills/skills/muse-review", "name": "muse-review", "kind": "skill", "official": false, "desc": "Launch an independent Meta Muse Wonder Max code review through Pi and OpenRouter, and return it verbatim. Use for /muse-review, or when the user or another agent asks for a Muse review.", "repo": "https://github.com/davidondrej/skills/tree/HEAD/skills/muse-review", "install": ["npx skills add davidondrej/skills --skill muse-review"], "stars": 4309, "pushed": "2026-11-14T02:00:36Z", "license": "MIT", "plat": "meta muse", "live": "Not checked (runs locally)"}, {"id": "skill:davidondrej/skills/skills/openrouter", "name": "openrouter", "kind": "skill", "official": false, "desc": "Design, build, debug, and optimize OpenRouter API integrations. Use for any OpenRouter API work, including models, reasoning, routing, media, tools, structured output, cost, or performance.", "repo": "https://github.com/davidondrej/skills/tree/HEAD/skills/openrouter", "install": ["npx skills add davidondrej/skills --skill openrouter"], "stars": 4309, "pushed": "2026-11-14T02:00:36Z", "license": "MIT", "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:davidondrej/skills/skills/total-review", "name": "total-review", "kind": "skill", "official": false, "desc": "Run five independent max-effort reviews (Fable 5.1, Opus 5.5, GPT-6 Astra, GPT-6.1 Sol, Muse Wonder), merge their findings, and fix only what the user approves. Use for /total-review, or when the user or another agent asks for a total review.", "repo": "https://github.com/davidondrej/skills/tree/HEAD/skills/total-review", "install": ["npx skills add davidondrej/skills --skill total-review"], "stars": 4309, "pushed": "2026-11-14T02:00:36Z", "license": "MIT", "plat": "fable, opus, gpt-6, muse", "live": "Not checked (runs locally)"}, {"id": "skill:michael-denyer/pstack-claude/plugins/pstack/skills/setup-pstack", "name": "setup-pstack", "kind": "skill", "official": false, "desc": "Configure which models pstack uses per role. Detects available models and writes the current runtime's override sheet. Use for /setup-pstack, \"configure pstack models\", changing pstack's model choices, or turning the SessionStart hook on or off.", "repo": "https://github.com/michael-denyer/pstack-claude/tree/HEAD/plugins/pstack/skills/setup-pstack", "install": ["npx skills add michael-denyer/pstack-claude --skill setup-pstack"], "stars": 4298, "pushed": "2026-11-13T15:53:05Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/dual-axis-skill-reviewer", "name": "dual-axis-skill-reviewer", "kind": "skill", "official": false, "desc": "Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/dual-axis-skill-reviewer", "install": ["npx skills add tradermonty/claude-trading-skills --skill dual-axis-skill-reviewer"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/edge-hint-extractor", "name": "edge-hint-extractor", "kind": "skill", "official": false, "desc": "Extract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/edge-hint-extractor", "install": ["npx skills add tradermonty/claude-trading-skills --skill edge-hint-extractor"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/edge-signal-aggregator", "name": "edge-signal-aggregator", "kind": "skill", "official": false, "desc": "Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/edge-signal-aggregator", "install": ["npx skills add tradermonty/claude-trading-skills --skill edge-signal-aggregator"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/edge-strategy-reviewer", "name": "edge-strategy-reviewer", "kind": "skill", "official": false, "desc": "Critically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism. Use when strategy_drafts/*.yaml exists and needs quality gate before pipeline export. Outputs PASS/REVISE/REJECT verdicts with confidence scores.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/edge-strategy-reviewer", "install": ["npx skills add tradermonty/claude-trading-skills --skill edge-strategy-reviewer"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/exposure-coach", "name": "exposure-coach", "kind": "skill", "official": false, "desc": "Generate a one-page Market Posture summary with net exposure ceiling, growth-vs-value bias, participation breadth, and new-entry-allowed vs cash-priority recommendation by integrating signals from breadth, regime, and flow analysis skills.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/exposure-coach", "install": ["npx skills add tradermonty/claude-trading-skills --skill exposure-coach"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/ftd-detector", "name": "ftd-detector", "kind": "skill", "official": false, "desc": "Detects Follow-Through Day (FTD) signals for market bottom confirmation using William O'Neil's methodology. Dual-index tracking (S&P 500 + NASDAQ) with state machine for rally attempt, FTD qualification, and post-FTD health monitoring. Use when user asks about market bottom signals, follow-through days, rally attempts, re-entry timing after corrections, or whether it's safe to increase equity exposure. Complementary to market-top-detector (defensive) - this skill is offensive (bottom confirmatio", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/ftd-detector", "install": ["npx skills add tradermonty/claude-trading-skills --skill ftd-detector"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/futures-position-sizer", "name": "futures-position-sizer", "kind": "skill", "official": false, "desc": "Calculate contract-based futures position sizes from a direction, entry, and stop-loss, using verified per-symbol contract specs (multiplier, tick size, tick value). Use when the user asks how many futures contracts to trade, wants to size a futures position (ES, NQ, ZB, GC, CL, 6E/E6, VX, BT, ...), or is handing off a contrarian-setup-gate READY_FOR_PLAN direction/invalidation_level for sizing. Pure, offline calculation -- no API keys, no network.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/futures-position-sizer", "install": ["npx skills add tradermonty/claude-trading-skills --skill futures-position-sizer"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/ibd-distribution-day-monitor", "name": "ibd-distribution-day-monitor", "kind": "skill", "official": false, "desc": "Detect IBD-style Distribution Days for QQQ/SPY (close down at least 0.2% on higher volume), track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk (NORMAL/CAUTION/HIGH/SEVERE), and emit TQQQ/QQQ exposure recommendations. Use after market close, before TQQQ exposure changes, or as input to FTD/market-state frameworks. Does not execute trades.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/ibd-distribution-day-monitor", "install": ["npx skills add tradermonty/claude-trading-skills --skill ibd-distribution-day-monitor"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/institutional-flow-tracker", "name": "institutional-flow-tracker", "kind": "skill", "official": false, "desc": "Use this skill to track institutional investor ownership changes and portfolio flows using 13F filings data. Analyzes hedge funds, mutual funds, and other institutional holders to identify stocks with significant smart money accumulation or distribution. Helps discover stocks before major moves by following where sophisticated investors are deploying capital.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/institutional-flow-tracker", "install": ["npx skills add tradermonty/claude-trading-skills --skill institutional-flow-tracker"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/kanchi-dividend-review-monitor", "name": "kanchi-dividend-review-monitor", "kind": "skill", "official": false, "desc": "Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when users ask for 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/kanchi-dividend-review-monitor", "install": ["npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-review-monitor"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/kanchi-dividend-sop", "name": "kanchi-dividend-sop", "kind": "skill", "official": false, "desc": "Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Use when users ask for かんち式配当投資, dividend screening, dividend growth quality checks, PERxPBR adaptation for US sectors, pullback limit-order planning, or one-page stock memo creation. Covers screening, deep dive, entry planning, and post-purchase monitoring cadence.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/kanchi-dividend-sop", "install": ["npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/kanchi-dividend-us-tax-accounting", "name": "kanchi-dividend-us-tax-accounting", "kind": "skill", "official": false, "desc": "Provide US dividend tax and account-location workflow for Kanchi-style income portfolios. Use when users ask about qualified vs ordinary dividends, 1099-DIV interpretation, REIT/BDC distribution treatment, holding-period checks, or taxable-vs-IRA account placement decisions for dividend assets.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/kanchi-dividend-us-tax-accounting", "install": ["npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-us-tax-accounting"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/macro-regime-detector", "name": "macro-regime-detector", "kind": "skill", "official": false, "desc": "Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states. Run when user asks about macro regime, market regime change, structural rotation, or long-term market positioning.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/macro-regime-detector", "install": ["npx skills add tradermonty/claude-trading-skills --skill macro-regime-detector"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/manifoldbt-backtester", "name": "manifoldbt-backtester", "kind": "skill", "official": false, "desc": "Runs a declarative strategy spec over OHLCV bars with the manifoldbt Rust engine, pairs the fill log into round trips, and emits the eight inputs the backtest-expert skill scores. Use when the user wants to execute a backtest, measure a rule they have described, obtain win rate / average win / average loss / max drawdown from real bars, or feed backtest-expert with measured numbers instead of estimates.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/manifoldbt-backtester", "install": ["npx skills add tradermonty/claude-trading-skills --skill manifoldbt-backtester"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/market-breadth-analyzer", "name": "market-breadth-analyzer", "kind": "skill", "official": false, "desc": "Quantifies market breadth health using TraderMonty's public CSV data. Generates a 0-100 composite score across 6 components (100 = healthy). No API key required. Use when user asks about market breadth, participation rate, advance-decline health, whether the rally is broad-based, or general market health assessment.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/market-breadth-analyzer", "install": ["npx skills add tradermonty/claude-trading-skills --skill market-breadth-analyzer"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:tradermonty/claude-trading-skills/skills/market-top-detector", "name": "market-top-detector", "kind": "skill", "official": false, "desc": "Assesses heuristic market top risk using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100 composite score with risk zone classification. Use when user asks about market top risk, distribution days, defensive rotation, leadership breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections.", "repo": "https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/market-top-detector", "install": ["npx skills add tradermonty/claude-trading-skills --skill market-top-detector"], "stars": 4283, "pushed": "2026-11-14T05:32:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:raullenchai/Rapid-MLX", "name": "Rapid-MLX", "kind": "mcp", "official": null, "desc": "Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents. Up to 4× faster than Apple's MLX (mlx-lm) on the same weights.", "repo": "https://github.com/raullenchai/Rapid-MLX", "install": [], "stars": 4145, "pushed": null, "license": null, "plat": "apple silicon", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/accelerated-computing-cudf", "name": "accelerated-computing-cudf", "kind": "skill", "official": false, "desc": "Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/accelerated-computing-cudf", "install": ["npx skills add NVIDIA/skills --skill accelerated-computing-cudf"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-boltz2-nim", "name": "boltz2-nim", "kind": "skill", "official": false, "desc": "Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-boltz2-nim", "install": ["npx skills add NVIDIA/skills --skill boltz2-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "boltz2", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-codonfm-embed", "name": "codonfm-embed", "kind": "skill", "official": false, "desc": "Validate coding-sequence CSVs, extract public CodonFM/Encodon embeddings, and choose checkpoints for downstream property modeling.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-codonfm-embed", "install": ["npx skills add NVIDIA/skills --skill codonfm-embed"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "codonfm", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-codonfm-finetune", "name": "codonfm-finetune", "kind": "skill", "official": false, "desc": "Fine-tune public CodonFM Encodon checkpoints on labeled coding-sequence or coding-variant data using LoRA, head-only, or full fine-tuning. Use when a user explicitly asks to fine-tune CodonFM or Encodon for regression or classification. Support generic public-v1 Encodon workflows only; reject Decodon, MissenseDataset, missense_synom_agg, and generation workflows.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-codonfm-finetune", "install": ["npx skills add NVIDIA/skills --skill codonfm-finetune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "codonfm", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-codonfm-score", "name": "codonfm-score", "kind": "skill", "official": false, "desc": "Validate, prepare, or run public CodonFM Encodon masked-codon variant scoring and review compatibility of its scoring workflows. Use only when the user explicitly requests CodonFM or Encodon, or that context is already established in the conversation. Do not select this skill for a generic variant-scoring request without that context; ask for the variant and intended analysis first.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-codonfm-score", "install": ["npx skills add NVIDIA/skills --skill codonfm-score"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "codonfm", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-codonfm-setup", "name": "codonfm-setup", "kind": "skill", "official": false, "desc": "Set up the public CodonFM v1 repository and download public Encodon checkpoints. Use for requests to build or launch the CodonFM development container, configure local data/checkpoint mounts, verify GPU access, or download public Encodon 80M, 600M, 1B, or Cdwt-1B weights. Do not use for Decodon, Encodon 5B/10B, missense-aggregation, or codon-optimization setup because those implementations are not in the public repository.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-codonfm-setup", "install": ["npx skills add NVIDIA/skills --skill codonfm-setup"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "codonfm", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-complexa-evaluate-pdbs", "name": "complexa-evaluate-pdbs", "kind": "skill", "official": false, "desc": "Agent runbook for evaluating an existing PDB directory through the separately installed first-party Proteina-Complexa CLI. Use to refold or score designs, compute interface pAE, i_pLDDT, scRMSD, motif RMSD, or designability, run complexa analysis, or assess third-party BindCraft, AlphaProteo, or RFdiffusion outputs. SKILL.md selects and invokes the evaluation configuration; bundled executables only inspect local readiness and write a run manifest.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-complexa-evaluate-pdbs", "install": ["npx skills add NVIDIA/skills --skill complexa-evaluate-pdbs"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "proteina-complexa", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-complexa-sweep", "name": "complexa-sweep", "kind": "skill", "official": false, "desc": "Agent runbook for Proteina-Complexa parameter sweeps through the separately installed first-party CLI and config generator. Use for cartesian-product hyperparameter scans, sweeper YAML authoring, configuration comparisons, ablations, tuning, success-rate ranking, or Pareto searches over quality and wall-clock cost. Do not use for a single existing-job launch or SLURM submission template. SKILL.md performs the orchestration; bundled executables only inspect local readiness and write a run manifes", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-complexa-sweep", "install": ["npx skills add NVIDIA/skills --skill complexa-sweep"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "proteina-complexa", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-complexa-target", "name": "complexa-target", "kind": "skill", "official": false, "desc": "Add, edit, inspect, or validate Proteina-Complexa target definitions for protein binders, ligand binders, and AME motif scaffolding. Use for target registries, chain/residue selectors, hotspots, ligand fields, and incomplete motif inputs. Covers direct YAML edits and complexa target/validate target commands; running a campaign with an already registered target belongs to complexa-design.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-complexa-target", "install": ["npx skills add NVIDIA/skills --skill complexa-target"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "proteina-complexa", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-cuequivariance", "name": "cuequivariance", "kind": "skill", "official": false, "desc": "Use when defining cuEquivariance groups and irreps, building equivariant tensor products, or validating segmented polynomials with NumPy.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-cuequivariance", "install": ["npx skills add NVIDIA/skills --skill cuequivariance"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "cuequivariance", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-cuequivariance-jax", "name": "cuequivariance-jax", "kind": "skill", "official": false, "desc": "Use when writing or debugging cuEquivariance JAX polynomials, irrep dictionaries, Flax NNX layers, or CPU/CUDA execution and layouts.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-cuequivariance-jax", "install": ["npx skills add NVIDIA/skills --skill cuequivariance-jax"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "cuequivariance", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-cuequivariance-torch", "name": "cuequivariance-torch", "kind": "skill", "official": false, "desc": "Use when building or debugging cuEquivariance PyTorch tensor products or equivariant GNNs, especially CPU references, layouts, indexing, and gradients.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-cuequivariance-torch", "install": ["npx skills add NVIDIA/skills --skill cuequivariance-torch"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "cuequivariance", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-diffdock-nim", "name": "diffdock-nim", "kind": "skill", "official": false, "desc": "Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets. Use for DiffDock, molecular docking, ligand docking, blind docking, SMILES or SDF ligands, ranked poses, confidence scores, hosted NVIDIA API, or local Docker deployment.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-diffdock-nim", "install": ["npx skills add NVIDIA/skills --skill diffdock-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "diffdock", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/digital-health-clinical-asr-build", "name": "digital-health-clinical-asr-build", "kind": "skill", "official": false, "desc": "Stage 2 of the Clinical ASR Flywheel. Use when curating clinical terms, tagging IPA, and synthesizing a NeMo manifest. NOT for scoring (use /digital-health-clinical-asr-eval).", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/digital-health-clinical-asr-build", "install": ["npx skills add NVIDIA/skills --skill digital-health-clinical-asr-build"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemo", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/digital-health-clinical-asr-eval", "name": "digital-health-clinical-asr-eval", "kind": "skill", "official": false, "desc": "Stage 3 of Clinical ASR Flywheel. Score a NeMo manifest, produce the five-section KER leaderboard (by-ipa_source diagnostic). Not for ASR auth (/riva-asr).", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/digital-health-clinical-asr-eval", "install": ["npx skills add NVIDIA/skills --skill digital-health-clinical-asr-eval"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemo", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/digital-health-clinical-asr-finetune", "name": "digital-health-clinical-asr-finetune", "kind": "skill", "official": false, "desc": "Stage 4 of the Clinical ASR Flywheel. Use when priority KER is above 0.3 to run stock NeMo SFT on Parakeet TDT v2 and offline cycle N+1 re-eval. NOT for generic word boosting (use /finetune-asr).", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/digital-health-clinical-asr-finetune", "install": ["npx skills add NVIDIA/skills --skill digital-health-clinical-asr-finetune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemo", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/digital-health-clinical-asr-setup", "name": "digital-health-clinical-asr-setup", "kind": "skill", "official": false, "desc": "Stage 1 of Clinical ASR Flywheel. Use when bootstrapping a cycle: NVCF+MW disclosure, NVIDIA_API_KEY check, deps install, TTS+ASR smoke test.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/digital-health-clinical-asr-setup", "install": ["npx skills add NVIDIA/skills --skill digital-health-clinical-asr-setup"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemo", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-drug-discovery-pipeline", "name": "drug-discovery-pipeline", "kind": "skill", "official": false, "desc": "NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform ", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-drug-discovery-pipeline", "install": ["npx skills add NVIDIA/skills --skill drug-discovery-pipeline"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-evo2-nim", "name": "evo2-nim", "kind": "skill", "official": false, "desc": "Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-evo2-nim", "install": ["npx skills add NVIDIA/skills --skill evo2-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "evo 2", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-genmol-nim", "name": "genmol-nim", "kind": "skill", "official": false, "desc": "Generate novel drug-like molecules using the GenMol NIM microservice. Use for de novo generation, scaffold decoration, motif extension, lead optimization, SAFE notation, QED or LogP ranking, hosted NVIDIA API calls, or local Docker deployment. GenMol takes SAFE notation in the smiles field, not ordinary SMILES.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-genmol-nim", "install": ["npx skills add NVIDIA/skills --skill genmol-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "genmol", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/isaac-cable-insertion-policy-training", "name": "isaac-cable-insertion-policy-training", "kind": "skill", "official": false, "desc": "Set up the standalone IsaacLabTraining package from NVIDIA-ISAAC-ROS/isaac_ros_manipulation, then launch, monitor, resume, evaluate, and tune DisplayPort insertion policies without a separate Isaac Lab source installation. Use for setup and campaign planning, dependency repair, curriculum or PhysX troubleshooting, video evidence, and long GPU runs.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/isaac-cable-insertion-policy-training", "install": ["npx skills add NVIDIA/skills --skill isaac-cable-insertion-policy-training"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "isaac sim", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/jetson-inference-mem-tune", "name": "jetson-inference-mem-tune", "kind": "skill", "official": false, "desc": "Pick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-inference-mem-tune", "install": ["npx skills add NVIDIA/skills --skill jetson-inference-mem-tune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia jetson", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/jetson-llm-benchmark", "name": "jetson-llm-benchmark", "kind": "skill", "official": false, "desc": "Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-llm-benchmark", "install": ["npx skills add NVIDIA/skills --skill jetson-llm-benchmark"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia jetson", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/jetson-llm-serve", "name": "jetson-llm-serve", "kind": "skill", "official": false, "desc": "Stand up vLLM or SGLang serving on Jetson, using upstream vLLM on Thor and Orin JetPack 7.2+, and NVIDIA-AI-IOT vLLM on older Orin.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-llm-serve", "install": ["npx skills add NVIDIA/skills --skill jetson-llm-serve"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia jetson", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/medtech-model-evidence-export", "name": "medtech-model-evidence-export", "kind": "skill", "official": false, "desc": "Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/medtech-model-evidence-export", "install": ["npx skills add NVIDIA/skills --skill medtech-model-evidence-export"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "mlflow", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-activation-recompute", "name": "nemo-mbridge-perf-activation-recompute", "kind": "skill", "official": false, "desc": "Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute. Use for activation memory OOMs or regressions involving recompute_granularity, recompute_num_layers, recompute_modules, recompute_method, selective recompute, full recompute, or activation checkpointing.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-activation-recompute", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-activation-recompute"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-cpu-offloading", "name": "nemo-mbridge-perf-cpu-offloading", "kind": "skill", "official": false, "desc": "Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-cpu-offloading", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cpu-offloading"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-cuda-graphs", "name": "nemo-mbridge-perf-cuda-graphs", "kind": "skill", "official": false, "desc": "Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-cuda-graphs", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-cuda-graphs"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-expert-parallel-overlap", "name": "nemo-mbridge-perf-expert-parallel-overlap", "kind": "skill", "official": false, "desc": "Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-expert-parallel-overlap", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-expert-parallel-overlap"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-hierarchical-context-parallel", "name": "nemo-mbridge-perf-hierarchical-context-parallel", "kind": "skill", "official": false, "desc": "Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-hierarchical-context-parallel", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-hierarchical-context-parallel"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-megatron-fsdp", "name": "nemo-mbridge-perf-megatron-fsdp", "kind": "skill", "official": false, "desc": "Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-megatron-fsdp", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-memory-tuning", "name": "nemo-mbridge-perf-memory-tuning", "kind": "skill", "official": false, "desc": "Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-memory-tuning", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-memory-tuning"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-moe-comm-overlap", "name": "nemo-mbridge-perf-moe-comm-overlap", "kind": "skill", "official": false, "desc": "MoE expert-parallel communication overlap in Megatron Bridge. Covers dispatch/combine overlap, flex dispatcher backends, and expert wgrad scheduling.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-comm-overlap", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-comm-overlap"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-moe-dispatcher-selection", "name": "nemo-mbridge-perf-moe-dispatcher-selection", "kind": "skill", "official": false, "desc": "Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage. Summarizes patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up work.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-dispatcher-selection", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-dispatcher-selection"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-moe-hardware-configs", "name": "nemo-mbridge-perf-moe-hardware-configs", "kind": "skill", "official": false, "desc": "Representative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-hardware-configs", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-hardware-configs"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-moe-long-context", "name": "nemo-mbridge-perf-moe-long-context", "kind": "skill", "official": false, "desc": "Long-context MoE training guidance for Megatron Bridge. Covers CP sizing, selective recompute, dispatcher choices, and practical patterns from DSV3, Qwen3, and Qwen3-Next long-context experiments.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-long-context", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-long-context"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-moe-optimization-workflow", "name": "nemo-mbridge-perf-moe-optimization-workflow", "kind": "skill", "official": false, "desc": "Evidence-gated workflow for MoE performance optimization in Megatron Bridge. Covers measurement contracts, the Three Walls framework, parallel folding, profiling, matched A/B tuning, and final validation.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-optimization-workflow", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-optimization-workflow"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-moe-vlm-training", "name": "nemo-mbridge-perf-moe-vlm-training", "kind": "skill", "official": false, "desc": "Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-moe-vlm-training", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-moe-vlm-training"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-parallelism-strategies", "name": "nemo-mbridge-perf-parallelism-strategies", "kind": "skill", "official": false, "desc": "Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-parallelism-strategies", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-parallelism-strategies"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-sequence-packing", "name": "nemo-mbridge-perf-sequence-packing", "kind": "skill", "official": false, "desc": "Validate and use packed sequences and long-context training in Megatron-Bridge, including offline LLM packing, collate-time VLM packing, Energon online packing, and CP constraints.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-sequence-packing", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-sequence-packing"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-perf-tp-dp-comm-overlap", "name": "nemo-mbridge-perf-tp-dp-comm-overlap", "kind": "skill", "official": false, "desc": "Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-perf-tp-dp-comm-overlap", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-perf-tp-dp-comm-overlap"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-recipe-recommender", "name": "nemo-mbridge-recipe-recommender", "kind": "skill", "official": false, "desc": "Recommend and customize Megatron Bridge library and benchmark recipes for a user's model, GPU count, hardware, sequence length, and pretrain/SFT/PEFT goal. Use when selecting a starting recipe, comparing library and benchmark configs, resizing parallelism for a GPU allocation, or distinguishing convergence changes, semantics-preserving execution tuning, and benchmark-only shortcuts.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-recipe-recommender", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-recipe-recommender"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemo-mbridge-resiliency", "name": "nemo-mbridge-resiliency", "kind": "skill", "official": false, "desc": "Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemo-mbridge-resiliency", "install": ["npx skills add NVIDIA/skills --skill nemo-mbridge-resiliency"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "megatron-bridge", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemotron-asr-finetune", "name": "nemotron-asr-finetune", "kind": "skill", "official": false, "desc": "Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation. Given a goal like \"improve/fine-tune ASR for my domain or language\", it scopes the task, picks the cheapest sufficient path (word boosting → n-gram LM → fine-tuning), delegates each stage to the right sub-skill (data generation, training, evaluation, deployment), and answers cost/time/data questions along the way.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemotron-asr-finetune", "install": ["npx skills add NVIDIA/skills --skill nemotron-asr-finetune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemotron", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemotron-customize", "name": "nemotron-customize", "kind": "skill", "official": false, "desc": "Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemotron-customize", "install": ["npx skills add NVIDIA/skills --skill nemotron-customize"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemotron", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemotron-policy-generator", "name": "nemotron-policy-generator", "kind": "skill", "official": false, "desc": "Generates BYO custom safety policies for NVIDIA Nemotron content-safety guardrails — Nemotron-Content-Safety-Reasoning-4B (text) and multimodal Nemotron-3-Content-Safety. Produces a Markdown policy, JSON taxonomy, and drop-in inference prompts. Maps rough words or an existing policy to V2 categories, adding custom categories or topic-following rules.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemotron-policy-generator", "install": ["npx skills add NVIDIA/skills --skill nemotron-policy-generator"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemotron", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemotron-retrieval-recipes", "name": "nemotron-retrieval-recipes", "kind": "skill", "official": false, "desc": "Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemotron-retrieval-recipes", "install": ["npx skills add NVIDIA/skills --skill nemotron-retrieval-recipes"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemotron", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemotron-speech", "name": "nemotron-speech", "kind": "skill", "official": false, "desc": "Routes NVIDIA Nemotron Speech (Formerly Riva) NIM tasks — deploys, runs, and tests ASR, TTS, and NMT NIMs on build.nvidia.com or self-hosted.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemotron-speech", "install": ["npx skills add NVIDIA/skills --skill nemotron-speech"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemotron", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nemotron-voice-agent-builder", "name": "nemotron-voice-agent-builder", "kind": "skill", "official": false, "desc": "Create, refine, or fix NVIDIA voice agents (Cascaded or Omni) with Pipecat or LiveKit. Use when building, scaffolding, or iterating on a real-time voice-agent pipeline, including speech (ASR/TTS) customization and cloud or local deployment. Not for offline/batch speech-to-text, text-only chat or RAG, or generic Docker or CUDA work unrelated to a voice agent.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nemotron-voice-agent-builder", "install": ["npx skills add NVIDIA/skills --skill nemotron-voice-agent-builder"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nemotron", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nv-generate-mr-brain-finetune", "name": "nv-generate-mr-brain-finetune", "kind": "skill", "official": false, "desc": "Used for finetuning NV-Generate-CTMR MR-Brain v1 for T1, T2, FLAIR, SWI, or MRA data from a NIfTI datalist. Not for clinical or production data approval.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-generate-mr-brain-finetune", "install": ["npx skills add NVIDIA/skills --skill nv-generate-mr-brain-finetune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nv-generate-ctmr", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nv-generate-vae-finetune", "name": "nv-generate-vae-finetune", "kind": "skill", "official": false, "desc": "Used for finetuning the NV-Generate-CTMR MAISI VAE from CT/MRI NIfTI datalists. Not for clinical or production data approval.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-generate-vae-finetune", "install": ["npx skills add NVIDIA/skills --skill nv-generate-vae-finetune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nv-generate-ctmr", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nv-reason-ct", "name": "nv-reason-ct", "kind": "skill", "official": false, "desc": "Run NV-Reason-CT inference on user-provided 3D NIfTI chest or abdominal CT volumes for engineering and research workflows. Not for diagnosis, treatment, or clinical reporting.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-reason-ct", "install": ["npx skills add NVIDIA/skills --skill nv-reason-ct"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nv-reason-ct", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nv-reason-cxr", "name": "nv-reason-cxr", "kind": "skill", "official": false, "desc": "Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-reason-cxr", "install": ["npx skills add NVIDIA/skills --skill nv-reason-cxr"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nv-reason-cxr", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/nv-segment-ct-finetune", "name": "nv-segment-ct-finetune", "kind": "skill", "official": false, "desc": "Runs standard or fixed-channel softmax finetuning of NV-Segment-CT VISTA3D on CT NIfTI image/label datasets, with optional MONAI-native MLflow tracking and checkpoint evidence. Uses softmax for predefined, mutually exclusive classes; keeps the standard workflow when point prompts or runtime-variable classes are needed. Not for clinical validation.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/nv-segment-ct-finetune", "install": ["npx skills add NVIDIA/skills --skill nv-segment-ct-finetune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nv-segment-ct", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/plugins/nvidia-skills/skills/nvidia-skill-finder", "name": "nvidia-skill-finder", "kind": "skill", "official": false, "desc": "Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs, GPUs, Jetson/JetPack/L4T/BSP/SDK Manager/driver/flashing/setup, CUDA, NIM, NeMo, Omniverse/OpenUSD/SimReady, Isaac, RAPIDS/cuDF, cuPyNumeric, cuOpt, Dynamo, Holoscan, TensorRT, DeepStream, VSS, TAO, NGC/NVCF. Do not use for generic non-NVIDIA route, optimize, deploy, AI, video, data, or infrastructure tasks.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/plugins/nvidia-skills/skills/nvidia-skill-finder", "install": ["npx skills add NVIDIA/skills --skill nvidia-skill-finder"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:nvidia-skills", "name": "nvidia-skills", "kind": "claude-plugin", "official": null, "desc": "Find the right NVIDIA skill for GPU acceleration, CUDA, AI agents, data loading, training, inference, robotics, Physical AI, Omniverse, simulation, and other NVIDIA-powered workflows.", "repo": "https://github.com/NVIDIA/skills.git", "install": ["claude plugin install nvidia-skills@claude-plugins-official"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:parcadei/Continuous-Claude-v3/.claude/skills/agentica-claude-proxy", "name": "agentica-claude-proxy", "kind": "skill", "official": false, "desc": "Guide for integrating Agentica SDK with Claude Code CLI proxy", "repo": "https://github.com/parcadei/Continuous-Claude-v3/tree/HEAD/.claude/skills/agentica-claude-proxy", "install": ["npx skills add parcadei/Continuous-Claude-v3 --skill agentica-claude-proxy"], "stars": 3949, "pushed": "2026-01-26T15:27:49Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:parcadei/Continuous-Claude-v3/.claude/skills/agentica-server", "name": "agentica-server", "kind": "skill", "official": false, "desc": "Agentica server + Claude proxy setup - architecture, startup sequence, debugging", "repo": "https://github.com/parcadei/Continuous-Claude-v3/tree/HEAD/.claude/skills/agentica-server", "install": ["npx skills add parcadei/Continuous-Claude-v3 --skill agentica-server"], "stars": 3949, "pushed": "2026-01-26T15:27:49Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:parcadei/Continuous-Claude-v3/.claude/skills/braintrust-analyze", "name": "braintrust-analyze", "kind": "skill", "official": false, "desc": "Analyze Claude Code sessions via Braintrust", "repo": "https://github.com/parcadei/Continuous-Claude-v3/tree/HEAD/.claude/skills/braintrust-analyze", "install": ["npx skills add parcadei/Continuous-Claude-v3 --skill braintrust-analyze"], "stars": 3949, "pushed": "2026-01-26T15:27:49Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:parcadei/Continuous-Claude-v3/.claude/skills/braintrust-tracing", "name": "braintrust-tracing", "kind": "skill", "official": false, "desc": "Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging", "repo": "https://github.com/parcadei/Continuous-Claude-v3/tree/HEAD/.claude/skills/braintrust-tracing", "install": ["npx skills add parcadei/Continuous-Claude-v3 --skill braintrust-tracing"], "stars": 3949, "pushed": "2026-01-26T15:27:49Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:parcadei/Continuous-Claude-v3/.claude/skills/cli-reference", "name": "cli-reference", "kind": "skill", "official": false, "desc": "Claude Code CLI commands, flags, headless mode, and automation patterns", "repo": "https://github.com/parcadei/Continuous-Claude-v3/tree/HEAD/.claude/skills/cli-reference", "install": ["npx skills add parcadei/Continuous-Claude-v3 --skill cli-reference"], "stars": 3949, "pushed": "2026-01-26T15:27:49Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:parcadei/Continuous-Claude-v3/.claude/skills/math-unified", "name": "math", "kind": "skill", "official": false, "desc": "Unified math capabilities - computation, solving, and explanation. I route to the right tool.", "repo": "https://github.com/parcadei/Continuous-Claude-v3/tree/HEAD/.claude/skills/math-unified", "install": ["npx skills add parcadei/Continuous-Claude-v3 --skill math"], "stars": 3949, "pushed": "2026-01-26T15:27:49Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:parcadei/Continuous-Claude-v3/.claude/plugins/braintrust-tracing/skills/trace-claude-code", "name": "trace-claude-code", "kind": "skill", "official": false, "desc": "Automatically trace Claude Code conversations to Braintrust for observability. Captures sessions, conversation turns, and tool calls as hierarchical traces.", "repo": "https://github.com/parcadei/Continuous-Claude-v3/tree/HEAD/.claude/plugins/braintrust-tracing/skills/trace-claude-code", "install": ["npx skills add parcadei/Continuous-Claude-v3 --skill trace-claude-code"], "stars": 3949, "pushed": "2026-01-26T15:27:49Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/cs-foundations", "name": "cs-foundations", "kind": "skill", "official": false, "desc": "Master discrete mathematics, logic, formal proofs, and computational thinking. Build the mathematical foundation for all computer science.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/cs-foundations", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill cs-foundations"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/dialectic", "name": "dialectic", "kind": "skill", "official": false, "desc": "Minimal compatibility wrapper for vibe Dialectic Mode (multi-perspective design analysis).", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/dialectic", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill dialectic"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/LQF_Machine_Learning_Expert_Guide", "name": "LQF_Machine_Learning_Expert_Guide", "kind": "skill", "official": false, "desc": "LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, di", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/LQF_Machine_Learning_Expert_Guide", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill LQF_Machine_Learning_Expert_Guide"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "lqf", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/mcp-integration", "name": "MCP Integration", "kind": "skill", "official": false, "desc": "This skill should be used when the user asks to \"add MCP server\", \"integrate MCP\", \"configure MCP in plugin\", \"use .mcp.json\", \"set up Model Context Protocol\", \"connect external service\", mentions \"${CLAUDE_PLUGIN_ROOT} with MCP\", or discusses MCP server types (SSE, stdio, HTTP, WebSocket). Provides comprehensive guidance for integrating Model Context Protocol servers into Claude Code plugins for external tool and service integration.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/mcp-integration", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill MCP Integration"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/prompt-lookup", "name": "prompt-lookup", "kind": "skill", "official": false, "desc": "Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/prompt-lookup", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill prompt-lookup"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "prompt lookup", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/pymc", "name": "pymc-bayesian-modeling", "kind": "skill", "official": false, "desc": "Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/pymc", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill pymc-bayesian-modeling"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "pymc", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/pytorch-lightning", "name": "pytorch-lightning", "kind": "skill", "official": false, "desc": "Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/pytorch-lightning", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill pytorch-lightning"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "pytorch", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/senior-computer-vision", "name": "senior-computer-vision", "kind": "skill", "official": false, "desc": "World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/senior-computer-vision", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill senior-computer-vision"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/senior-data-scientist", "name": "senior-data-scientist", "kind": "skill", "official": false, "desc": "World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/senior-data-scientist", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill senior-data-scientist"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/senior-ml-engineer", "name": "senior-ml-engineer", "kind": "skill", "official": false, "desc": "World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/senior-ml-engineer", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill senior-ml-engineer"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/shap", "name": "shap", "kind": "skill", "official": false, "desc": "Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/shap", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill shap"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/stable-baselines3", "name": "stable-baselines3", "kind": "skill", "official": false, "desc": "Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/stable-baselines3", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill stable-baselines3"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/timesfm-forecasting", "name": "timesfm-forecasting", "kind": "skill", "official": false, "desc": "Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run ", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/timesfm-forecasting", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill timesfm-forecasting"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "google timesfm", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/torch-geometric", "name": "torch-geometric", "kind": "skill", "official": false, "desc": "Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/torch-geometric", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill torch-geometric"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "pytorch geometric", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/transformers", "name": "transformers", "kind": "skill", "official": false, "desc": "This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/transformers", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill transformers"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/umap-learn", "name": "umap-learn", "kind": "skill", "official": false, "desc": "UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/umap-learn", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill umap-learn"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelcontextprotocol/java-sdk", "name": "java-sdk", "kind": "mcp", "official": null, "desc": "The official Java SDK for Model Context Protocol servers and clients. Maintained in collaboration with Spring AI", "repo": "https://github.com/modelcontextprotocol/java-sdk", "install": [], "stars": 3768, "pushed": null, "license": null, "plat": "model context protocol", "live": "Not checked (runs locally)"}, {"id": "skill:scenario-labs/skills/skills/scenario", "name": "scenario", "kind": "skill", "official": false, "desc": "Use when connecting an AI agent to Scenario (scenario.com) through MCP, or when a task involves generating images, video, 3D, audio, sprites, textures, or game assets. Also when picking a Scenario model, running a LoRA, refining a generation prompt, uploading reference images, waiting on generation jobs, checking credits or quota, hitting Scenario auth, scope, or Forbidden errors, or setting up mcp.scenario.com in Claude Code, Cursor, VSCode, or another agent.", "repo": "https://github.com/scenario-labs/skills/tree/HEAD/skills/scenario", "install": ["npx skills add scenario-labs/skills --skill scenario"], "stars": 3764, "pushed": "2026-11-13T14:09:04Z", "license": "MIT", "plat": "scenario", "live": "Not checked (runs locally)"}, {"id": "skill:scenario-labs/skills/skills/scenario-model-comparison", "name": "scenario-model-comparison", "kind": "skill", "official": false, "desc": "Use when comparing Scenario models on the same brief through MCP: a bake-off with shared prompts and inputs, cost, generation time and quality measured side by side, choosing a default model, testing a new release against the incumbent, or finding which model handles a style, edit, or reference image best. Keywords: model comparison, benchmark, bake-off, A/B test, contact sheet, cost per asset, latency.", "repo": "https://github.com/scenario-labs/skills/tree/HEAD/skills/scenario-model-comparison", "install": ["npx skills add scenario-labs/skills --skill scenario-model-comparison"], "stars": 3764, "pushed": "2026-11-13T14:09:04Z", "license": "MIT", "plat": "scenario", "live": "Not checked (runs locally)"}, {"id": "skill:scenario-labs/skills/skills/scenario-model-training", "name": "scenario-model-training", "kind": "skill", "official": false, "desc": "Use when generated assets must keep a consistent style, character, or product look and references stop scaling, or when a user asks to train a custom model through the Scenario MCP, fine-tune an image or video LoRA (MiniMax H3), clone a voice, curate a training dataset, choose a base model, set epochs and sample prompts, estimate training cost, diagnose a trained model that lost its identity or has one epoch, or generate with a trained model. Keywords: custom model, video clips, epoch previews.", "repo": "https://github.com/scenario-labs/skills/tree/HEAD/skills/scenario-model-training", "install": ["npx skills add scenario-labs/skills --skill scenario-model-training"], "stars": 3764, "pushed": "2026-11-13T14:09:04Z", "license": "MIT", "plat": "scenario", "live": "Not checked (runs locally)"}, {"id": "skill:scenario-labs/skills/skills/scenario-moderation", "name": "scenario-moderation", "kind": "skill", "official": false, "desc": "Use when a Scenario generation is blocked, refused, or returns a moderation or sensitive-content error, when a video is rejected after rendering or its audio track is flagged, when an output is flagged for likeness to a real person or for IP and copyright detection, when the same prompt passes on one model and fails on another, or when a team's own characters, weapons, or props get flagged. Keywords: blocked prompt, content moderation, refused generation, provider filter, false positive.", "repo": "https://github.com/scenario-labs/skills/tree/HEAD/skills/scenario-moderation", "install": ["npx skills add scenario-labs/skills --skill scenario-moderation"], "stars": 3764, "pushed": "2026-11-13T14:09:04Z", "license": "MIT", "plat": "scenario", "live": "Not checked (runs locally)"}, {"id": "skill:scenario-labs/skills/skills/scenario-quality-gate", "name": "scenario-quality-gate", "kind": "skill", "official": false, "desc": "Use when a generated Scenario image needs a quality check or a brand-brief compliance verdict before it ships: pass/warn/fail scoring, on-brand review against a configured brief, QA gating a batch, pricing or refreshing a stored verdict, or iterating a generation until it clears the gate. Keywords: quality gate, quality check, QA, verdict, brief compliance, on-brand, brand brief, pass fail, score, review, asset_quality_gate_run.", "repo": "https://github.com/scenario-labs/skills/tree/HEAD/skills/scenario-quality-gate", "install": ["npx skills add scenario-labs/skills --skill scenario-quality-gate"], "stars": 3764, "pushed": "2026-11-13T14:09:04Z", "license": "MIT", "plat": "scenario", "live": "Not checked (runs locally)"}, {"id": "skill:scenario-labs/skills/skills/scenario-refine-loop", "name": "scenario-refine-loop", "kind": "skill", "official": false, "desc": "Use when a Scenario output must be checked and improved rather than accepted first roll: iterating a generation until it matches the brief, fixing a batch that came back off-brief, retrying failed shots methodically, wiring an automated generate, review, revise loop, or deciding whether to re-prompt, swap references, inpaint, post-process, or change model. Keywords: refine, iterate, critique, review loop, QA, self-correction, verify, acceptance criteria, retry, drift.", "repo": "https://github.com/scenario-labs/skills/tree/HEAD/skills/scenario-refine-loop", "install": ["npx skills add scenario-labs/skills --skill scenario-refine-loop"], "stars": 3764, "pushed": "2026-11-13T14:09:04Z", "license": "MIT", "plat": "scenario", "live": "Not checked (runs locally)"}, {"id": "skill:glitternetwork/pinme/skills/pinme-llm", "name": "pinme-llm", "kind": "skill", "official": false, "desc": "Use this skill when a PinMe project (Worker TypeScript) needs to call OpenRouter-backed LLM APIs, including models, chat/completions, streaming, or OpenRouter web search. Guides AI to generate correct Worker TS code.", "repo": "https://github.com/glitternetwork/pinme/tree/HEAD/skills/pinme-llm", "install": ["npx skills add glitternetwork/pinme --skill pinme-llm"], "stars": 3760, "pushed": "2026-09-12T05:23:49Z", "license": "MIT", "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:davepoon/buildwithclaude/plugins/all-skills/skills/hexanon-x402-apis", "name": "hexanon-x402-apis", "kind": "skill", "official": false, "desc": "Pay-per-call access to the Hexanon family of eight x402 APIs for autonomous agents — no signup, no API keys, USDC on Base. Covers US/Canada vehicle & VIN intelligence (Vindex), e-commerce demand signals (Demandex), Polymarket whale intelligence (OrcaTrace), GitHub trending-repo digests (gitBeacon), narrative intelligence (Signalis), Moltbook community digests (Moltalyzer), x402 seller conformance scanning (x402lint), and Polymarket weather-market signals (Isocast).", "repo": "https://github.com/davepoon/buildwithclaude/tree/HEAD/plugins/all-skills/skills/hexanon-x402-apis", "install": ["npx skills add davepoon/buildwithclaude --skill hexanon-x402-apis"], "stars": 3743, "pushed": "2026-11-13T07:12:33Z", "license": "MIT", "plat": "hexanon", "live": "Not checked (runs locally)"}, {"id": "skill:davepoon/buildwithclaude/plugins/all-skills/skills/localaipicks", "name": "localaipicks", "kind": "skill", "official": false, "desc": "Find open-weight AI models that run on the user's own hardware with Local AI Picks, with weight file sizes, memory needs, sources and test status. Use when the user asks which local LLM, coding, speech, image, video, music, translation or OCR model fits their Mac, GPU or RAM, or wants to compare open-weight models before downloading. No key needed.", "repo": "https://github.com/davepoon/buildwithclaude/tree/HEAD/plugins/all-skills/skills/localaipicks", "install": ["npx skills add davepoon/buildwithclaude --skill localaipicks"], "stars": 3743, "pushed": "2026-11-13T07:12:33Z", "license": "MIT", "plat": "local ai", "live": "Not checked (runs locally)"}, {"id": "skill:davepoon/buildwithclaude/plugins/all-skills/skills/perq", "name": "perq", "kind": "skill", "official": false, "desc": "Find verified free AI credits, free tiers, startup programs and student perks for LLM APIs, GPUs, voice, databases and dev tools through Perq's read-only search API or hosted MCP server. Use when the user asks what free credits they can get, the cheapest way to try a model or API, or which startup or student programs apply. Every answer carries the vendor's source link and a last-verified date.", "repo": "https://github.com/davepoon/buildwithclaude/tree/HEAD/plugins/all-skills/skills/perq", "install": ["npx skills add davepoon/buildwithclaude --skill perq"], "stars": 3743, "pushed": "2026-11-13T07:12:33Z", "license": "MIT", "plat": "perq", "live": "Not checked (runs locally)"}, {"id": "skill:davepoon/buildwithclaude/plugins/all-skills/skills/routerbase-model-gateway", "name": "routerbase-model-gateway", "kind": "skill", "official": false, "desc": "Integrate and route AI model requests through RouterBase. Use when migrating OpenAI-compatible clients, choosing model IDs, configuring fallbacks, or building chat, image, video, audio, speech, and embedding workflows.", "repo": "https://github.com/davepoon/buildwithclaude/tree/HEAD/plugins/all-skills/skills/routerbase-model-gateway", "install": ["npx skills add davepoon/buildwithclaude --skill routerbase-model-gateway"], "stars": 3743, "pushed": "2026-11-13T07:12:33Z", "license": "MIT", "plat": "routerbase", "live": "Not checked (runs locally)"}, {"id": "skill:Ar9av/obsidian-wiki/skills/llm-wiki", "name": "llm-wiki", "kind": "skill", "official": false, "desc": "Explain and apply the foundational LLM Wiki architecture and knowledge-management strategy. Use for setup, architecture, schema, or organization decisions; operational ingest/query/lint work uses dedicated skills.", "repo": "https://github.com/Ar9av/obsidian-wiki/tree/HEAD/skills/llm-wiki", "install": ["npx skills add Ar9av/obsidian-wiki --skill llm-wiki"], "stars": 3724, "pushed": "2026-11-12T12:15:04Z", "license": "MIT", "plat": "wiki", "live": "Not checked (runs locally)"}, {"id": "skill:ljagiello/ctf-skills/ctf-ai-ml", "name": "ctf-ai-ml", "kind": "skill", "official": false, "desc": "Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.", "repo": "https://github.com/ljagiello/ctf-skills/tree/HEAD/ctf-ai-ml", "install": ["npx skills add ljagiello/ctf-skills --skill ctf-ai-ml"], "stars": 3701, "pushed": "2026-11-13T15:16:52Z", "license": "MIT", "plat": "ctf", "live": "Not checked (runs locally)"}, {"id": "skill:ljagiello/ctf-skills/ctf-crypto", "name": "ctf-crypto", "kind": "skill", "official": false, "desc": "Provides cryptography attack techniques for CTF challenges. Use when attacking encryption, hashing, signatures, ZKP, PRNG, or mathematical crypto problems involving RSA, AES, ECC, lattices, LWE, CVP, number theory, Coppersmith, Pollard, Wiener, padding oracle, GCM, key derivation, or stream/block cipher weaknesses.", "repo": "https://github.com/ljagiello/ctf-skills/tree/HEAD/ctf-crypto", "install": ["npx skills add ljagiello/ctf-skills --skill ctf-crypto"], "stars": 3701, "pushed": "2026-11-13T15:16:52Z", "license": "MIT", "plat": "ctf", "live": "Not checked (runs locally)"}, {"id": "skill:ljagiello/ctf-skills/ctf-forensics", "name": "ctf-forensics", "kind": "skill", "official": false, "desc": "Provides digital forensics and signal analysis techniques for CTF challenges. Use when analyzing disk images, memory dumps, event logs, network captures, cryptocurrency transactions, steganography, PDF analysis, Windows registry, Volatility, PCAP, Docker images, coredumps, side-channel power traces, DTMF audio spectrograms, packet timing analysis, CD audio disc images, or recovering deleted files and credentials.", "repo": "https://github.com/ljagiello/ctf-skills/tree/HEAD/ctf-forensics", "install": ["npx skills add ljagiello/ctf-skills --skill ctf-forensics"], "stars": 3701, "pushed": "2026-11-13T15:16:52Z", "license": "MIT", "plat": "ctf", "live": "Not checked (runs locally)"}, {"id": "skill:ljagiello/ctf-skills/ctf-malware", "name": "ctf-malware", "kind": "skill", "official": false, "desc": "Provides malware analysis and network traffic techniques for CTF challenges. Use when analyzing obfuscated scripts, malicious packages, custom crypto protocols, C2 traffic, PE/.NET binaries, RC4/AES encrypted communications, YARA rules, shellcode analysis, memory forensics for malware (Volatility malfind, process injection detection), anti-analysis techniques (VM/sandbox detection, timing evasion, API hashing, process injection, environment checks), or extracting malware configurations and indic", "repo": "https://github.com/ljagiello/ctf-skills/tree/HEAD/ctf-malware", "install": ["npx skills add ljagiello/ctf-skills --skill ctf-malware"], "stars": 3701, "pushed": "2026-11-13T15:16:52Z", "license": "MIT", "plat": "ctf", "live": "Not checked (runs locally)"}, {"id": "skill:ljagiello/ctf-skills/ctf-misc", "name": "ctf-misc", "kind": "skill", "official": false, "desc": "Provides miscellaneous CTF challenge techniques for problems that do not cleanly fit the main categories. Use for encoding puzzles, pyjails, bash jails, RF/SDR, DNS oddities, unicode tricks, esoteric languages, QR or audio puzzles, constraint solving, game theory, unusual sandbox escapes, and hybrid logic puzzles. Prefer a more specific skill first when the challenge is mainly web, pwn, reverse, forensics, malware, OSINT, or crypto. Treat this as the fallback skill for genuine cross-category or ", "repo": "https://github.com/ljagiello/ctf-skills/tree/HEAD/ctf-misc", "install": ["npx skills add ljagiello/ctf-skills --skill ctf-misc"], "stars": 3701, "pushed": "2026-11-13T15:16:52Z", "license": "MIT", "plat": "ctf", "live": "Not checked (runs locally)"}, {"id": "skill:ljagiello/ctf-skills/ctf-osint", "name": "ctf-osint", "kind": "skill", "official": false, "desc": "Provides open source intelligence techniques for CTF challenges. Use when gathering information from public sources, social media, geolocation, DNS records, username enumeration, reverse image search, Google dorking, Wayback Machine, Tor relays, FEC filings, or identifying unknown data like hashes and coordinates.", "repo": "https://github.com/ljagiello/ctf-skills/tree/HEAD/ctf-osint", "install": ["npx skills add ljagiello/ctf-skills --skill ctf-osint"], "stars": 3701, "pushed": "2026-11-13T15:16:52Z", "license": "MIT", "plat": "ctf", "live": "Not checked (runs locally)"}, {"id": "skill:ljagiello/ctf-skills/ctf-pwn", "name": "ctf-pwn", "kind": "skill", "official": false, "desc": "Provides binary exploitation techniques for CTF challenges. Use when you already have a vulnerable native target or service and need to turn memory corruption or low-level primitives into code execution or privilege escalation, such as buffer overflows, format strings, heap bugs, ROP, ret2libc, shellcode, kernel exploitation, seccomp bypass, sandbox escape, or Windows/Linux exploit chains. Do not use it when the main blocker is understanding what the binary does; use reverse engineering first. D", "repo": "https://github.com/ljagiello/ctf-skills/tree/HEAD/ctf-pwn", "install": ["npx skills add ljagiello/ctf-skills --skill ctf-pwn"], "stars": 3701, "pushed": "2026-11-13T15:16:52Z", "license": "MIT", "plat": "ctf", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:liaokongVFX/MCP-Chinese-Getting-Started-Guide", "name": "MCP-Chinese-Getting-Started-Guide", "kind": "mcp", "official": null, "desc": "Model Context Protocol(MCP) 编程极速入门", "repo": "https://github.com/liaokongVFX/MCP-Chinese-Getting-Started-Guide", "install": [], "stars": 3584, "pushed": null, "license": null, "plat": "model context protocol", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.divu2025/codemaster", "name": "Claude Code Explorer MCP", "kind": "mcp", "official": null, "desc": "Explore the Claude Code CLI source — browse tools, commands, search code, and more.", "repo": "https://github.com/codeaashu/claude-code", "install": ["npm:@divyesh2000/codemaster"], "stars": 3437, "pushed": "2026-10-14T14:15:16Z", "license": "NOASSERTION", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:metorial/metorial", "name": "metorial", "kind": "mcp", "official": null, "desc": "Connect any AI model to 1200+ integrations (MCP, CLI, API)", "repo": "https://github.com/metorial/metorial", "install": [], "stars": 3379, "pushed": null, "license": null, "plat": "metorial", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:off-grid-ai/OGAM", "name": "OGAM", "kind": "mcp", "official": null, "desc": "The Swiss Army Knife of Offline AI. Chat, see, speak, and generate images on your phone or Mac — GGUF LLMs, vision, Whisper speech-to-text, Stable Diffusion, tool calling, and local-network servers. Runs on your CPU, GPU, or NPU. No account, no API key, zero data leaves your device.", "repo": "https://github.com/off-grid-ai/OGAM", "install": [], "stars": 3378, "pushed": null, "license": null, "plat": "ogam", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:punkpeye/fastmcp", "name": "fastmcp", "kind": "mcp", "official": null, "desc": "A TypeScript framework for building MCP servers.", "repo": "https://github.com/punkpeye/fastmcp", "install": [], "stars": 3288, "pushed": null, "license": null, "plat": "fastmcp", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:av/harbor", "name": "harbor", "kind": "mcp", "official": null, "desc": "Stop configuring your AI stack. Start using it. One command brings a complete pre-wired LLM stack with hundreds of services to explore.", "repo": "https://github.com/av/harbor", "install": [], "stars": 3280, "pushed": null, "license": null, "plat": "harbor", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-skills/skills/azure-ai", "name": "azure-ai", "kind": "skill", "official": false, "desc": "Build and troubleshoot Azure AI Search, Speech, OpenAI, and Document Intelligence apps. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, Speech SDK logs, session ID, result ID, OCR, convert text to speech.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-skills/skills/azure-ai", "install": ["npx skills add microsoft/skills --skill azure-ai"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-ai-anomalydetector-java", "name": "azure-ai-anomalydetector-java", "kind": "skill", "official": false, "desc": "Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-java/skills/azure-ai-anomalydetector-java", "install": ["npx skills add microsoft/skills --skill azure-ai-anomalydetector-java"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai anomaly detector", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-ai-contentsafety-java", "name": "azure-ai-contentsafety-java", "kind": "skill", "official": false, "desc": "Build content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text/image analysis, blocklist management, or harm detection for hate, violence, sexual content, and self-harm.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-java/skills/azure-ai-contentsafety-java", "install": ["npx skills add microsoft/skills --skill azure-ai-contentsafety-java"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai content safety", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py", "name": "azure-ai-contentsafety-py", "kind": "skill", "official": false, "desc": "Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: \"azure-ai-contentsafety\", \"ContentSafetyClient\", \"content moderation\", \"harmful content\", \"text analysis\", \"image analysis\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py", "install": ["npx skills add microsoft/skills --skill azure-ai-contentsafety-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai content safety", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py", "name": "azure-ai-contentunderstanding-py", "kind": "skill", "official": false, "desc": "Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: \"azure-ai-contentunderstanding\", \"ContentUnderstandingClient\", \"multimodal analysis\", \"document extraction\", \"video analysis\", \"audio transcription\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py", "install": ["npx skills add microsoft/skills --skill azure-ai-contentunderstanding-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai content understanding", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-ai-formrecognizer-java", "name": "azure-ai-formrecognizer-java", "kind": "skill", "official": false, "desc": "Azure AI Document Intelligence SDK for Java (com.azure:azure-ai-documentintelligence). Use for extracting text, tables, key-value pairs from documents, receipts, invoices, IDs, or building custom document models. Triggers: \"document intelligence java\", \"form recognizer java\", \"extract text from PDF java\", \"OCR document java\", \"analyze invoice receipt java\", \"custom document model java\", \"document classification java\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-java/skills/azure-ai-formrecognizer-java", "install": ["npx skills add microsoft/skills --skill azure-ai-formrecognizer-java"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai document intelligence", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py", "name": "azure-ai-language-conversations-py", "kind": "skill", "official": false, "desc": "Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py", "install": ["npx skills add microsoft/skills --skill azure-ai-language-conversations-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai language conversations", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py", "name": "azure-ai-ml-py", "kind": "skill", "official": false, "desc": "Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: \"azure-ai-ml\", \"MLClient\", \"workspace\", \"model registry\", \"training jobs\", \"datasets\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py", "install": ["npx skills add microsoft/skills --skill azure-ai-ml-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure machine learning", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-dotnet/skills/azure-ai-openai-dotnet", "name": "azure-ai-openai-dotnet", "kind": "skill", "official": false, "desc": "Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: \"Azure OpenAI\", \"AzureOpenAIClient\", \"ChatClient\", \"chat completions .NET\", \"GPT-4\", \"embeddings\", \"DALL-E\", \"Whisper\", \"OpenAI .NET\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-dotnet/skills/azure-ai-openai-dotnet", "install": ["npx skills add microsoft/skills --skill azure-ai-openai-dotnet"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-typescript/skills/azure-ai-projects-ts", "name": "azure-ai-projects-ts", "kind": "skill", "official": false, "desc": "Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-typescript/skills/azure-ai-projects-ts", "install": ["npx skills add microsoft/skills --skill azure-ai-projects-ts"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py", "name": "azure-ai-textanalytics-py", "kind": "skill", "official": false, "desc": "Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text. Triggers: \"text analytics\", \"sentiment analysis\", \"entity recognition\", \"key phrase\", \"PII detection\", \"TextAnalyticsClient\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py", "install": ["npx skills add microsoft/skills --skill azure-ai-textanalytics-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai text analytics", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py", "name": "azure-ai-transcription-py", "kind": "skill", "official": false, "desc": "Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization. Triggers: \"transcription\", \"speech to text\", \"Azure AI Transcription\", \"TranscriptionClient\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py", "install": ["npx skills add microsoft/skills --skill azure-ai-transcription-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai transcription", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py", "name": "azure-ai-translation-document-py", "kind": "skill", "official": false, "desc": "Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale. Triggers: \"document translation\", \"batch translation\", \"translate documents\", \"DocumentTranslationClient\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py", "install": ["npx skills add microsoft/skills --skill azure-ai-translation-document-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai document translation", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py", "name": "azure-ai-translation-text-py", "kind": "skill", "official": false, "desc": "Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications. Triggers: \"text translation\", \"translator\", \"translate text\", \"transliterate\", \"TextTranslationClient\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py", "install": ["npx skills add microsoft/skills --skill azure-ai-translation-text-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai text translation", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-typescript/skills/azure-ai-translation-ts", "name": "azure-ai-translation-ts", "kind": "skill", "official": false, "desc": "Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest/ai-translation-text, @azure-rest/ai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-typescript/skills/azure-ai-translation-ts", "install": ["npx skills add microsoft/skills --skill azure-ai-translation-ts"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-ai-vision-imageanalysis-java", "name": "azure-ai-vision-imageanalysis-java", "kind": "skill", "official": false, "desc": "Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-java/skills/azure-ai-vision-imageanalysis-java", "install": ["npx skills add microsoft/skills --skill azure-ai-vision-imageanalysis-java"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai vision", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py", "name": "azure-ai-vision-imageanalysis-py", "kind": "skill", "official": false, "desc": "Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: \"image analysis\", \"computer vision\", \"OCR\", \"object detection\", \"ImageAnalysisClient\", \"image caption\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py", "install": ["npx skills add microsoft/skills --skill azure-ai-vision-imageanalysis-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai vision image analysis", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-ai-voicelive-java", "name": "azure-ai-voicelive-java", "kind": "skill", "official": false, "desc": "Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket. Triggers: \"VoiceLiveClient java\", \"voice assistant java\", \"real-time voice java\", \"audio streaming java\", \"voice activity detection java\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-java/skills/azure-ai-voicelive-java", "install": ["npx skills add microsoft/skills --skill azure-ai-voicelive-java"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai voicelive", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py", "name": "azure-ai-voicelive-py", "kind": "skill", "official": false, "desc": "Build real-time voice AI applications using Azure AI Voice Live SDK (azure-ai-voicelive). Use this skill when creating Python applications that need real-time bidirectional audio communication with Azure AI, including voice assistants, voice-enabled chatbots, real-time speech-to-speech translation, voice-driven avatars, or any WebSocket-based audio streaming with AI models. Supports Server VAD (Voice Activity Detection), turn-based conversation, function calling, MCP tools, avatar integration, a", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py", "install": ["npx skills add microsoft/skills --skill azure-ai-voicelive-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure ai voice live", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-typescript/skills/azure-ai-voicelive-ts", "name": "azure-ai-voicelive-ts", "kind": "skill", "official": false, "desc": "Azure AI Voice Live SDK for JavaScript/TypeScript. Build real-time voice AI applications with bidirectional WebSocket communication. Use for voice assistants, conversational AI, real-time speech-to-speech, and voice-enabled chatbots in Node.js or browser environments. Triggers: \"voice live\", \"real-time voice\", \"VoiceLiveClient\", \"VoiceLiveSession\", \"voice assistant TypeScript\", \"bidirectional audio\", \"speech-to-speech JavaScript\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-typescript/skills/azure-ai-voicelive-ts", "install": ["npx skills add microsoft/skills --skill azure-ai-voicelive-ts"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-arizeaiobservabilityeval-dotnet", "name": "azure-mgmt-arizeaiobservabilityeval-dotnet", "kind": "skill", "official": false, "desc": "Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET). Use when managing Arize AI organizations \\non Azure via Azure Marketplace, creating/updating/deleting Arize resources, or integrating Arize ML observability \\ninto .NET applications. Triggers: \\\"Arize AI\\\", \\\"ML observability\\\", \\\"ArizeAIObservabilityEval\\\", \\\"Arize organization\\\".\\n", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-arizeaiobservabilityeval-dotnet", "install": ["npx skills add microsoft/skills --skill azure-mgmt-arizeaiobservabilityeval-dotnet"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "arize ai", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-weightsandbiases-dotnet", "name": "azure-mgmt-weightsandbiases-dotnet", "kind": "skill", "official": false, "desc": "Azure Weights & Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&B instances, managing SSO, marketplace integration, and ML observability. Triggers: \"Weights and Biases\", \"W&B\", \"WeightsAndBiases\", \"ML experiment tracking\", \"model registry\", \"experiment management\", \"wandb\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-weightsandbiases-dotnet", "install": ["npx skills add microsoft/skills --skill azure-mgmt-weightsandbiases-dotnet"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "weights & biases", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-skills/skills/microsoft-foundry/finetuning", "name": "finetuning", "kind": "skill", "official": false, "desc": "Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents),", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-skills/skills/microsoft-foundry/finetuning", "install": ["npx skills add microsoft/skills --skill finetuning"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "microsoft foundry", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-skills/skills/microsoft-foundry", "name": "microsoft-foundry", "kind": "skill", "official": false, "desc": "Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent, managed harness agent, Copilot harness, voice agent, create/update/invoke/evaluate agent, add tool to agent, agent insights, batch/continuous eval, continuous monitoring, agent CI/CD, optimize/improve prompt, optimize agent instructions, Agent Optimizer, datas", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-skills/skills/microsoft-foundry", "install": ["npx skills add microsoft/skills --skill microsoft-foundry"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "microsoft foundry", "live": "Not checked (runs locally)"}, {"id": "skill:open-gitagent/opengap/examples/jason-my-claw-is-the-law-deebee-4567b4/skills/compute-ladder", "name": "compute-ladder", "kind": "skill", "official": false, "desc": "Model selection by provider health, not task complexity. Tier 0 (local) handles most tasks. Paid models are break-glass only. Fallbacks activate on 429 or timeout — never on perceived task difficulty.", "repo": "https://github.com/open-gitagent/opengap/tree/HEAD/examples/jason-my-claw-is-the-law-deebee-4567b4/skills/compute-ladder", "install": ["npx skills add open-gitagent/opengap --skill compute-ladder"], "stars": 2988, "pushed": "2026-07-02T21:15:45Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:rohitg00/pro-workflow/skills/llm-council", "name": "llm-council", "kind": "skill", "official": false, "desc": "Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Credentials come from explicit plugin configuration or pro-workflow-specific CLI variables. Persists transcript to a wiki page when --wiki <slug> is passed. Use when the user wants multiple AI perspectives, consensus-building, or the \"LLM Council\" approach for high-stakes reviews, plan critique, or contested learning rules.", "repo": "https://github.com/rohitg00/pro-workflow/tree/HEAD/skills/llm-council", "install": ["npx skills add rohitg00/pro-workflow --skill llm-council"], "stars": 2955, "pushed": "2026-09-29T10:12:13Z", "license": null, "plat": "mcp servers", "live": "Not checked (runs locally)"}, {"id": "skill:rohitg00/pro-workflow/skills/llm-gate", "name": "llm-gate", "kind": "skill", "official": false, "desc": "LLM-powered quality verification using prompt hooks. Validates commit messages, code patterns, and conventions using AI before allowing operations. Use to set up intelligent guardrails.", "repo": "https://github.com/rohitg00/pro-workflow/tree/HEAD/skills/llm-gate", "install": ["npx skills add rohitg00/pro-workflow --skill llm-gate"], "stars": 2955, "pushed": "2026-09-29T10:12:13Z", "license": null, "plat": "mcp servers", "live": "Not checked (runs locally)"}, {"id": "skill:rohitg00/pro-workflow/skills/skill-optimizer", "name": "skill-optimizer", "kind": "skill", "official": false, "desc": "SkillOpt-flavored offline training loop for any SKILL.md. Treats accumulated learn-rule corrections as training trajectories, proposes bounded patches via an optimizer LLM, gates each candidate against a held-out validation set built from the user's own past corrections, and ships only candidates that demonstrably improve the score. Inspired by Microsoft SkillOpt's ReflACT pipeline (rollout → reflect → aggregate → select → update → evaluate) adapted to pro-workflow's SQLite store. Use when a ski", "repo": "https://github.com/rohitg00/pro-workflow/tree/HEAD/skills/skill-optimizer", "install": ["npx skills add rohitg00/pro-workflow --skill skill-optimizer"], "stars": 2955, "pushed": "2026-09-29T10:12:13Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:rohitg00/pro-workflow/skills/token-efficiency", "name": "token-efficiency", "kind": "skill", "official": false, "desc": "Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient.", "repo": "https://github.com/rohitg00/pro-workflow/tree/HEAD/skills/token-efficiency", "install": ["npx skills add rohitg00/pro-workflow --skill token-efficiency"], "stars": 2955, "pushed": "2026-09-29T10:12:13Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/transfer-learning-adapter/skills/adapting-transfer-learning-models", "name": "adapting-transfer-learning-models", "kind": "skill", "official": false, "desc": "Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/transfer-learning-adapter/skills/adapting-transfer-learning-models", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill adapting-transfer-learning-models"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/sentiment-analysis-tool/skills/analyzing-text-sentiment", "name": "analyzing-text-sentiment", "kind": "skill", "official": false, "desc": "Execute this skill enables AI assistant to analyze the sentiment of text data. it identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. use this skill when a user requests sentiment analysis, opinion mining, or emoti... Use when analyzing code or data. Trigger with phrases like ''analyze'', ''review'', or ''examine''. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/sentiment-analysis-tool/skills/analyzing-text-sentiment", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill analyzing-text-sentiment"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/nlp-text-analyzer/skills/analyzing-text-with-nlp", "name": "analyzing-text-with-nlp", "kind": "skill", "official": false, "desc": "Execute this skill enables AI assistant to perform natural language processing and text analysis using the nlp-text-analyzer plugin. it should be used when the user requests analysis of text, including sentiment analysis, keyword extraction, topic modeling, or ... Use when analyzing code or data. Trigger with phrases like ''analyze'', ''review'', or ''examine''. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/nlp-text-analyzer/skills/analyzing-text-with-nlp", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill analyzing-text-with-nlp"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/automl-pipeline-builder/skills/building-automl-pipelines", "name": "building-automl-pipelines", "kind": "skill", "official": false, "desc": "Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like \"build automl pipeline\", \"automate ml workflow\", or \"create automated training pipeline\". '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/automl-pipeline-builder/skills/building-automl-pipelines", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-automl-pipelines"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/classification-model-builder/skills/building-classification-models", "name": "building-classification-models", "kind": "skill", "official": false, "desc": "Build and evaluate classification models for supervised learning tasks with labeled data. Use when requesting \"build a classifier\", \"create classification model\", or \"train classifier\". Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/classification-model-builder/skills/building-classification-models", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-classification-models"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/neural-network-builder/skills/building-neural-networks", "name": "building-neural-networks", "kind": "skill", "official": false, "desc": "Execute this skill allows AI assistant to construct and configure neural network architectures using the neural-network-builder plugin. it should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/neural-network-builder/skills/building-neural-networks", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-neural-networks"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/recommendation-engine/skills/building-recommendation-systems", "name": "building-recommendation-systems", "kind": "skill", "official": false, "desc": "Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. it analyzes user preferences, item features, and interaction data to generate personalized recommendations... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/recommendation-engine/skills/building-recommendation-systems", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-recommendation-systems"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/cortex", "name": "cortex", "kind": "skill", "official": false, "desc": "ML/AI engineer — LLM integrations, prompt engineering, model pipelines, evals, RAG.", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/cortex", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/cortex-integrate", "name": "cortex-integrate", "kind": "skill", "official": false, "desc": "Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to \"add AI to this\", \"LLM integration\", \"add Claude/GPT\", or \"AI-powered feature\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/cortex-integrate", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-integrate"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "claude/gpt", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/cortex-model", "name": "cortex-model", "kind": "skill", "official": false, "desc": "Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to \"build ML model\", \"train a model\", \"prediction pipeline\", \"classification\", or \"regression\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/cortex-model", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-model"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "ml", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/cortex-prompt", "name": "cortex-prompt", "kind": "skill", "official": false, "desc": "Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to \"prompt engineering\", \"build a prompt\", \"write a system prompt\", or \"improve this prompt\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/cortex-prompt", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-prompt"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/cortex-recon", "name": "cortex-recon", "kind": "skill", "official": false, "desc": "ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked \"what ML do we have\", \"model inventory\", or \"ML assessment\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/cortex-recon", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-recon"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "ml", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/model-deployment-helper/skills/deploying-machine-learning-models", "name": "deploying-machine-learning-models", "kind": "skill", "official": false, "desc": "Deploy this skill enables AI assistant to deploy machine learning models to production environments. it automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. use this skill when th... Use when deploying or managing infrastructure. Trigger with phrases like ''deploy'', ''infrastructure'', or ''CI/CD''. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/model-deployment-helper/skills/deploying-machine-learning-models", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill deploying-machine-learning-models"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/feature-engineering-toolkit/skills/engineering-features-for-machine-learning", "name": "engineering-features-for-machine-learning", "kind": "skill", "official": false, "desc": "Execute create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to \"engineer features\" or \"select features\". Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/feature-engineering-toolkit/skills/engineering-features-for-machine-learning", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill engineering-features-for-machine-learning"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/model-evaluation-suite/skills/evaluating-machine-learning-models", "name": "evaluating-machine-learning-models", "kind": "skill", "official": false, "desc": "Build this skill allows AI assistant to evaluate machine learning models using a comprehensive suite of metrics. it should be used when the user requests model performance analysis, validation, or testing. AI assistant can use this skill to assess model accuracy, p... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/model-evaluation-suite/skills/evaluating-machine-learning-models", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill evaluating-machine-learning-models"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/time-series-forecaster/skills/forecasting-time-series-data", "name": "forecasting-time-series-data", "kind": "skill", "official": false, "desc": "Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/time-series-forecaster/skills/forecasting-time-series-data", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill forecasting-time-series-data"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/jeremy-gcp-starter-examples/skills/gcp-examples-expert", "name": "gcp-examples-expert", "kind": "skill", "official": false, "desc": "Generate production-ready Google Cloud code examples from official repositories including ADK samples, Genkit templates, Vertex AI notebooks, and Gemini patterns. Use when asked to \"show ADK example\" or \"provide GCP starter kit\". Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/jeremy-gcp-starter-examples/skills/gcp-examples-expert", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill gcp-examples-expert"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "google cloud", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/ollama-local-ai/skills/ollama-setup", "name": "ollama-setup", "kind": "skill", "official": false, "desc": "Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs. Trigger phrases: \"install ollama\", \"local AI\", \"free LLM\", \"self-hosted AI\", \"replace OpenAI\", \"no API costs\". Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/ollama-local-ai/skills/ollama-setup", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill ollama-setup"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/deep-learning-optimizer/skills/optimizing-deep-learning-models", "name": "optimizing-deep-learning-models", "kind": "skill", "official": false, "desc": "Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time. Use when asked to \"optimize deep learning model\" or \"improve model performance\". Trigger with phrases like ''optimize'', ''performance'', or ''speed up''. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/deep-learning-optimizer/skills/optimizing-deep-learning-models", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill optimizing-deep-learning-models"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/regression-analysis-tool/skills/performing-regression-analysis", "name": "performing-regression-analysis", "kind": "skill", "official": false, "desc": "Execute this skill empowers AI assistant to perform regression analysis and modeling using the regression-analysis-tool plugin. it analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performa... Use when analyzing code or data. Trigger with phrases like ''analyze'', ''review'', or ''examine''. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/regression-analysis-tool/skills/performing-regression-analysis", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill performing-regression-analysis"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/computer-vision-processor/skills/processing-computer-vision-tasks", "name": "processing-computer-vision-tasks", "kind": "skill", "official": false, "desc": "Process images using object detection, classification, and segmentation. Use when requesting \"analyze image\", \"object detection\", \"image classification\", or \"computer vision\". Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/computer-vision-processor/skills/processing-computer-vision-tasks", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill processing-computer-vision-tasks"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/clustering-algorithm-runner/skills/running-clustering-algorithms", "name": "running-clustering-algorithms", "kind": "skill", "official": false, "desc": "Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting \"run clustering\", \"cluster analysis\", or \"group data points\". Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/clustering-algorithm-runner/skills/running-clustering-algorithms", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill running-clustering-algorithms"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/experiment-tracking-setup/skills/setting-up-experiment-tracking", "name": "setting-up-experiment-tracking", "kind": "skill", "official": false, "desc": "Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to \"setup experiment tracking\" or \"initialize MLflow\". Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/experiment-tracking-setup/skills/setting-up-experiment-tracking", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill setting-up-experiment-tracking"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/model-versioning-tracker/skills/tracking-model-versions", "name": "tracking-model-versions", "kind": "skill", "official": false, "desc": "Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin. it should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control f... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/model-versioning-tracker/skills/tracking-model-versions", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill tracking-model-versions"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/ml-model-trainer/skills/training-machine-learning-models", "name": "training-machine-learning-models", "kind": "skill", "official": false, "desc": "Build train machine learning models with automated workflows. Analyzes datasets, selects model types (classification, regression), configures parameters, trains with cross-validation, and saves model artifacts. Use when asked to \"train model\" or \"evalua... Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/ml-model-trainer/skills/training-machine-learning-models", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill training-machine-learning-models"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-ml/hyperparameter-tuner/skills/tuning-hyperparameters", "name": "tuning-hyperparameters", "kind": "skill", "official": false, "desc": "Optimize machine learning model hyperparameters using grid search, random search, or Bayesian optimization. Finds best parameter configurations to maximize performance. Use when asked to \"tune hyperparameters\" or \"optimize model\". Trigger with relevant phrases based on skill purpose. '", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-ml/hyperparameter-tuner/skills/tuning-hyperparameters", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill tuning-hyperparameters"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:typesafe-ai/skills/skills/typesafe-ai", "name": "typesafe-ai", "kind": "skill", "official": false, "desc": "TypeSafe integration craft for System One models (sometimes called Decision Models) such as Jev, which answer typed questions about text, images, and application state with calibrated probabilities. Use when building, reviewing, or debugging code that calls TypeSafe; when writing its questions or state; when replacing an LLM prompt-and-parse step, keyword matching, or a brittle heuristic with a typed question; or when brainstorming where programmable common sense fits in an app.", "repo": "https://github.com/typesafe-ai/skills/tree/HEAD/skills/typesafe-ai", "install": ["npx skills add typesafe-ai/skills --skill typesafe-ai"], "stars": 2899, "pushed": "2026-10-23T21:40:54Z", "license": "MIT", "plat": "system one", "live": "Not checked (runs locally)"}, {"id": "skill:rohitg00/awesome-claude-code-toolkit/skills/llm-integration", "name": "llm-integration", "kind": "skill", "official": false, "desc": "LLM integration patterns including API usage, streaming, function calling, RAG pipelines, and cost optimization", "repo": "https://github.com/rohitg00/awesome-claude-code-toolkit/tree/HEAD/skills/llm-integration", "install": ["npx skills add rohitg00/awesome-claude-code-toolkit --skill llm-integration"], "stars": 2838, "pushed": "2026-05-12T07:18:34Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:softaworks/agent-toolkit/dist/plugins/codex/skills/codex", "name": "codex", "kind": "skill", "official": false, "desc": "Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.2 by default for state-of-the-art software engineering.", "repo": "https://github.com/softaworks/agent-toolkit/tree/HEAD/dist/plugins/codex/skills/codex", "install": ["npx skills add softaworks/agent-toolkit --skill codex"], "stars": 2679, "pushed": "2026-03-05T16:46:24Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ggozad/oterm", "name": "oterm", "kind": "mcp", "official": null, "desc": "the terminal client for LLMs", "repo": "https://github.com/ggozad/oterm", "install": [], "stars": 2447, "pushed": null, "license": null, "plat": "llms", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:amontlabs/lcu", "name": "lcu", "kind": "mcp", "official": null, "desc": "Codex computer use, decoupled from the app, for usage inside any harness.", "repo": "https://github.com/amontlabs/lcu", "install": [], "stars": 2426, "pushed": null, "license": null, "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:MCPJam/inspector", "name": "inspector", "kind": "mcp", "official": null, "desc": "Testing and evaluation platform to chat, inspect, and debug MCP servers, MCP apps, and ChatGPT apps.", "repo": "https://github.com/MCPJam/inspector", "install": [], "stars": 2319, "pushed": null, "license": null, "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jamubc/gemini-mcp-tool", "name": "gemini-mcp-tool", "kind": "mcp", "official": null, "desc": "MCP server that enables AI assistants to interact with Google Gemini CLI, leveraging Gemini's massive token window for large file analysis and codebase understanding", "repo": "https://github.com/jamubc/gemini-mcp-tool", "install": [], "stars": 2298, "pushed": null, "license": null, "plat": "google gemini", "live": "Not checked (runs locally)"}, {"id": "skill:mrgoonie/claudekit-skills/.claude/skills/ai-multimodal", "name": "ai-multimodal", "kind": "skill", "official": false, "desc": "Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, com", "repo": "https://github.com/mrgoonie/claudekit-skills/tree/HEAD/.claude/skills/ai-multimodal", "install": ["npx skills add mrgoonie/claudekit-skills --skill ai-multimodal"], "stars": 2244, "pushed": "2026-04-03T06:19:25Z", "license": null, "plat": "google gemini api", "live": "Not checked (runs locally)"}, {"id": "skill:mrgoonie/claudekit-skills/.claude/skills/skill-creator", "name": "skill-creator", "kind": "skill", "official": false, "desc": "Create or update Claude skills. Use for new skills, skill references, skill scripts, optimizing existing skills, extending Claude's capabilities.", "repo": "https://github.com/mrgoonie/claudekit-skills/tree/HEAD/.claude/skills/skill-creator", "install": ["npx skills add mrgoonie/claudekit-skills --skill skill-creator"], "stars": 2244, "pushed": "2026-04-03T06:19:25Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/add-newcli-provider", "name": "add-newcli-provider", "kind": "skill", "official": false, "desc": "为 OpenClaw 配置 code.newcli.com 作为模型源，包含四个 provider：newcli（Claude 主线路）、newcli-aws（Claude AWS 特价线路，消耗 1/24）、newcli-codex（GPT 系列）、newcli-gemini（Gemini 系列）。适用于需要接入 Claude 或 GPT 模型的场景。包含 provider 注册、模型定义、别名配置、fallback 链接入和验证的完整流程。当管理员说想\"加 Claude\"、\"加 GPT\"、\"配 newcli\"、\"加 fox 源\"、\"接入 Claude 模型\"、\"接入 GPT 模型\"、\"加 codex\"、\"加 aws 线路\"时使用此 skill。", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/add-newcli-provider", "install": ["npx skills add LeoYeAI/agent-master-skills --skill add-newcli-provider"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "code.newcli.com", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/agi-evolution-model-basic", "name": "agi-evolution-model", "kind": "skill", "official": false, "desc": "基于双环架构的AGI进化模型，通过意向性分析、人格层映射和元认知检测实现持续自我演进；当用户需要智能对话、人格定制或复杂问题求解时使用", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/agi-evolution-model-basic", "install": ["npx skills add LeoYeAI/agent-master-skills --skill agi-evolution-model"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-leaderboard", "name": "AI Rankings Leaderboard", "kind": "skill", "official": false, "desc": "Comprehensive AI leaderboard for LLM models and AI applications. Query model rankings, model IDs, and pricing from OpenRouter, Artificial Analysis, and Pinchbench. Trigger words include \"AI rankings\", \"LLM leaderboard\", \"model comparison\", \"AI apps ranking\", \"best AI models\", \"model benchmark\", \"free models\", \"免费模型\", \"OpenRouter model ID\", \"OpenRouter 模型\", \"Artificial Analysis\", \"artificial analysis\", \"AI 智力指数\", \"intelligence index\", \"coding index\", \"coding排行榜\", \"agentic index\", \"agentic排行榜\", \"模", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-leaderboard", "install": ["npx skills add LeoYeAI/agent-master-skills --skill AI Rankings Leaderboard"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "openrouter, artificial analysis, pinchbe", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-md", "name": "ai-md", "kind": "skill", "official": false, "desc": "AI.MD — Convert any human-written CLAUDE.md into AI-native structured format. Your CLAUDE.md is read by AI every single turn, not by you — so write it in AI's language. Battle-tested: 5 rounds, 4 models (GPT-5.3, Gemini 2.5 Pro, Grok-4, Claude Opus 4.6). Structured-label format raised Codex compliance from 6/8 → 8/8 on identical content. Same rules, fewer tokens, higher precision. This skill contains the complete methodology: how to take natural language instructions and convert them into a form", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-md", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-md"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "claude.md", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-model-router-v2", "name": "ai-model-router", "kind": "skill", "official": false, "desc": "Intelligent AI model router that automatically switches between two configured models (local for simple tasks, cloud for complex ones). Detects local models (Ollama, LM Studio) automatically, routes based on task complexity and privacy. Use when users ask to \\\"switch model\\\", \\\"use local/cloud model\\\", or mention API keys/passwords (triggers privacy mode). Trigger on: sensitive data detection (forces local), complex tasks like \\\"design architecture\\\" (uses cloud), or configuration requests.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-model-router-v2", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-model-router"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "ollama, lm studio", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-ppt-generator", "name": "ai-ppt-generator", "kind": "skill", "official": false, "desc": "Generate PPT with Baidu AI. Smart template selection based on content.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-ppt-generator", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-ppt-generator"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "baidu ai", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-scam-defense", "name": "ai-scam-defense", "kind": "skill", "official": false, "desc": "Identify and defend against AI-powered scams including deepfakes, voice cloning, AI phishing, and fake job offers. Use when someone received a suspicious call from a \"family member,\" got a too-perfect email, encountered a video call that felt off, or suspects any AI-generated fraud.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-scam-defense", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-scam-defense"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-security", "name": "ai-security", "kind": "skill", "official": false, "desc": "Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-security", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-security"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-shifu-course-creator", "name": "ai-shifu-course-creator", "kind": "skill", "official": false, "desc": "Convert raw course material into optimized, runnable MarkdownFlow teaching scripts and deploy them as live courses through a five-phase pipeline covering segmentation, orchestration, generation, optimization, and deployment.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-shifu-course-creator", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-shifu-course-creator"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "markdownflow", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-task-hub", "name": "ai-task-hub", "kind": "skill", "official": false, "desc": "AI task hub for image analysis, background removal, speech-to-text, text-to-speech, markdown conversion, and async execute/poll/presentation orchestration. Use when users need hosted AI outcomes while host runtime manages identity, credits, payment, and risk control.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-task-hub", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-task-hub"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-text-to-video-generator", "name": "ai-text-to-video-generator", "kind": "skill", "official": false, "desc": "The ai-text-to-video-generator skill on ClawHub transforms written prompts into fully rendered video content through a conversational interface. Describe your scene, tone, pacing, and visual style in plain language — the skill interprets your intent and produces polished video output without requiring editing software or technical know-how. Ideal for content creators, marketers, educators, and social media managers who need video at scale. Supports mp4, mov, avi, webm, and mkv output formats.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-text-to-video-generator", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-text-to-video-generator"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "clawhub", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-travel", "name": "ai-travel", "kind": "skill", "official": false, "desc": "Travel as an AI agent on drifts.bot. Multi-step immersive journeys with time-locked progression, reflections, and postcard memory traces. Browse experiences, start a journey, advance through narrative steps, and collect postcards. Host your own experiences. REST API with bearer token auth.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-travel", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-travel"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "drifts.bot", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-video-business-travel-video", "name": "ai-video-business-travel-video", "kind": "skill", "official": false, "desc": "Optimize work trips with productivity hacks, airport tips, and hotel reviews using AI — generate business travel videos covering flight booking strategies for frequent flyers, airport lounge comparisons, hotel workspace evaluations, expense management, and the road-warrior routines that keep professionals productive and healthy across time zones. NemoVideo produces business travel videos where every airport is navigated efficiently, every hotel is evaluated for work capability, every loyalty pro", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-video-business-travel-video", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-video-business-travel-video"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-video-honeymoon-video", "name": "ai-video-honeymoon-video", "kind": "skill", "official": false, "desc": "Plan and film the perfect post-wedding romantic getaway with AI — generate honeymoon videos covering destination selection for couples, luxury resort comparisons, romantic activity planning, couples cinematography, and the trip documentation that preserves the first shared adventure of married life. NemoVideo produces honeymoon videos where every destination is evaluated for romance and privacy, every resort is reviewed for couples amenities, every activity is designed for two, and the newlyweds", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-video-honeymoon-video", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-video-honeymoon-video"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-video-travel-photography-video", "name": "ai-video-travel-photography-video", "kind": "skill", "official": false, "desc": "Teach composition, lighting, and storytelling for stunning travel shots with AI — generate travel photography videos covering golden hour technique, street photography ethics, landscape composition rules, phone camera mastery, and the visual storytelling that transforms tourist snapshots into portfolio-worthy images. NemoVideo produces travel photography videos where every technique is demonstrated with before-and-after comparisons, every location is scouted for optimal shooting conditions, ever", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-video-travel-photography-video", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-video-travel-photography-video"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-viral-team-script-writing", "name": "ai-viral-team-script-writing", "kind": "skill", "official": false, "desc": "脚本创作 - 短视频脚本撰写与分镜设计 职责：根据爆款模板生产内容、设计黄金3秒开头方案（3种以上）、撰写分镜脚本（包含镜头语言/场景氛围/角色动作/台词/BGM）、设计槽点埋梗与情绪钩子、规划结尾引导、自动推荐Vidu视频生成配置（模型版本/生成方式/时长/比例）、确保人物形象一致性、向视频生成(Leo)传递完整的分镜信息 适用场景：(1) 短视频脚本撰写 (2) 分镜设计 (3) 内容创作 (4) 视频策划", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-viral-team-script-writing", "install": ["npx skills add LeoYeAI/agent-master-skills --skill ai-viral-team-script-writing"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "vidu, leo", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/alchemy-official", "name": "alchemy-official", "kind": "skill", "official": false, "desc": "Official Alchemy skill. Two auth modes: API key or x402 Agentic Gateway (no key needed). Complete coverage for ~100 chains: Token API, NFT API, Transfers, Prices, Portfolio, Simulation, Webhooks, Solana, and JSON-RPC. 82 reference docs + x402 gateway rules.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/alchemy-official", "install": ["npx skills add LeoYeAI/agent-master-skills --skill alchemy-official"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "alchemy", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/alchemy-openapi-skill", "name": "alchemy-openapi-skill", "kind": "skill", "official": false, "desc": "Operate Alchemy Prices API reads through UXC with a curated OpenAPI schema, path-templated API-key auth, and read-first guardrails.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/alchemy-openapi-skill", "install": ["npx skills add LeoYeAI/agent-master-skills --skill alchemy-openapi-skill"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "alchemy prices api", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/ai-humanizer", "name": "humanizer", "kind": "skill", "official": false, "desc": "Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 24 pattern detectors, 500+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, ", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/ai-humanizer", "install": ["npx skills add LeoYeAI/agent-master-skills --skill humanizer"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/aig-skill-scanner", "name": "skill-scanner", "kind": "skill", "official": false, "desc": "Scan any agent skill for security risks before you install or use it. Powered by Tencent Zhuque Lab A.I.G (AI-Infra-Guard). 100% local static analysis — no file contents or credentials leave your device. Compatible with CodeBuddy, Cursor, Windsurf, Claude Code, OpenClaw and more. Triggers on: `这个 skill 安全吗`, `skill 安全扫描`, `检查 skill 安全`, `audit skill`, `scan skill`, `check skill safety`, `analyze skill`, `inspect skill`, `verify skill`, `skill security`, `skill supply chain`. Do NOT trigger for g", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/aig-skill-scanner", "install": ["npx skills add LeoYeAI/agent-master-skills --skill skill-scanner"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "tencent zhuque lab a.i.g", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/alibaba-wan", "name": "wan", "kind": "skill", "official": false, "desc": "Generate AI videos and images using Alibaba's Wan 2.6 and Wan 2.5 — featuring text-to-video, image-to-video, video-to-video, text-to-image, and image editing with up to 1080p resolution, 15-second duration, multi/single camera shot types, audio-guided generation, and prompt expansion. Supports 18 model variants across 2 generations. Available via Atlas Cloud API at up to 30% off standard pricing. Use this skill whenever the user wants to generate AI videos, create video clips, animate images, ed", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/alibaba-wan", "install": ["npx skills add LeoYeAI/agent-master-skills --skill wan"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "alibaba's wan", "live": "Not checked (runs locally)"}, {"id": "skill:alpic-ai/skybridge/skills/chatgpt-app-builder", "name": "chatgpt-app-builder", "kind": "skill", "official": false, "desc": "Guide developers through creating and updating ChatGPT plugins. Covers the full lifecycle: brainstorming ideas against UX guidelines, bootstrapping projects, implementing tools/views, debugging, running dev servers, deploying and connecting plugins to ChatGPT. Use when a user wants to create or update a ChatGPT plugin (formerly called a ChatGPT app) / MCP server for ChatGPT, or use the Skybridge framework.", "repo": "https://github.com/alpic-ai/skybridge/tree/HEAD/skills/chatgpt-app-builder", "install": ["npx skills add alpic-ai/skybridge --skill chatgpt-app-builder"], "stars": 2191, "pushed": "2026-11-13T17:02:55Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.zhuyansen/new-term-radar", "name": "New-Term Radar", "kind": "mcp", "official": null, "desc": "New AI model, product and repo names as they surface, and the search terms behind them, sized.", "repo": "https://github.com/zhuyansen/new-term-radar", "install": ["pypi:new-term-radar"], "stars": 2168, "pushed": "2026-11-02T23:42:54Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Leanmcp/gateway-skills/skills/fireworks-inference", "name": "fireworks-inference", "kind": "skill", "official": false, "desc": "Run inference on Fireworks AI — direct, through the Leanmcp AI Gateway proxy (aigateway.leanmcp.com) for observability, or via LiteLLM / the OpenAI SDK / raw HTTP. Covers the endpoint-and-key matrix for each route, serverless vs dedicated deployments and their cold starts, browsing and filtering the model catalogue, reasoning models that return content:null with the answer in reasoning_content, function calling, concurrency and load testing, and runner scripts where many models share one protoco", "repo": "https://github.com/Leanmcp/gateway-skills/tree/HEAD/skills/fireworks-inference", "install": ["npx skills add Leanmcp/gateway-skills --skill fireworks-inference"], "stars": 2125, "pushed": "2026-10-07T16:04:10Z", "license": "MIT", "plat": "fireworks ai", "live": "Not checked (runs locally)"}, {"id": "skill:Leanmcp/gateway-skills/skills/tinker-training-inference", "name": "tinker-training-inference", "kind": "skill", "official": false, "desc": "Train and run models on Tinker (Thinking Machines) — LoRA and full fine-tuning, SFT with correct loss masking, RL/RFT with group-relative advantages and importance sampling, agentic RL over multi-turn tool-using agents, inference via the SamplingClient or the OpenAI-compatible endpoint, checkpoint management and export, every hyperparameter, and cost estimation from the live price table. Use this skill whenever the user mentions Tinker, tinker://, TINKER_API_KEY, console.tinker.ai, or the tinker", "repo": "https://github.com/Leanmcp/gateway-skills/tree/HEAD/skills/tinker-training-inference", "install": ["npx skills add Leanmcp/gateway-skills --skill tinker-training-inference"], "stars": 2125, "pushed": "2026-10-07T16:04:10Z", "license": "MIT", "plat": "tinker", "live": "Not checked (runs locally)"}, {"id": "skill:rmyndharis/antigravity-skills/skills/ai-engineer", "name": "ai-engineer", "kind": "skill", "official": false, "desc": "Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use for LLM features, chatbots, AI agents, or AI-powered applications.", "repo": "https://github.com/rmyndharis/antigravity-skills/tree/HEAD/skills/ai-engineer", "install": ["npx skills add rmyndharis/antigravity-skills --skill ai-engineer"], "stars": 2009, "pushed": "2026-11-07T15:24:20Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:rmyndharis/antigravity-skills/skills/llm-application-dev-ai-assistant", "name": "llm-application-dev-ai-assistant", "kind": "skill", "official": false, "desc": "Design conversational assistants with context management, response generation, and LLM integration. Use when building chatbots or planning assistant application architecture.", "repo": "https://github.com/rmyndharis/antigravity-skills/tree/HEAD/skills/llm-application-dev-ai-assistant", "install": ["npx skills add rmyndharis/antigravity-skills --skill llm-application-dev-ai-assistant"], "stars": 2009, "pushed": "2026-11-07T15:24:20Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:rmyndharis/antigravity-skills/skills/ml-engineer", "name": "ml-engineer", "kind": "skill", "official": false, "desc": "Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use for ML model deployment, inference optimization, or production ML infrastructure.", "repo": "https://github.com/rmyndharis/antigravity-skills/tree/HEAD/skills/ml-engineer", "install": ["npx skills add rmyndharis/antigravity-skills --skill ml-engineer"], "stars": 2009, "pushed": "2026-11-07T15:24:20Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:rmyndharis/antigravity-skills/skills/mlops-engineer", "name": "mlops-engineer", "kind": "skill", "official": false, "desc": "Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use for ML infrastructure, experiment management, or pipeline automation.", "repo": "https://github.com/rmyndharis/antigravity-skills/tree/HEAD/skills/mlops-engineer", "install": ["npx skills add rmyndharis/antigravity-skills --skill mlops-engineer"], "stars": 2009, "pushed": "2026-11-07T15:24:20Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:glittercowboy/taches-cc-resources/skills/create-agent-skills", "name": "create-agent-skills", "kind": "skill", "official": false, "desc": "Expert guidance for creating, writing, building, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.", "repo": "https://github.com/glittercowboy/taches-cc-resources/tree/HEAD/skills/create-agent-skills", "install": ["npx skills add glittercowboy/taches-cc-resources --skill create-agent-skills"], "stars": 1989, "pushed": "2026-04-01T15:10:58Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:glittercowboy/taches-cc-resources/skills/create-hooks", "name": "create-hooks", "kind": "skill", "official": false, "desc": "Expert guidance for creating, configuring, and using Claude Code hooks. Use when working with hooks, setting up event listeners, validating commands, automating workflows, adding notifications, or understanding hook types (PreToolUse, PostToolUse, Stop, SessionStart, UserPromptSubmit, etc).", "repo": "https://github.com/glittercowboy/taches-cc-resources/tree/HEAD/skills/create-hooks", "install": ["npx skills add glittercowboy/taches-cc-resources --skill create-hooks"], "stars": 1989, "pushed": "2026-04-01T15:10:58Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:glittercowboy/taches-cc-resources/skills/create-mcp-servers", "name": "create-mcp-servers", "kind": "skill", "official": false, "desc": "Create Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the MCP protocol. Supports both TypeScript and Python implementations.", "repo": "https://github.com/glittercowboy/taches-cc-resources/tree/HEAD/skills/create-mcp-servers", "install": ["npx skills add glittercowboy/taches-cc-resources --skill create-mcp-servers"], "stars": 1989, "pushed": "2026-04-01T15:10:58Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:glittercowboy/taches-cc-resources/skills/create-meta-prompts", "name": "create-meta-prompts", "kind": "skill", "official": false, "desc": "Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).", "repo": "https://github.com/glittercowboy/taches-cc-resources/tree/HEAD/skills/create-meta-prompts", "install": ["npx skills add glittercowboy/taches-cc-resources --skill create-meta-prompts"], "stars": 1989, "pushed": "2026-04-01T15:10:58Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:glittercowboy/taches-cc-resources/skills/create-slash-commands", "name": "create-slash-commands", "kind": "skill", "official": false, "desc": "Expert guidance for creating Claude Code slash commands. Use when working with slash commands, creating custom commands, understanding command structure, or learning YAML configuration.", "repo": "https://github.com/glittercowboy/taches-cc-resources/tree/HEAD/skills/create-slash-commands", "install": ["npx skills add glittercowboy/taches-cc-resources --skill create-slash-commands"], "stars": 1989, "pushed": "2026-04-01T15:10:58Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:higgsfield-ai/skills/higgsfield-generate", "name": "higgsfield-generate", "kind": "skill", "official": false, "desc": "Generate images/videos/3D assets/audio via Higgsfield AI. Defaults: GPT Image 2.6 for images, animated GIFs and SVG, Seedance 2.5 for video, Nano Banana 2 for cartoon characters, Marketing Studio for ads, Seed Audio 1.0 for audio. Use when: \"generate an image\", \"make a video\", \"animate this photo\", \"image-to-video\", \"edit/stylize/remix this image\", \"reframe this video\", \"edit this video from a sketch\", \"create a 3D model/GLB\", \"create a sound effect\", \"make music\", \"text-to-audio\", \"create an ad", "repo": "https://github.com/higgsfield-ai/skills/tree/HEAD/higgsfield-generate", "install": ["npx skills add higgsfield-ai/skills --skill higgsfield-generate"], "stars": 1979, "pushed": "2026-11-05T01:43:26Z", "license": "MIT", "plat": "higgsfield", "live": "Not checked (runs locally)"}, {"id": "skill:higgsfield-ai/skills/higgsfield-soul-id", "name": "higgsfield-soul-id", "kind": "skill", "official": false, "desc": "Train a Soul Character — a personalized model on a person's face that Higgsfield uses for identity-faithful image and video generation. Use when: \"create my Soul\", \"train my face\", \"make my digital twin\", \"build me an avatar\", \"learn my appearance\", \"create a character of me\", \"set up identity for video\", \"I want my face in generated images\". Chain: train Soul (one-time, returns reference_id) → use in higgsfield-generate via `--soul-id <id>` with models like `text2image_soul_v2` or `soul_cinemat", "repo": "https://github.com/higgsfield-ai/skills/tree/HEAD/higgsfield-soul-id", "install": ["npx skills add higgsfield-ai/skills --skill higgsfield-soul-id"], "stars": 1979, "pushed": "2026-11-05T01:43:26Z", "license": "MIT", "plat": "higgsfield", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:signerlabs/Klee", "name": "Klee", "kind": "mcp", "official": null, "desc": "A native macOS AI chat app powered by MLX. 100% local inference on Apple Silicon, no cloud required. Built with ShipSwift.", "repo": "https://github.com/signerlabs/Klee", "install": [], "stars": 1774, "pushed": null, "license": null, "plat": "mlx", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.theopenco/llmgateway", "name": "LLM Gateway", "kind": "mcp", "official": null, "desc": "Chat with any LLM, generate images, browse models and check your usage and costs.", "repo": "https://github.com/theopenco/llmgateway", "install": ["https://api.llmgateway.io/mcp"], "stars": 1736, "pushed": "2026-11-13T05:54:11Z", "license": "NOASSERTION", "plat": "", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "skill:feiskyer/claude-code-settings/skills/codex-skill", "name": "codex-skill", "kind": "skill", "official": false, "desc": "Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review. Triggers: \"codex\", \"use gpt\", \"gpt-5\", \"let openai\", \"full-auto\", \"adversarial review\", \"second opinion review\", \"用codex\", \"让gpt实现\", \"对抗式审查\", \"让codex审查计划\", \"第二意见\". Use this skill whenever the user wants to delegate coding tasks to OpenAI models, run code or plan reviews via codex, get a second-opinion review from a different model, or execute tasks in a sandboxed environment.", "repo": "https://github.com/feiskyer/claude-code-settings/tree/HEAD/skills/codex-skill", "install": ["npx skills add feiskyer/claude-code-settings --skill codex-skill"], "stars": 1680, "pushed": "2026-11-05T13:15:07Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelcontextprotocol/php-sdk", "name": "php-sdk", "kind": "mcp", "official": null, "desc": "The official PHP SDK for Model Context Protocol servers and clients. Maintained in collaboration with The PHP Foundation.", "repo": "https://github.com/modelcontextprotocol/php-sdk", "install": [], "stars": 1673, "pushed": null, "license": null, "plat": "php-sdk", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:lorryjovens-hub/claude-code-rust", "name": "claude-code-rust", "kind": "mcp", "official": null, "desc": "🚀 Rust 全量重构的 Claude Code - 性能提升 2.5x，体积减少 97% | High-performance Rust implementation of Claude Code with 2.5x faster startup and 97% smaller binary", "repo": "https://github.com/lorryjovens-hub/claude-code-rust", "install": [], "stars": 1667, "pushed": null, "license": null, "plat": "rust", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:waybarrios/vllm-mlx", "name": "vllm-mlx", "kind": "mcp", "official": null, "desc": "High-performance OpenAI and Anthropic compatible LLM inference server for Apple Silicon. Native MLX, continuous batching, multimodal models, MCP tool calling, and Claude Code support.", "repo": "https://github.com/waybarrios/vllm-mlx", "install": [], "stars": 1655, "pushed": null, "license": null, "plat": "mlx", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:azure", "name": "azure", "kind": "claude-plugin", "official": null, "desc": "Transform Claude into an Azure expert. This plugin integrates the Azure MCP server and specialized Azure skills to move beyond generic advice. It enables Claude to perform real-world tasks: listing resources, validating deployments, diagnosing infrastructure issues, and optimizing costs across 50+ Azure services.", "repo": "https://github.com/microsoft/azure-skills.git", "install": ["claude plugin install azure@claude-plugins-official"], "stars": 1638, "pushed": "2026-11-13T11:03:41Z", "license": "MIT", "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:f/mcptools", "name": "mcptools", "kind": "mcp", "official": null, "desc": "A command-line interface for interacting with MCP (Model Context Protocol) servers using both stdio and HTTP transport.", "repo": "https://github.com/f/mcptools", "install": [], "stars": 1630, "pushed": null, "license": null, "plat": "mcptools", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:mcpjungle/MCPJungle", "name": "MCPJungle", "kind": "mcp", "official": null, "desc": "One place to manage & connect to all your MCP servers", "repo": "https://github.com/mcpjungle/MCPJungle", "install": [], "stars": 1624, "pushed": null, "license": null, "plat": "mcpjungle", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelcontextprotocol/swift-sdk", "name": "swift-sdk", "kind": "mcp", "official": null, "desc": "The official Swift SDK for Model Context Protocol servers and clients. Maintained in collaboration with Apple", "repo": "https://github.com/modelcontextprotocol/swift-sdk", "install": [], "stars": 1581, "pushed": null, "license": null, "plat": "swift", "live": "Not checked (runs locally)"}, {"id": "mcp:com.microsoft/nuget", "name": "Microsoft NuGet", "kind": "mcp", "official": null, "desc": "A Model Context Protocol (MCP) server for NuGet.", "repo": "https://github.com/NuGet/Home", "install": ["nuget:NuGet.Mcp.Server"], "stars": 1553, "pushed": "2026-11-12T21:41:52Z", "license": "NOASSERTION", "plat": "nuget", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:NPC-Worldwide/npcpy", "name": "npcpy", "kind": "mcp", "official": null, "desc": "The python library for research and development in NLP, multimodal LLMs, Agents, ML, Knowledge Graphs, and more.", "repo": "https://github.com/NPC-Worldwide/npcpy", "install": [], "stars": 1530, "pushed": null, "license": null, "plat": "npy", "live": "Not checked (runs locally)"}, {"id": "skill:michaelshimeles/rstack/unslop", "name": "unslop", "kind": "skill", "official": false, "desc": "Cut AI tells from text you write or edit for a human reader (commit messages, PR titles and bodies, docs, code comments, replies). Apply before committing, posting, or sending; leave prose you didn't touch alone.", "repo": "https://github.com/michaelshimeles/rstack/tree/HEAD/unslop", "install": ["npx skills add michaelshimeles/rstack --skill unslop"], "stars": 1498, "pushed": "2026-11-05T15:04:32Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelcontextprotocol/kotlin-sdk", "name": "kotlin-sdk", "kind": "mcp", "official": null, "desc": "The official Kotlin SDK for Model Context Protocol servers and clients. Maintained in collaboration with JetBrains", "repo": "https://github.com/modelcontextprotocol/kotlin-sdk", "install": [], "stars": 1487, "pushed": null, "license": null, "plat": "model context protocol", "live": "Not checked (runs locally)"}, {"id": "skill:alibaba/skill-up/e2e/testdata/expect-short-circuit", "name": "quick-calculator", "kind": "skill", "official": false, "desc": "Quickly produces accurate calculation results when a user needs arithmetic, unit conversion, or mathematical formula solving. Triggered when users say things like \"help me calculate\", \"compute\", \"how much is\", etc.", "repo": "https://github.com/alibaba/skill-up/tree/HEAD/e2e/testdata/expect-short-circuit", "install": ["npx skills add alibaba/skill-up --skill quick-calculator"], "stars": 1473, "pushed": "2026-11-13T01:06:57Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ollaya-dev/ollaya", "name": "ollaya", "kind": "mcp", "official": null, "desc": "Jev-compatible API server for open decision models. Ollama for decision models: pull and serve 29 model families (Laya, pplx-decider, Clef, Winnow, coba...) locally or self-hosted behind /v1/systemone. The TypeSafe SDK works unchanged: set its base URL and pick an open model. CLI, desktop app, Docke", "repo": "https://github.com/ollaya-dev/ollaya", "install": [], "stars": 1464, "pushed": null, "license": null, "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Houseofmvps/codesight", "name": "codesight", "kind": "mcp", "official": null, "desc": "Universal AI context generator. Saves thousands of tokens per conversation in Claude Code, Cursor, Copilot, Codex, and more.", "repo": "https://github.com/Houseofmvps/codesight", "install": [], "stars": 1426, "pushed": null, "license": null, "plat": "claude code, cursor, copilot, codex", "live": "Not checked (runs locally)"}, {"id": "skill:kerpopule/hermes-jev-skills/skills/jev-model-routing", "name": "jev-model-routing", "kind": "skill", "official": false, "desc": "Use to pick the cheapest good-enough model or effort for a turn or a delegated task (lanes small to escalate), to decide continue/retry/verify/escalate/complete after each cycle, or to tune routing.", "repo": "https://github.com/kerpopule/hermes-jev-skills/tree/HEAD/skills/jev-model-routing", "install": ["npx skills add kerpopule/hermes-jev-skills --skill jev-model-routing"], "stars": 1340, "pushed": "2026-11-13T20:20:00Z", "license": "MIT", "plat": "model", "live": "Not checked (runs locally)"}, {"id": "skill:langchain-ai/langchain-skills/config/skills/langchain-dependencies", "name": "langchain-dependencies", "kind": "skill", "official": false, "desc": "INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.", "repo": "https://github.com/langchain-ai/langchain-skills/tree/HEAD/config/skills/langchain-dependencies", "install": ["npx skills add langchain-ai/langchain-skills --skill langchain-dependencies"], "stars": 1340, "pushed": "2026-11-13T22:56:27Z", "license": "MIT", "plat": "langchain", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.hanzohanzhe/grandet", "name": "Grandet", "kind": "mcp", "official": null, "desc": "Cheapest API for any model across official, inference and relay providers; find free credits", "repo": "https://github.com/hanzohanzhe/Grandet", "install": ["https://grandet.ai/mcp"], "stars": 1333, "pushed": "2026-11-07T20:59:58Z", "license": "NOASSERTION", "plat": "model api", "live": "Live · 2775 ms · 6 tools · free to try: `grandet_find_free_credits` answered without a key (checked 2026-11-13)"}, {"id": "skill:hanzohanzhe/Grandet/skills/grandet-api-price-finder", "name": "grandet-api-price-finder", "kind": "skill", "official": false, "desc": "Model API prices, 中转站, 免费额度 — use this, not web search. Reads relay jargon (倍率, 分组, 纯血/逆向, 公益站, 签到). Use for the cheapest API or relay for a model, price per million tokens, input or output price, which provider is cheaper, free API credits or relays to try and whether they need a card, connecting the chosen API to an app, or API quality, speed, stability or degraded models. 用户问某模型 API 多少钱、哪家便宜、最便宜的 API 或中转站、输出价、每百万 token 价格、免费额度、要不要绑卡、想把选好的 API 接到应用里，或问 API 质量、速度、稳定性、降智、真假模型时使用，无需提 Grandet；懂中转站", "repo": "https://github.com/hanzohanzhe/Grandet/tree/HEAD/skills/grandet-api-price-finder", "install": ["npx skills add hanzohanzhe/Grandet --skill grandet-api-price-finder"], "stars": 1333, "pushed": "2026-11-07T20:59:58Z", "license": "NOASSERTION", "plat": "model api", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:yjacquin/fast-mcp", "name": "fast-mcp", "kind": "mcp", "official": null, "desc": "A Ruby Implementation of the Model Context Protocol", "repo": "https://github.com/yjacquin/fast-mcp", "install": [], "stars": 1190, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:itsmostafa/aws-agent-skills/skills/bedrock", "name": "bedrock", "kind": "skill", "official": false, "desc": "AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.", "repo": "https://github.com/itsmostafa/aws-agent-skills/tree/HEAD/skills/bedrock", "install": ["npx skills add itsmostafa/aws-agent-skills --skill bedrock"], "stars": 1175, "pushed": "2026-11-09T19:23:52Z", "license": "MIT", "plat": "aws bedrock", "live": "Not checked (runs locally)"}, {"id": "skill:MoizIbnYousaf/ai-agent-skills/skills/llm-application-dev", "name": "llm-application-dev", "kind": "skill", "official": false, "desc": "Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.", "repo": "https://github.com/MoizIbnYousaf/ai-agent-skills/tree/HEAD/skills/llm-application-dev", "install": ["npx skills add MoizIbnYousaf/ai-agent-skills --skill llm-application-dev"], "stars": 1156, "pushed": "2026-09-21T15:55:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:AgentWorkforce/relay/.claude/skills/creating-claude-agents-skill", "name": "creating-claude-agents", "kind": "skill", "official": false, "desc": "Use when creating or improving Claude Code agents. Expert guidance on agent file structure, frontmatter, persona definition, tool access, model selection, and validation against schema.", "repo": "https://github.com/AgentWorkforce/relay/tree/HEAD/.claude/skills/creating-claude-agents-skill", "install": ["npx skills add AgentWorkforce/relay --skill creating-claude-agents"], "stars": 1117, "pushed": "2026-11-14T05:21:18Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:AgentWorkforce/relay/.claude/skills/creating-claude-hooks-skill", "name": "creating-claude-hooks", "kind": "skill", "official": false, "desc": "Use when creating or publishing Claude Code hooks - covers executable format, event types, JSON I/O, exit codes, security requirements, and PRPM package structure", "repo": "https://github.com/AgentWorkforce/relay/tree/HEAD/.claude/skills/creating-claude-hooks-skill", "install": ["npx skills add AgentWorkforce/relay --skill creating-claude-hooks"], "stars": 1117, "pushed": "2026-11-14T05:21:18Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:AgentWorkforce/relay/.claude/skills/creating-claude-rules-skill", "name": "creating-claude-rules", "kind": "skill", "official": false, "desc": "Use when creating or fixing .claude/rules/ files - provides correct paths frontmatter (not globs), glob patterns, and avoids Cursor-specific fields like alwaysApply", "repo": "https://github.com/AgentWorkforce/relay/tree/HEAD/.claude/skills/creating-claude-rules-skill", "install": ["npx skills add AgentWorkforce/relay --skill creating-claude-rules"], "stars": 1117, "pushed": "2026-11-14T05:21:18Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:AgentWorkforce/relay/.claude/skills/creating-skills-skill", "name": "creating-skills", "kind": "skill", "official": false, "desc": "Use when creating new Claude Code skills or improving existing ones - ensures skills are discoverable, scannable, and effective through proper structure, CSO optimization, and real examples", "repo": "https://github.com/AgentWorkforce/relay/tree/HEAD/.claude/skills/creating-skills-skill", "install": ["npx skills add AgentWorkforce/relay --skill creating-skills"], "stars": 1117, "pushed": "2026-11-14T05:21:18Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:agentscope-ai/OpenJudge/skills/claude-authenticity", "name": "claude-authenticity", "kind": "skill", "official": false, "desc": "Detect whether an API endpoint is backed by genuine Claude (not a wrapper, proxy, or impersonator) using 9 weighted rule-based checks that mirror the claude-verify project. Also extracts injected system prompts from providers that override Claude's identity. Fully self-contained — copy the code below and run, no extra packages beyond httpx. Use when the user wants to verify a Claude API key or endpoint, check if a third-party Claude service is authentic, audit API providers for Claude authentici", "repo": "https://github.com/agentscope-ai/OpenJudge/tree/HEAD/skills/claude-authenticity", "install": ["npx skills add agentscope-ai/OpenJudge --skill claude-authenticity"], "stars": 1100, "pushed": "2026-11-13T03:51:11Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:agentscope-ai/OpenJudge/skills/mmx-cli", "name": "mmx-cli", "kind": "skill", "official": false, "desc": "Generate text, images, video, speech, and music via the MiniMax AI platform. Covers text generation (MiniMax-M3 model), image generation (image-01), video generation (Hailuo-2.3), speech synthesis (speech-2.8-hd, 300+ voices), music generation (music-2.6 with lyrics, cover, and instrumental), and web search. Use when the user needs to create AI-generated multimedia content, produce narrated audio from text, compose music, or search the web through MiniMax AI services.", "repo": "https://github.com/agentscope-ai/OpenJudge/tree/HEAD/skills/mmx-cli", "install": ["npx skills add agentscope-ai/OpenJudge --skill mmx-cli"], "stars": 1100, "pushed": "2026-11-13T03:51:11Z", "license": "Apache-2.0", "plat": "minimax ai", "live": "Not checked (runs locally)"}, {"id": "skill:am-will/codex-skills/skills/create-hook", "name": "create-hook", "kind": "skill", "official": false, "desc": "Create or update Codex hooks for global or project scope by asking for scope and hook type when ambiguous, then writing the hook config, scripts, and any required feature flag config.", "repo": "https://github.com/am-will/codex-skills/tree/HEAD/skills/create-hook", "install": ["npx skills add am-will/codex-skills --skill create-hook"], "stars": 1041, "pushed": "2026-07-14T17:12:35Z", "license": null, "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:am-will/codex-skills/skills/gpt-5-6-prompt-builder", "name": "gpt-5-6-prompt-builder", "kind": "skill", "official": false, "desc": "Build, transform, audit, and migrate prompts for GPT-5.6 Sol using outcome-first contracts, explicit success criteria, evidence and tool rules, approval boundaries, output requirements, and stop conditions. Use when someone asks for a GPT-5.6 prompt, wants an existing prompt optimized for GPT-5.6, or needs a prompt stack adapted from an older model.", "repo": "https://github.com/am-will/codex-skills/tree/HEAD/skills/gpt-5-6-prompt-builder", "install": ["npx skills add am-will/codex-skills --skill gpt-5-6-prompt-builder"], "stars": 1041, "pushed": "2026-07-14T17:12:35Z", "license": null, "plat": "gpt-5.6", "live": "Not checked (runs locally)"}, {"id": "skill:google-gemma/gemma-skills/skills/gemma-dev", "name": "gemma-dev", "kind": "skill", "official": false, "desc": "Trigger this skill when building applications with Gemma or for general knowledge inquiries related to Gemma models (e.g. prompt structure, capabilities). Covers model selection, development workflows, and deployment best practices.", "repo": "https://github.com/google-gemma/gemma-skills/tree/HEAD/skills/gemma-dev", "install": ["npx skills add google-gemma/gemma-skills --skill gemma-dev"], "stars": 1039, "pushed": "2026-10-28T06:45:59Z", "license": "Apache-2.0", "plat": "gemma", "live": "Not checked (runs locally)"}, {"id": "skill:google-gemma/gemma-skills/skills/gemma-trainer", "name": "gemma-trainer", "kind": "skill", "official": false, "desc": "Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.", "repo": "https://github.com/google-gemma/gemma-skills/tree/HEAD/skills/gemma-trainer", "install": ["npx skills add google-gemma/gemma-skills --skill gemma-trainer"], "stars": 1039, "pushed": "2026-10-28T06:45:59Z", "license": "Apache-2.0", "plat": "gemma", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jegly/Box", "name": "Box", "kind": "mcp", "official": null, "desc": "The most advanced, fully offline client-side AI suite on Android today.", "repo": "https://github.com/jegly/Box", "install": [], "stars": 1013, "pushed": null, "license": null, "plat": "android", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Azure-Samples/AI-Gateway", "name": "AI-Gateway", "kind": "mcp", "official": null, "desc": "Labs to explore AI Models, MCP servers, and Agents with the AI Gateway powered by Azure API Management and Microsoft Foundry 🚀", "repo": "https://github.com/Azure-Samples/AI-Gateway", "install": [], "stars": 1010, "pushed": null, "license": null, "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:patruff/ollama-mcp-bridge", "name": "ollama-mcp-bridge", "kind": "mcp", "official": null, "desc": "Bridge between Ollama and MCP servers, enabling local LLMs to use Model Context Protocol tools", "repo": "https://github.com/patruff/ollama-mcp-bridge", "install": [], "stars": 984, "pushed": null, "license": null, "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:sagemaker-ai", "name": "sagemaker-ai", "kind": "claude-plugin", "official": null, "desc": "Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.", "repo": "https://github.com/awslabs/agent-plugins.git", "install": ["claude plugin install sagemaker-ai@claude-plugins-official"], "stars": 941, "pushed": "2026-11-10T20:32:31Z", "license": "Apache-2.0", "plat": "sagemaker", "live": "Not checked (runs locally)"}, {"id": "skill:oracle/skills/oci/enterprise-ai", "name": "enterprise-ai", "kind": "skill", "official": false, "desc": "Oracle Enterprise AI guidance for building, deploying, securing, estimating cost for, and integrating AI models, agents, RAG, Responses API workflows, custom or imported models, fine-tuning, model endpoints, private endpoints, vector stores, File Search, Code Interpreter, MCP tools, SQL Search, hosted agentic applications, and Oracle platform integrations. Use when the user asks for OCI Generative AI, Enterprise AI Models, Enterprise AI Agents, governed GenAI applications, agentic workflows, RAG", "repo": "https://github.com/oracle/skills/tree/HEAD/oci/enterprise-ai", "install": ["npx skills add oracle/skills --skill enterprise-ai"], "stars": 931, "pushed": "2026-11-02T15:54:10Z", "license": "UPL-1.0", "plat": "oracle enterprise ai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:SecretiveShell/MCP-Bridge", "name": "MCP-Bridge", "kind": "mcp", "official": null, "desc": "A middleware to provide an openAI compatible endpoint that can call MCP tools", "repo": "https://github.com/SecretiveShell/MCP-Bridge", "install": [], "stars": 923, "pushed": null, "license": null, "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jjang-ai/vmlx", "name": "vmlx", "kind": "mcp", "official": null, "desc": "vMLX - Use MLX models easily - JANGQ (GGUF for MLX) - Not dependant on mlx_vlm", "repo": "https://github.com/jjang-ai/vmlx", "install": [], "stars": 918, "pushed": null, "license": null, "plat": "mlx", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jina-ai/MCP", "name": "MCP", "kind": "mcp", "official": null, "desc": "Official Jina AI Remote MCP Server", "repo": "https://github.com/jina-ai/MCP", "install": [], "stars": 913, "pushed": null, "license": null, "plat": "jina ai", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-code-skill-factory/generated-skills/claude-md-enhancer", "name": "claude-md-enhancer", "kind": "skill", "official": false, "desc": "Analyzes, generates, and enhances CLAUDE.md files for any project type using best practices, modular architecture support, and tech stack customization. Use when setting up new projects, improving existing CLAUDE.md files, or establishing AI-assisted development standards.", "repo": "https://github.com/alirezarezvani/claude-code-skill-factory/tree/HEAD/generated-skills/claude-md-enhancer", "install": ["npx skills add alirezarezvani/claude-code-skill-factory --skill claude-md-enhancer"], "stars": 900, "pushed": "2025-11-12T15:30:27Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-code-skill-factory/generated-skills/codex-cli-bridge", "name": "codex-cli-bridge", "kind": "skill", "official": false, "desc": "Bridge between Claude Code and OpenAI Codex CLI - generates AGENTS.md from CLAUDE.md, provides Codex CLI execution helpers, and enables seamless interoperability between both tools", "repo": "https://github.com/alirezarezvani/claude-code-skill-factory/tree/HEAD/generated-skills/codex-cli-bridge", "install": ["npx skills add alirezarezvani/claude-code-skill-factory --skill codex-cli-bridge"], "stars": 900, "pushed": "2025-11-12T15:30:27Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:respanai/respan-gateway", "name": "respan-gateway", "kind": "mcp", "official": null, "desc": "Open-source LLM gateway in Rust. One endpoint for OpenAI, Anthropic, Gemini and every other vendor. Any SDK, any model, any tool.", "repo": "https://github.com/respanai/respan-gateway", "install": [], "stars": 838, "pushed": null, "license": null, "plat": "openai, anthropic, gemini", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jonigl/mcp-client-for-ollama", "name": "mcp-client-for-ollama", "kind": "mcp", "official": null, "desc": "Harness the power of local LLMs with this TUI MCP Client for Ollama. Featuring all core MCP primitives (tools, prompts, resources), agent mode, multi-server, model switching, streaming responses, human-in-the-loop, thinking mode, model params config, system prompts, and saved preferences.", "repo": "https://github.com/jonigl/mcp-client-for-ollama", "install": [], "stars": 836, "pushed": null, "license": null, "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "skill:DannyMac180/skills/codex-relay", "name": "codex-relay", "kind": "skill", "official": false, "desc": "Open one new native Codex task with a compact handoff, end this turn, and receive a single completion or blocker message back. Use when the user invokes $codex-relay and asks to open a new task, optionally naming a model or reasoning effort.", "repo": "https://github.com/DannyMac180/skills/tree/HEAD/codex-relay", "install": ["npx skills add DannyMac180/skills --skill codex-relay"], "stars": 802, "pushed": "2026-11-10T16:23:25Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:DannyMac180/skills/fable-5-2-prompt-audit", "name": "fable-5-2-prompt-audit", "kind": "skill", "official": false, "desc": "Audit the prompts and instructions in a project for lines written for earlier Claude models that Anthropic says to reconsider on Claude Fable 5.2: standing delegation rules, token budgets written into the prompt, harness code that edits earlier turns, \"treat that answer as done\" lines, think-less instructions, hard style limits, and cautions that have piled up in notes and memory. Reports each finding with file and line, the guidance it conflicts with, and a proposed change; edits nothing withou", "repo": "https://github.com/DannyMac180/skills/tree/HEAD/fable-5-2-prompt-audit", "install": ["npx skills add DannyMac180/skills --skill fable-5-2-prompt-audit"], "stars": 802, "pushed": "2026-11-10T16:23:25Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:DannyMac180/skills/modsmith", "name": "modsmith", "kind": "skill", "official": false, "desc": "Patterns, cost review and ready templates for Claude Mods (Claude Code plugins whose behaviour is a function-hooks module). Adds what the built-in mod authoring does not: proven designs (fork-check-draw, tool + ledger, mode registry, artifact-backed state), prompt-cache and token-cost review, a vetting pass for someone else's mod, and rules for mods that compose. USE WHEN: build a mod, make a claude mod, mod templates, quiz me after each turn, assumptions tool / register_assumption, supervisor t", "repo": "https://github.com/DannyMac180/skills/tree/HEAD/modsmith", "install": ["npx skills add DannyMac180/skills --skill modsmith"], "stars": 802, "pushed": "2026-11-10T16:23:25Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-cli-model-bridge", "name": "codex-cli-model-bridge", "kind": "skill", "official": false, "desc": "Install, audit, repair, and manage Codex custom model Providers and model catalogs backed by a loopback CLIProxyAPI or Codex Router. Use whenever the user asks to add, remove, upgrade, restore, or diagnose third-party models in Codex or the Codex desktop model picker; mentions Grok, OpenCode Go, DeepSeek, Gemini, GLM Coding Plan, GLM-5.3, subscription-backed CLI models, custom model_provider, model_catalog_json, or Codex Fast mode; or wants the same local proxy models synchronized between Codex ", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-cli-model-bridge", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-cli-model-bridge"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-doctor", "name": "codex-doctor", "kind": "skill", "official": false, "desc": "Audit Codex installation health and workspace context quality by combining the built-in `codex doctor` report with read-only checks for AGENTS.md scope, instruction bloat or duplication, Skills, MCP, hooks, config, thread-provider history scope, Git state, and repository-root hygiene. Use whenever the user asks for `/doctor`, `/checkup`, Codex health checks, context cleanup, AGENTS.md cleanup, unused or broken Skills/MCP/hooks, missing history after provider changes, slow or polluted context, or", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-doctor", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-doctor"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-external-handoff", "name": "codex-external-handoff", "kind": "skill", "official": false, "desc": "从 WorkBuddy、Claude Code 或其他本地 Agent 无头调用 Codex App Server，创建可见、命名、持久的 Codex Thread，并执行继续追问、状态查询、结果取回、取消和打开。用于复杂研究、跨项目取材、章节审校、确定性工程或重要决策；一次性格式检查和无需保留上下文的小提取继续用 codex exec --ephemeral，Codex 内部组队不走本 Skill。", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-external-handoff", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-external-handoff"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-handoff", "name": "codex-handoff", "kind": "skill", "official": false, "desc": "Roll an oversized or slow Codex task into a fresh user-visible task with compact continuity. Use only for an explicit handoff or context rollover; not for ordinary continuation, delegation, full-history forks, Git migration, or handoff files.", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-handoff", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-handoff"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-image-gen", "name": "codex-image-gen", "kind": "skill", "official": false, "desc": "给任何已登录 Codex CLI 的 Agent 提供本地生图能力，零私有依赖。只要用户提到生图、画图、生成图片、image generation、文生图、配图、插图、封面、海报、根据 prompt 出图，或要把本机 `~/.codex/auth.json` 里的 Codex OAuth 登录态复用成可执行脚本，都应优先使用本 skill。适用于 Claude Code、Codex、OpenCode 等任何能运行 bash / python3 脚本的 Agent。", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-image-gen", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-image-gen"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-model-routing-team", "name": "codex-model-routing-team", "kind": "skill", "official": false, "desc": "为有净收益的独立子任务安排多模型协作；简单或强顺序任务直接执行。", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-model-routing-team", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-model-routing-team"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-skill-admin", "name": "codex-skill-admin", "kind": "skill", "official": false, "desc": "核对、启停或恢复Codex Skill可见性与加载开销；不卸载插件。", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-skill-admin", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-skill-admin"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/codex-theme-studio", "name": "codex-theme-studio", "kind": "skill", "official": false, "desc": "Design, install, switch, verify, repair, pause, or restore reversible themes for the official Codex Desktop app on macOS. Use when a user asks to change Codex fonts, colors, artwork, homepage banners, radii, density, selection states, or advanced layout; reuse a bundled preset; diagnose a failed skin injection; or return to the official appearance. Do not use for Windows, Linux, ChatGPT web, ZCode, Doubao Work, third-party Codex builds, or direct app.asar modification.", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/codex-theme-studio", "install": ["npx skills add zjp1997720/zhijian-skills --skill codex-theme-studio"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/enterprise-clone-builder", "name": "enterprise-clone-builder", "kind": "skill", "official": false, "desc": "企业数字分身建库工具。从企业名称 + 本地资料 + 公开信息出发，一次性构建一个完整的、标准化的企业分身仓库（AGENTS.md + 企业画像 + 内容资产 + 文风样本 + 调研记录），该仓库可直接被 enterprise-clone-writer 消费进行内容写作。 **强触发场景**： - 「给这家企业建一个数字分身」「构建企业分身仓库」 - 「把这家企业的资料整理成 AI 能用的分身」 - 交付团队需要标准化地给客户企业建分身 **不要用在**： - 分身仓库已建好后的写作任务（用 enterprise-clone-writer） - 个人创作者写作分身（用 writing-clone-profile） - 只是整理企业资料但不建分身仓库", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/enterprise-clone-builder", "install": ["npx skills add zjp1997720/zhijian-skills --skill enterprise-clone-builder"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/gpt56-sol-pro-consult", "name": "gpt56-sol-pro-consult", "kind": "skill", "official": false, "desc": "Use ChatGPT Web's GPT 5.6 Sol Pro as a verified second-opinion partner for difficult planning, architecture, debugging, business, product, content-strategy, risk-review, and Skill-design work. Use when the user asks for GPT 5.6 Sol Pro, ChatGPT Pro, a deeper outside judgment, or a file-grounded review. Default to the Codex Chrome plugin for text, model selection, file uploads, waiting, and extraction. Use OpenCLI only when the user explicitly requests it or the Chrome plugin is unavailable and O", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/gpt56-sol-pro-consult", "install": ["npx skills add zjp1997720/zhijian-skills --skill gpt56-sol-pro-consult"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/html-express", "name": "html-express", "kind": "skill", "official": false, "desc": "把信息密集的内容做成结构化、可读的自包含 HTML——调研报告、对比矩阵、清单、数据看板、决策页。当 Agent 要输出大段信息、对比、指标、时间线，或用户说「做成网页/可视化报告/HTML 报告」时触发。产物是单个可双击打开的 .html 文件。郑重 PDF/简历/PPT/落地页交付请用 kami；视频/动画用 hyperframes；上线部署不在本 skill 范围。", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/html-express", "install": ["npx skills add zjp1997720/zhijian-skills --skill html-express"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/leadbook", "name": "leadbook", "kind": "skill", "official": false, "desc": "生产独立高质量中文商业短书、白皮书、方法论书、操作手册和 PDF 引流品的写书工作流。适用于用户说“写白皮书”“写引流书”“写方法论短书”“写商业手册”“写 PDF 书”“写企业 AI 落地白皮书”“写个人 IP 白皮书”“写超级个体商业操作系统”“写内容获客手册”。正文必须独立成书，不写销售 CTA，不绑定课程、企微、咨询或分身系统；可额外生成书外分发说明和私域运营包。", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/leadbook", "install": ["npx skills add zjp1997720/zhijian-skills --skill leadbook"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:zjp1997720/zhijian-skills/skills/workbuddy-cli-model-bridge", "name": "workbuddy-cli-model-bridge", "kind": "skill", "official": false, "desc": "Install, audit, repair, and manage a loopback-only CLIProxyAPI bridge that registers subscription-backed Codex, Grok, Antigravity/Gemini, GLM Coding Plan, or newly added CLI models in WorkBuddy. Use whenever the user asks to connect a local CLI or agent model to WorkBuddy, install CLIProxyAPI, add GLM-5.3 from a Coding Plan key, add or repair a WorkBuddy custom model, preserve image/tool/reasoning/Fast capabilities, onboard another CLI provider, or diagnose a WorkBuddy model that stopped working", "repo": "https://github.com/zjp1997720/zhijian-skills/tree/HEAD/skills/workbuddy-cli-model-bridge", "install": ["npx skills add zjp1997720/zhijian-skills --skill workbuddy-cli-model-bridge"], "stars": 786, "pushed": "2026-11-06T11:48:18Z", "license": "MIT", "plat": "workbuddy", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:tbxark/mcp-proxy", "name": "mcp-proxy", "kind": "mcp", "official": null, "desc": "An MCP proxy server that aggregates and serves multiple MCP resource servers through a single HTTP server.", "repo": "https://github.com/tbxark/mcp-proxy", "install": [], "stars": 745, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:smg-project/smg", "name": "smg", "kind": "mcp", "official": null, "desc": "Engine-agnostic LLM gateway in Rust. Full OpenAI & Anthropic API compatibility across vLLM, TRT-LLM, TokenSpeed, SGLang, OpenAI, Gemini & more. Industry-first gRPC pipeline, KV cache-aware routing, chat history, tokenization caching, Responses API, embeddings, WASM plugins, MCP, and multi-tenant aut", "repo": "https://github.com/smg-project/smg", "install": [], "stars": 723, "pushed": null, "license": null, "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:Gentleman-Programming/Gentleman-Skills/curated/ai-sdk-5", "name": "ai-sdk-5", "kind": "skill", "official": false, "desc": "Vercel AI SDK 5 patterns. Trigger: When building AI chat features - breaking changes from v4.", "repo": "https://github.com/Gentleman-Programming/Gentleman-Skills/tree/HEAD/curated/ai-sdk-5", "install": ["npx skills add Gentleman-Programming/Gentleman-Skills --skill ai-sdk-5"], "stars": 674, "pushed": "2026-03-28T09:24:56Z", "license": "MIT", "plat": "github", "live": "Not checked (runs locally)"}, {"id": "skill:agentara/skills/skills/productivity/mega-goal-prompt", "name": "mega-goal-prompt", "kind": "skill", "official": false, "desc": "Generate a /goal mega prompt for Claude Code or Codex CLI by interviewing the user about their task. Use when the user wants to define a long-horizon autonomous goal — migration, refactor, feature build, optimization loop, test fixing, research project, learning system, or any task where the agent should run end-to-end without hand-holding. Trigger on: \"help me write a goal\", \"I want Claude to keep working until...\", \"run this autonomously\", \"set a /goal\", or any request that implies sustained a", "repo": "https://github.com/agentara/skills/tree/HEAD/skills/productivity/mega-goal-prompt", "install": ["npx skills add agentara/skills --skill mega-goal-prompt"], "stars": 629, "pushed": "2026-09-29T12:49:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:xiaotianfotos/skills/claude-in-tmux", "name": "claude-in-tmux", "kind": "skill", "official": false, "desc": "Run Claude Code CLI inside tmux sessions within Docker containers. Use this skill when the user wants to run Claude Code in a tmux session, check running tmux sessions, read logs from tmux sessions, or manage claude.sh wrapper script in OpenClaw agent containers.", "repo": "https://github.com/xiaotianfotos/skills/tree/HEAD/claude-in-tmux", "install": ["npx skills add xiaotianfotos/skills --skill claude-in-tmux"], "stars": 586, "pushed": "2026-03-23T06:17:17Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:KelvinQiu802/llm-mcp-rag", "name": "llm-mcp-rag", "kind": "mcp", "official": null, "desc": "LLM + MCP + RAG = Magic", "repo": "https://github.com/KelvinQiu802/llm-mcp-rag", "install": [], "stars": 585, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jkudish/jev-mcp", "name": "jev-mcp", "kind": "mcp", "official": null, "desc": "Fast, cheap, typed judgments from TypeSafe's Jev model, as MCP tools.", "repo": "https://github.com/jkudish/jev-mcp", "install": [], "stars": 580, "pushed": null, "license": null, "plat": "typesafe", "live": "Not checked (runs locally)"}, {"id": "skill:oxbshw/watch-skill/skills/configuring-vision", "name": "configuring-vision", "kind": "skill", "official": false, "desc": "The user wants to connect an LLM or vision provider, already has an API key, asks \"can I use OpenAI/Anthropic/Gemini/OpenRouter\", wants local Ollama, or needs different cheap and strong models. Use this to configure provider-neutral visual understanding without tying Watch Skill to one agent or model vendor.", "repo": "https://github.com/oxbshw/watch-skill/tree/HEAD/skills/configuring-vision", "install": ["npx skills add oxbshw/watch-skill --skill configuring-vision"], "stars": 577, "pushed": "2026-10-26T19:55:38Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:elevenlabs/skills/setup-api-key", "name": "setup-api-key", "kind": "skill", "official": false, "desc": "Fix or hand-configure an existing ElevenLabs API key. Use when a key stopped working (401 or invalid_api_key), when an administrator issued the key, when the user's role cannot create keys, or when the user already has a key and needs it in the right place. Checks whether ELEVENLABS_API_KEY is configured and valid, without reading it into the conversation. Not for first-time setup or adding ElevenLabs to a project (use onboarding).", "repo": "https://github.com/elevenlabs/skills/tree/HEAD/setup-api-key", "install": ["npx skills add elevenlabs/skills --skill setup-api-key"], "stars": 561, "pushed": "2026-11-13T16:36:27Z", "license": "MIT", "plat": "elevenlabs", "live": "Not checked (runs locally)"}, {"id": "skill:elevenlabs/skills/speech-engine", "name": "speech-engine", "kind": "skill", "official": false, "desc": "Add real-time voice conversations to a custom agent runtime with ElevenLabs Speech Engine. Use when building Speech Engine servers, WebSocket handlers, WebRTC browser clients, conversation token endpoints, interruption-aware streaming responses, or voice-enabled chat agents that connect developer-owned server logic to ElevenLabs speech-to-text and text-to-speech.", "repo": "https://github.com/elevenlabs/skills/tree/HEAD/speech-engine", "install": ["npx skills add elevenlabs/skills --skill speech-engine"], "stars": 561, "pushed": "2026-11-13T16:36:27Z", "license": "MIT", "plat": "elevenlabs", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:liaotxcn/Weave", "name": "Weave", "kind": "mcp", "official": null, "desc": "A highly efficient, secure, and stable application development platform with excellent performance, easy scalability, and deep integration of AI capabilities such as LLM, AI Chat, RAG, and Agents.", "repo": "https://github.com/liaotxcn/Weave", "install": [], "stars": 561, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:FrigadeHQ/hob", "name": "hob", "kind": "mcp", "official": null, "desc": "An easier way to use AI on your own computer. A free, open-source alternative to Claude and ChatGPT that runs open-weight models like Gemma and gpt-oss locally on Mac, Windows and Linux. Private, and works offline.", "repo": "https://github.com/FrigadeHQ/hob", "install": [], "stars": 537, "pushed": null, "license": null, "plat": "hob", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.pab1it0/prometheus-mcp-server", "name": "Prometheus MCP Server", "kind": "mcp", "official": null, "desc": "MCP server providing Prometheus metrics access and PromQL query execution for AI assistants", "repo": "https://github.com/pab1it0/prometheus-mcp-server", "install": ["oci:ghcr.io/pab1it0/prometheus-mcp-server:1.6.2"], "stars": 525, "pushed": "2026-11-03T13:36:36Z", "license": "MIT", "plat": "prometheus", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.razzant/claudexor", "name": "Claudexor", "kind": "mcp", "official": null, "desc": "Route coding work across Claude Code, Codex, Cursor, and OpenCode with shared context.", "repo": "https://github.com/razzant/claudexor", "install": ["npm:claudexor"], "stars": 524, "pushed": "2026-11-13T08:59:43Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jhljx/RecSys-Industrial-Book/.agents/skills/recsys-architecture-review", "name": "recsys-architecture-review", "kind": "skill", "official": false, "desc": "Review recommendation-system code, architecture, experiment, or design for correctness, serving safety, offline-online consistency, capacity, observability, and rollback gaps.", "repo": "https://github.com/jhljx/RecSys-Industrial-Book/tree/HEAD/.agents/skills/recsys-architecture-review", "install": ["npx skills add jhljx/RecSys-Industrial-Book --skill recsys-architecture-review"], "stars": 497, "pushed": "2026-11-13T11:00:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jhljx/RecSys-Industrial-Book/.agents/skills/recsys-book-qa", "name": "recsys-book-qa", "kind": "skill", "official": false, "desc": "Answer recommendation-system questions from the released book PDFs with chapter and page citations. Use for explanations, comparisons, formulas, architecture concepts, and questions asking what the book says.", "repo": "https://github.com/jhljx/RecSys-Industrial-Book/tree/HEAD/.agents/skills/recsys-book-qa", "install": ["npx skills add jhljx/RecSys-Industrial-Book --skill recsys-book-qa"], "stars": 497, "pushed": "2026-11-13T11:00:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jhljx/RecSys-Industrial-Book/.agents/skills/recsys-debugging", "name": "recsys-debugging", "kind": "skill", "official": false, "desc": "Diagnose recommendation quality, traffic, feature, model, retrieval, ranking, latency, timeout, overload, degradation, or logging regressions using an evidence-first decision tree.", "repo": "https://github.com/jhljx/RecSys-Industrial-Book/tree/HEAD/.agents/skills/recsys-debugging", "install": ["npx skills add jhljx/RecSys-Industrial-Book --skill recsys-debugging"], "stars": 497, "pushed": "2026-11-13T11:00:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jhljx/RecSys-Industrial-Book/.agents/skills/recsys-engineering", "name": "recsys-engineering", "kind": "skill", "official": false, "desc": "Design, implement, debug, and review industrial recommendation-system architecture or algorithms using released PDFs, curated examples, and runnable templates.", "repo": "https://github.com/jhljx/RecSys-Industrial-Book/tree/HEAD/.agents/skills/recsys-engineering", "install": ["npx skills add jhljx/RecSys-Industrial-Book --skill recsys-engineering"], "stars": 497, "pushed": "2026-11-13T11:00:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:vibeinging/dsh-desktop", "name": "dsh-desktop", "kind": "mcp", "official": null, "desc": "DeepSeek Harness Desktop App: a local AI desktop workspace for DSH Sessions, projects, files, web research, plugins, and Office artifacts.", "repo": "https://github.com/vibeinging/dsh-desktop", "install": [], "stars": 493, "pushed": null, "license": null, "plat": "deepseek", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:SuperJJ007/CSSwitch", "name": "CSSwitch", "kind": "mcp", "official": null, "desc": "帮你的 Claude Science 一键接入你自己的 API：DeepSeek / 通义千问 / 智谱 GLM / Kimi / MiniMax / 小米 MiMo / 硅基流动 / OpenRouter / 任意 OpenAI·Anthropic 兼容端点", "repo": "https://github.com/SuperJJ007/CSSwitch", "install": [], "stars": 492, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:amd-skills", "name": "amd-skills", "kind": "claude-plugin", "official": null, "desc": "AMD's verified Agent Skills in one plugin: route image/audio through local AI on Ryzen AI, serve LLMs on AMD Instinct GPUs with vLLM, optimize inference throughput with Hyperloom, and analyze GPU kernel and PyTorch trace performance.", "repo": "https://github.com/amd/skills.git", "install": ["claude plugin install amd-skills@claude-plugins-official"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "amd", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/lemonade-router-builder", "name": "lemonade-router-builder", "kind": "skill", "official": false, "desc": "Turns a natural-language description of routing intent into a valid Lemonade `collection.router` policy JSON. The skill generates and validates the JSON only - it does not register it or call the live server. Use when the user wants to route requests between models (\"route sensitive queries to X and everything else to Y\"), generate a router/hybrid-router config or policy, author a collection.router JSON, split traffic between a small local model and a big/cloud model, add PII/jailbreak/topic cla", "repo": "https://github.com/amd/skills/tree/HEAD/skills/lemonade-router-builder", "install": ["npx skills add amd/skills --skill lemonade-router-builder"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/local-ai-app-integration", "name": "local-ai-app-integration", "kind": "skill", "official": false, "desc": "Integrates local AI capabilities into applications using Embeddable Lemonade. Use when the user wants to add local AI, offline AI, private AI, on-device AI, a local LLM, local chat, embeddings, image generation, speech-to-text, or text-to-speech to an existing app; replace or supplement OpenAI, Anthropic, Ollama, or other cloud AI APIs with a local backend; only use to convert user apps. Do not use when the user just wants the agent itself to generate images, transcribe, or speak locally in the ", "repo": "https://github.com/amd/skills/tree/HEAD/skills/local-ai-app-integration", "install": ["npx skills add amd/skills --skill local-ai-app-integration"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "lemonade", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/local-ai-use", "name": "local-ai-use", "kind": "skill", "official": false, "desc": "Makes this agent generate images, transcribe audio, and synthesize speech on the user's own machine through a local Lemonade Server instead of a paid cloud API. Use it above all to change that routing persistently, from now on — keep generating pictures locally while chat stays on the cloud; set this workspace up to make images on my own machine — even when the user asks for no image or file in the same breath. Also use it for a single request the user wants done locally, offline, on-device, or ", "repo": "https://github.com/amd/skills/tree/HEAD/skills/local-ai-use", "install": ["npx skills add amd/skills --skill local-ai-use"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "lemonade", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/quark-install", "name": "quark-install", "kind": "skill", "official": false, "desc": "Installs or verifies AMD Quark and ensures the selected Python environment has an accelerator-matched PyTorch. Applies to \"install Quark\", \"set up Quark\", \"pip install amd-quark\", Quark dependency or import failures, ModuleNotFoundError for quark, and Quark kernel compiler errors. Defaults to GPU and requires explicit confirmation before CPU mode. Covers PyPI universal, exactly matched native, local-wheel, and local-source installs. Does not handle standalone PyTorch requests, ONNX Runtime-only ", "repo": "https://github.com/amd/skills/tree/HEAD/skills/quark-install", "install": ["npx skills add amd/skills --skill quark-install"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "pytorch", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/quark-torch-llm-ptq", "name": "quark-torch-llm-ptq", "kind": "skill", "official": false, "desc": "Runs an end-to-end AMD Quark post-training quantization workflow for PyTorch / Hugging Face LLMs: inspect a Hub or local model, choose a quantization plan, create reproducible artifacts, request execution approval, and produce a quantized model. Applies to Llama, Qwen, Mistral, and similar transformer LLM requests involving FP8, INT4, or another Quark scheme. Does not handle .onnx model inputs or ONNX PTQ.", "repo": "https://github.com/amd/skills/tree/HEAD/skills/quark-torch-llm-ptq", "install": ["npx skills add amd/skills --skill quark-torch-llm-ptq"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "pytorch", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/serving-llms-on-epyc", "name": "serving-llms-on-epyc", "kind": "skill", "official": false, "desc": "Serves an LLM on a supported AMD EPYC server CPU using vLLM with zentorch, in Docker, Podman, or conda. Use for \"vLLM on CPU\", \"zentorch serving\", or an EPYC CPU endpoint, including on a host that also has AMD Instinct GPUs. Detects the EPYC generation, validates the runtime, checks model support and RAM fit, sizes threads/KV/NUMA, confirms the plan, launches, and verifies the endpoint. Runs one instance on one socket and its memory. Reports and stops on failure; does not retry or debug. Use ser", "repo": "https://github.com/amd/skills/tree/HEAD/skills/serving-llms-on-epyc", "install": ["npx skills add amd/skills --skill serving-llms-on-epyc"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "vllm", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/serving-llms-on-instinct", "name": "serving-llms-on-instinct", "kind": "skill", "official": false, "desc": "Serves AI models on AMD Instinct GPU hardware using vLLM. Use this skill whenever the user wants to run, serve, deploy, start, host, or launch a language model on an AMD GPU, AMD Instinct, MI300X, MI325X, MI350X, or MI355X. Also use when the user mentions vLLM on ROCm, vLLM on AMD, serving on HBM, or asks how to get a model running on AMD data center hardware. Use when the user asks \"run Qwen3\", \"serve DeepSeek\", \"start a vLLM endpoint\", \"get a model running on my AMD machine\", or any similar ph", "repo": "https://github.com/amd/skills/tree/HEAD/skills/serving-llms-on-instinct", "install": ["npx skills add amd/skills --skill serving-llms-on-instinct"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "vllm", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:oracle/mcp", "name": "mcp", "kind": "mcp", "official": null, "desc": "Repository containing MCP (Model Context Protocol) servers that provides a suite of tools for managing and interacting with Oracle products.", "repo": "https://github.com/oracle/mcp", "install": [], "stars": 479, "pushed": null, "license": null, "plat": "oracle", "live": "Not checked (runs locally)"}, {"id": "skill:zai-org/GLM-skills/skills/glmv-resume-screen", "name": "glmv-resume-screen", "kind": "skill", "official": false, "desc": "Screen and evaluate resumes against criteria using ZhiPu GLM-V multimodal model. Reads multiple resume files (PDF/DOCX/TXT), compares against user-defined screening criteria, and outputs a Markdown table with pass/fail analysis. Use when the user wants to filter resumes, compare candidates, or batch-evaluate job applications.", "repo": "https://github.com/zai-org/GLM-skills/tree/HEAD/skills/glmv-resume-screen", "install": ["npx skills add zai-org/GLM-skills --skill glmv-resume-screen"], "stars": 476, "pushed": "2026-04-15T12:28:08Z", "license": "Apache-2.0", "plat": "zhipu glm-v", "live": "Not checked (runs locally)"}, {"id": "skill:realmyang/MLView/claude-plugin/skills/mlview", "name": "mlview", "kind": "skill", "official": false, "desc": "Analyze an ML or deep-learning workflow with the active host model and publish a source-linked interactive MLView artifact.", "repo": "https://github.com/realmyang/MLView/tree/HEAD/claude-plugin/skills/mlview", "install": ["npx skills add realmyang/MLView --skill mlview"], "stars": 466, "pushed": "2026-10-30T18:42:34Z", "license": "MIT", "plat": "mlview", "live": "Not checked (runs locally)"}, {"id": "skill:malob/nix-config/configs/claude/plugins/ai-sdk/skills/ai-sdk-docs", "name": "AI SDK Documentation", "kind": "skill", "official": false, "desc": "This skill should be used when working with Vercel AI SDK, AI Gateway, streamText, generateText, generateObject, streamObject, tool calling, or AI SDK providers. Also relevant for \"ai-sdk\", \"@ai-sdk/*\" packages, or questions about AI SDK patterns, configuration, and best practices.", "repo": "https://github.com/malob/nix-config/tree/HEAD/configs/claude/plugins/ai-sdk/skills/ai-sdk-docs", "install": ["npx skills add malob/nix-config --skill AI SDK Documentation"], "stars": 464, "pushed": "2026-11-08T05:35:07Z", "license": "MIT", "plat": "vercel ai sdk", "live": "Not checked (runs locally)"}, {"id": "skill:malob/nix-config/configs/claude/plugins/pua-unicode/skills/icon-lookup", "name": "icon-lookup", "kind": "skill", "official": false, "desc": "Workaround for Claude Code filtering BMP PUA Unicode (U+E000-U+F8FF). Supplementary PUA Nerd Font icons like 󰊤 󱃾 󰁹 (U+F0000+, e.g. nf-md-github, nf-md-kubernetes, nf-md-battery) can be written directly. BMP PUA icons (Powerline, Font Awesome, Devicons) require placeholder syntax like {{ U+E0A0 }} or {{ nf-fa-star }} (without spaces), which hooks auto-convert. Invoke when reading or writing Starship configs, tmux themes, shell prompts, or statuslines.", "repo": "https://github.com/malob/nix-config/tree/HEAD/configs/claude/plugins/pua-unicode/skills/icon-lookup", "install": ["npx skills add malob/nix-config --skill icon-lookup"], "stars": 464, "pushed": "2026-11-08T05:35:07Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:awslabs/graphrag-toolkit", "name": "graphrag-toolkit", "kind": "mcp", "official": null, "desc": "Python toolkit for building graph-enhanced GenAI applications", "repo": "https://github.com/awslabs/graphrag-toolkit", "install": [], "stars": 463, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:eliasbiondo/antirion", "name": "antirion", "kind": "mcp", "official": null, "desc": "🕷️ The fastest open-source AI Gateway and LLM Gateway — one binary with built-in console to route, secure, budget and observe every LLM call across OpenAI, Anthropic, Gemini, Bedrock and more.", "repo": "https://github.com/eliasbiondo/antirion", "install": [], "stars": 462, "pushed": null, "license": null, "plat": "openai, anthropic, gemini, bedrock", "live": "Not checked (runs locally)"}, {"id": "skill:SponsioLabs/Sponsio/plugins/sponsio-claude-code/skills/configure", "name": "configure", "kind": "skill", "official": false, "desc": "Use after `/plugin install sponsio-claude-code` to wire the runtime end-to-end. The plugin install only registers hooks + skills; the contract library and per-environment overrides are configured here. Bootstraps the per-plugin contract library tree at ~/.sponsio/plugins/, installs bundled starter libraries for popular MCP servers (github, filesystem, playwright), generates fresh starter libraries for plugins / MCP servers that don't ship one (via `sponsio plugin scan`), tunes the shipped rules ", "repo": "https://github.com/SponsioLabs/Sponsio/tree/HEAD/plugins/sponsio-claude-code/skills/configure", "install": ["npx skills add SponsioLabs/Sponsio --skill configure"], "stars": 455, "pushed": "2026-11-13T17:34:36Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:SponsioLabs/Sponsio/plugins/sponsio-openclaw/skills/configure", "name": "configure", "kind": "skill", "official": false, "desc": "Use after installing the sponsio-openclaw plugin to wire the runtime end-to-end. The plugin install only registers hooks + skills; the contract library and per-environment overrides are configured here. Bootstraps the per-plugin contract library tree at ~/.sponsio/plugins/, generates fresh starter libraries for OpenClaw plugins / MCP servers via `sponsio plugin scan` (with `--introspect` to auto-discover tool inventory; the agent then applies the prompt from `sponsio plugin prompt openclaw` to e", "repo": "https://github.com/SponsioLabs/Sponsio/tree/HEAD/plugins/sponsio-openclaw/skills/configure", "install": ["npx skills add SponsioLabs/Sponsio --skill configure"], "stars": 455, "pushed": "2026-11-13T17:34:36Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:SponsioLabs/Sponsio/sponsio/skills/sponsio", "name": "sponsio", "kind": "skill", "official": false, "desc": "Install, observe, tune, and enforce Sponsio: a runtime contract layer for LLM agents that blocks unsafe tool calls and scores output quality against declared rules. Use when the user wants to set up / add / install Sponsio, add guardrails or runtime safety to an LLM agent, generate or refine a sponsio.yaml, audit tool configurations for risks (data leaks, unguarded writes, missing confirmations), explain or review existing contracts, check what Sponsio would have blocked (`sponsio report`), move", "repo": "https://github.com/SponsioLabs/Sponsio/tree/HEAD/sponsio/skills/sponsio", "install": ["npx skills add SponsioLabs/Sponsio --skill sponsio"], "stars": 455, "pushed": "2026-11-13T17:34:36Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:BrianRWagner/ai-marketing-claude-code-skills/voice-extractor", "name": "voice-extractor", "kind": "skill", "official": false, "desc": "Extract and document someone's authentic writing voice from samples. Use when someone needs a \"voice guide,\" wants to capture their writing DNA, or needs to train AI to write in their style. Also useful for ghostwriting, brand voice documentation, or onboarding writers.", "repo": "https://github.com/BrianRWagner/ai-marketing-claude-code-skills/tree/HEAD/voice-extractor", "install": ["npx skills add BrianRWagner/ai-marketing-claude-code-skills --skill voice-extractor"], "stars": 455, "pushed": "2026-03-19T12:37:28Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sanjay3290/ai-skills/skills/gemini-api", "name": "gemini-api", "kind": "skill", "official": false, "desc": "Call the Gemini API from the shell with Google's `gemini-api` CLI, when that binary is installed — text and reasoning, image generation and editing, text-to-speech, music, video, document/audio/video/YouTube analysis, transcription and captions, file uploads, and managed agents (Deep Research) run in the background. Use when the task needs a Gemini model or a second opinion from one, a generated image/voiceover/soundtrack/clip, an answer out of a PDF or recording, an SRT caption file, or a long-", "repo": "https://github.com/sanjay3290/ai-skills/tree/HEAD/skills/gemini-api", "install": ["npx skills add sanjay3290/ai-skills --skill gemini-api"], "stars": 454, "pushed": "2026-11-02T21:35:18Z", "license": "Apache-2.0", "plat": "gemini api", "live": "Not checked (runs locally)"}, {"id": "skill:guangshu100/BidMaster-Pro/skills/generate/content_gen", "name": "content_gen", "kind": "skill", "official": false, "desc": "正文生成(四模式)", "repo": "https://github.com/guangshu100/BidMaster-Pro/tree/HEAD/skills/generate/content_gen", "install": ["npx skills add guangshu100/BidMaster-Pro --skill content_gen"], "stars": 450, "pushed": "2026-09-03T09:44:03Z", "license": "AGPL-3.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:guangshu100/BidMaster-Pro/skills/output/docx_format", "name": "docx_format", "kind": "skill", "official": false, "desc": "智能排版", "repo": "https://github.com/guangshu100/BidMaster-Pro/tree/HEAD/skills/output/docx_format", "install": ["npx skills add guangshu100/BidMaster-Pro --skill docx_format"], "stars": 450, "pushed": "2026-09-03T09:44:03Z", "license": "AGPL-3.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:guangshu100/BidMaster-Pro/skills/interpret/tender_interpret", "name": "tender_interpret", "kind": "skill", "official": false, "desc": "15维度招标文件解读", "repo": "https://github.com/guangshu100/BidMaster-Pro/tree/HEAD/skills/interpret/tender_interpret", "install": ["npx skills add guangshu100/BidMaster-Pro --skill tender_interpret"], "stars": 450, "pushed": "2026-09-03T09:44:03Z", "license": "AGPL-3.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:securetron-gh/aifw", "name": "aifw", "kind": "mcp", "official": null, "desc": "AI Agent Identity and Access Management and Inference Firewall", "repo": "https://github.com/securetron-gh/aifw", "install": [], "stars": 438, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:dev.classifier/classifier", "name": "classifier.dev", "kind": "mcp", "official": null, "desc": "Sort up to 1,000 texts into your own labels with a calibrated confidence per answer. No API key.", "repo": "https://github.com/mrmps/classifier-dev", "install": ["https://classifier.dev/mcp"], "stars": 438, "pushed": "2026-11-06T02:42:19Z", "license": "MIT", "plat": "", "live": "Live · 747 ms · 5 tools (checked 2026-11-13)"}, {"id": "mcp:gh:raysonmeng/agent-bridge", "name": "agent-bridge", "kind": "mcp", "official": null, "desc": "A local bridge for bidirectional collaboration between Claude Code and Codex. 连接 Claude Code 与 Codex 的本地实时协作桥接工具。", "repo": "https://github.com/raysonmeng/agent-bridge", "install": [], "stars": 431, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:Ruya-AI/cozempic/plugin/skills/cozempic-doctor", "name": "cozempic-doctor", "kind": "skill", "official": false, "desc": "Run health checks on Claude Code configuration and sessions. Use when troubleshooting Claude Code issues. (cozempic — does not shadow Claude Code's built-in /doctor)", "repo": "https://github.com/Ruya-AI/cozempic/tree/HEAD/plugin/skills/cozempic-doctor", "install": ["npx skills add Ruya-AI/cozempic --skill cozempic-doctor"], "stars": 430, "pushed": "2026-07-03T23:04:34Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:gotalab/skillport/.skills/experimental/opus-4-5-migration", "name": "opus-4-5-migration", "kind": "skill", "official": false, "desc": "Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.", "repo": "https://github.com/gotalab/skillport/tree/HEAD/.skills/experimental/opus-4-5-migration", "install": ["npx skills add gotalab/skillport --skill opus-4-5-migration"], "stars": 421, "pushed": "2026-10-25T17:21:09Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:IBM/mcp", "name": "mcp", "kind": "mcp", "official": null, "desc": "A collection of Model Context Protocol (MCP) servers, clients and developer tools by IBM.", "repo": "https://github.com/IBM/mcp", "install": [], "stars": 420, "pushed": null, "license": null, "plat": "ibm", "live": "Not checked (runs locally)"}, {"id": "skill:syahiidkamil/Software-Engineer-AI-Agent-Atlas/.claude/skills/human-writing", "name": "human-writing", "kind": "skill", "official": false, "desc": "Write content in any language indistinguishable from a skilled human writer. Avoids AI-sounding phrases, varies sentence structure, creates natural prose. Detects target language and loads language-specific AI-tells, register, and orthography from `languages/`.", "repo": "https://github.com/syahiidkamil/Software-Engineer-AI-Agent-Atlas/tree/HEAD/.claude/skills/human-writing", "install": ["npx skills add syahiidkamil/Software-Engineer-AI-Agent-Atlas --skill human-writing"], "stars": 406, "pushed": "2026-11-01T04:14:32Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Eyadkelleh/awesome-skills-security/skills/llm-testing", "name": "llm-testing", "kind": "skill", "official": false, "desc": "Comprehensive LLM security testing prompts for bias detection, data leakage, alignment testing, and adversarial prompt resistance.", "repo": "https://github.com/Eyadkelleh/awesome-skills-security/tree/HEAD/skills/llm-testing", "install": ["npx skills add Eyadkelleh/awesome-skills-security --skill llm-testing"], "stars": 401, "pushed": "2026-06-08T08:31:04Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ErickWendel/erickwendel-contributions-mcp", "name": "erickwendel-contributions-mcp", "kind": "mcp", "official": null, "desc": "A Model Context Protocol (MCP) server that provides tools to query Erick Wendel's contributions across different platforms", "repo": "https://github.com/ErickWendel/erickwendel-contributions-mcp", "install": [], "stars": 393, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:afrise/MCPSharp", "name": "MCPSharp", "kind": "mcp", "official": null, "desc": "MCPSharp is a .NET library that helps you build Model Context Protocol (MCP) servers and clients - the standardized API protocol used by AI assistants and models.", "repo": "https://github.com/afrise/MCPSharp", "install": [], "stars": 378, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:java-ai-tech/spring-ai-summary", "name": "spring-ai-summary", "kind": "mcp", "official": null, "desc": "SpringAI，LLM，MCP，Embedding", "repo": "https://github.com/java-ai-tech/spring-ai-summary", "install": [], "stars": 375, "pushed": null, "license": null, "plat": "springai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:modelstudioai/cli", "name": "cli", "kind": "mcp", "official": null, "desc": "Official Model Studio CLI（阿里云百炼 CLI）built for AI Agent frameworks, exposing models, search, multimodal, and workflow capabilities as structured tool calls.", "repo": "https://github.com/modelstudioai/cli", "install": [], "stars": 371, "pushed": null, "license": null, "plat": "阿里云百炼", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:kerlenton/mcpsnoop", "name": "mcpsnoop", "kind": "mcp", "official": null, "desc": "Wireshark for MCP. A transparent proxy between your AI client and MCP server. Watch every call live in your terminal, fail CI on what it finds, export OpenTelemetry spans.", "repo": "https://github.com/kerlenton/mcpsnoop", "install": [], "stars": 368, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.DMontgomery40/deepseek", "name": "DeepSeek MCP Server", "kind": "mcp", "official": null, "desc": "Official DeepSeek MCP server for chat, completion, model listing, and balance endpoints.", "repo": "https://github.com/DMontgomery40/deepseek-mcp-server", "install": ["npm:deepseek-mcp-server"], "stars": 366, "pushed": "2026-10-30T20:24:36Z", "license": "MIT", "plat": "deepseek", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:itsmostafa/system-one-connector", "name": "system-one-connector", "kind": "mcp", "official": null, "desc": "System One MCP connector to evaluate anything fast and cheap. Give your AI agent direct access to models like: Jev, D1, CLM and Laya", "repo": "https://github.com/itsmostafa/system-one-connector", "install": [], "stars": 361, "pushed": null, "license": null, "plat": "system one", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:yeuxuan/Ace-Mcp-Node", "name": "Ace-Mcp-Node", "kind": "mcp", "official": null, "desc": "面向 AI 助手的代码库索引与语义搜索 MCP Server", "repo": "https://github.com/yeuxuan/Ace-Mcp-Node", "install": [], "stars": 360, "pushed": null, "license": null, "plat": "code", "live": "Not checked (runs locally)"}, {"id": "skill:semgrep/skills/skills/llm-security", "name": "llm-security", "kind": "skill", "official": false, "desc": "Security guidelines for LLM applications based on OWASP Top 10 for LLM 2025. Use when building LLM apps, reviewing AI security, implementing RAG systems, or asking about LLM vulnerabilities like 'prompt injection' or 'check LLM security'. IMPORTANT: Always consult this skill when building chatbots, AI agents, RAG pipelines, tool-using LLMs, agentic systems, or any application that calls an LLM API (OpenAI, Anthropic, Gemini, etc.) — even if the user doesn't explicitly mention security. Also use ", "repo": "https://github.com/semgrep/skills/tree/HEAD/skills/llm-security", "install": ["npx skills add semgrep/skills --skill llm-security"], "stars": 344, "pushed": "2026-11-11T00:31:33Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:langfuse", "name": "langfuse", "kind": "claude-plugin", "official": null, "desc": "Skills for working with Langfuse, the open-source LLM engineering platform for tracing, prompt management, and evaluation.", "repo": "https://github.com/langfuse/skills.git", "install": ["claude plugin install langfuse@claude-plugins-official"], "stars": 339, "pushed": "2026-11-10T13:54:01Z", "license": "MIT", "plat": "langfuse", "live": "Not checked (runs locally)"}, {"id": "skill:langfuse/skills/skills/langfuse", "name": "langfuse", "kind": "skill", "official": false, "desc": "Interact with Langfuse and access its documentation: tracing, monitoring, creating datasets, running experiments, and evaluating AI applications. Use when needing to (1) query or modify Langfuse data, (2) look up Langfuse documentation, concepts, integration guides, a feature or SDK usage, or (3) do any AI engineering task (AI observability, prompt engineering/management, evaluation and evaluator management, experimentation, dataset management, evaluation-driven CI/CD, feedback collection). Invo", "repo": "https://github.com/langfuse/skills/tree/HEAD/skills/langfuse", "install": ["npx skills add langfuse/skills --skill langfuse"], "stars": 339, "pushed": "2026-11-10T13:54:01Z", "license": "MIT", "plat": "langfuse", "live": "Not checked (runs locally)"}, {"id": "skill:JayantDevkar/claude-code-karma/skills/link-ticket-to-session", "name": "link-ticket-to-session", "kind": "skill", "official": false, "desc": "Link the current Claude Code session to a ticket (Linear, Jira, GitHub Issues, or GitHub Pull Requests) and cache its title/status in karma. Use when the user explicitly asks to link, attach, associate, or connect this session to a ticket, issue, or PR — e.g. \"/link-ticket-to-session ABC-123\", \"link this session to LINEAR-42\", \"associate this work with issue #15\", \"attach session to PR octocat/repo#7\". Do NOT auto-invoke from passing ticket-key mentions in normal conversation.", "repo": "https://github.com/JayantDevkar/claude-code-karma/tree/HEAD/skills/link-ticket-to-session", "install": ["npx skills add JayantDevkar/claude-code-karma --skill link-ticket-to-session"], "stars": 339, "pushed": "2026-11-09T22:49:13Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:langfuse/skills/skills/migrate-to-langfuse", "name": "migrate-to-langfuse", "kind": "skill", "official": false, "desc": "Migrate to Langfuse from another LLM observability/evals platform (LangSmith, Arize AX, Phoenix, Braintrust, Helicone, Promptfoo, ...). A one-off vendor switch: live tracing cutover plus transfer of historical traces, datasets, prompts, and evaluators. Not for anything Langfuse-to-Langfuse — SDK or version upgrades, project/region moves, self-hosted to Cloud — and not for moving in-code prompts into Langfuse; the main langfuse skill covers those.", "repo": "https://github.com/langfuse/skills/tree/HEAD/skills/migrate-to-langfuse", "install": ["npx skills add langfuse/skills --skill migrate-to-langfuse"], "stars": 339, "pushed": "2026-11-10T13:54:01Z", "license": "MIT", "plat": "langfuse", "live": "Not checked (runs locally)"}, {"id": "skill:ckelsoe/prompt-architect/skills/prompt-architect", "name": "prompt-architect", "kind": "skill", "official": false, "desc": "Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like \"help me write a better prompt\", \"improve this prompt\", \"what framework should I use\", \"make this prompt more effective\", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, hi", "repo": "https://github.com/ckelsoe/prompt-architect/tree/HEAD/skills/prompt-architect", "install": ["npx skills add ckelsoe/prompt-architect --skill prompt-architect"], "stars": 333, "pushed": "2026-07-24T18:00:40Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:abdulmominsakib/localmind", "name": "localmind", "kind": "mcp", "official": null, "desc": "A mobile application designed to provide a beautiful, fast, and privacy-respecting interface for on device LLM and local LLM servers and cloud providers", "repo": "https://github.com/abdulmominsakib/localmind", "install": [], "stars": 328, "pushed": null, "license": null, "plat": "mobile", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:DAWNCR0W/affine-mcp-server", "name": "affine-mcp-server", "kind": "mcp", "official": null, "desc": "Model Context Protocol server for AFFiNE. Connect AI assistants to AFFiNE workspaces, documents, databases, and collaboration APIs over stdio or HTTP.", "repo": "https://github.com/DAWNCR0W/affine-mcp-server", "install": [], "stars": 327, "pushed": null, "license": null, "plat": "affine", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:hemanth/paws-on-mcp", "name": "paws-on-mcp", "kind": "mcp", "official": null, "desc": "A comprehensive Model Context Protocol (MCP) server implementing the latest specification.", "repo": "https://github.com/hemanth/paws-on-mcp", "install": [], "stars": 327, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:MiniMax-AI/OpenAgentCore", "name": "OpenAgentCore", "kind": "mcp", "official": null, "desc": "The OpenAI Agents API, open source and self-hosted. Run Codex, Claude Code or MiniMax Code on your own machines with your own models, through the official OpenAI SDK.", "repo": "https://github.com/MiniMax-AI/OpenAgentCore", "install": [], "stars": 326, "pushed": null, "license": null, "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/open-responses", "name": "open-responses", "kind": "skill", "official": false, "desc": "This skill should be used when implementing, consuming, or debugging an Open Responses-compliant API — the open standard for multi-provider LLM interoperability. Covers protocol, items, state machines, streaming events, tools, the agentic loop pattern, and extensions. Triggers on: Open Responses, open-responses, /v1/responses endpoint, multi-provider LLM API, Open Responses compliance.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/open-responses", "install": ["npx skills add OpenRouterTeam/skills --skill open-responses"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "open responses", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-analytics", "name": "openrouter-analytics", "kind": "skill", "official": false, "desc": "Answer natural-language questions about a user's OpenRouter usage data — spend, request volume, model breakdown, latency, token usage, and cost optimization. Use when the user asks about their API usage, billing, costs, top models, traffic patterns, or wants to optimize their OpenRouter spend.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-analytics", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-analytics"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-analytics-query", "name": "openrouter-analytics-query", "kind": "skill", "official": false, "desc": "Construct and execute analytics queries against the OpenRouter API — full parameter reference for metrics, dimensions, filters, time ranges, ordering, and pagination. Use when building or debugging an analytics query, understanding the request/response shape, or handling query errors.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-analytics-query", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-analytics-query"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-analytics-schema", "name": "openrouter-analytics-schema", "kind": "skill", "official": false, "desc": "Discover the OpenRouter analytics schema — available metrics, dimensions, filter operators, and granularities. Use when you need to know what analytics data is queryable, what dimensions you can break down by, or how to map a user's question to the right metric/dimension combination.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-analytics-schema", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-analytics-schema"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-benchmarks", "name": "openrouter-benchmarks", "kind": "skill", "official": false, "desc": "Query OpenRouter's Benchmarks API for model benchmark rankings and scores. Use when the user asks for benchmark-backed model selection, model rankings by coding/intelligence/agentic ability, Artificial Analysis or Design Arena ELO/win-rate results, benchmark citations, or wants to call GET /api/v1/benchmarks. Also use alongside openrouter-models when the user asks what model should power an app, product, workflow, or use case and benchmark evidence could inform or rule out part of the recommenda", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-benchmarks", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-benchmarks"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-decisions", "name": "openrouter-decisions", "kind": "skill", "official": false, "desc": "Implement decision models through OpenRouter's Decisions API, which returns probabilities instead of generated text: pick the primitive per judgment, build minimal state, write the questions, pin a model from the live catalog, gate in code, and probe thresholds on real inputs. Use when replacing a prompt-and-parse LLM call, a keyword or similarity heuristic, a human review queue, or a misfiring router, filter, or classifier with a decision model, when locating something in an image with a Box qu", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-decisions", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-decisions"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-generations", "name": "openrouter-generations", "kind": "skill", "official": false, "desc": "Retrieve detailed metadata and stored content for individual OpenRouter generations. Use when the user wants to inspect a specific request — its cost, latency, token usage, provider routing, or the actual prompt/completion text — or is debugging a failed or unexpected generation.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-generations", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-generations"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-models", "name": "openrouter-models", "kind": "skill", "official": false, "desc": "Query OpenRouter for available AI models, pricing, capabilities, throughput, and provider performance. Use when the user asks about available OpenRouter models, model pricing, model context lengths, model capabilities, provider latency or uptime, throughput limits, supported parameters, wants to search/filter/compare models, or find the fastest provider for a model.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-models", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-models"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-oauth", "name": "openrouter-oauth", "kind": "skill", "official": false, "desc": "Implement \"Sign In with OpenRouter\" using OAuth PKCE — framework-agnostic, no SDK or client registration required. Use when the user wants to add OpenRouter login, authentication, sign-in buttons, OAuth, or AI model inference API keys for browser-based apps. No client registration, no backend, no secrets required.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-oauth", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-oauth"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-stt", "name": "openrouter-stt", "kind": "skill", "official": false, "desc": "Transcribe speech to text using OpenRouter's speech-to-text API. Use when the user asks to transcribe audio, convert speech to text, extract a transcript from a recording or meeting, caption a video's audio, or mentions STT, speech-to-text, ASR, or transcription.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-stt", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-stt"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-tts", "name": "openrouter-tts", "kind": "skill", "official": false, "desc": "Generate speech audio from text using OpenRouter's text-to-speech API. Use when the user asks to synthesize speech, narrate text, create a voiceover, generate an audiobook clip, read text aloud, convert text to an audio file, or mentions TTS, text-to-speech, or voice synthesis.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-tts", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-tts"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-typescript-sdk", "name": "openrouter-typescript-sdk", "kind": "skill", "official": false, "desc": "Complete reference for integrating with 300+ AI models through the OpenRouter TypeScript SDK and Agent packages using the callModel pattern", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-typescript-sdk", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-typescript-sdk"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/openrouter-video", "name": "openrouter-video", "kind": "skill", "official": false, "desc": "Generate videos from text prompts (and optional reference or frame images) using OpenRouter's asynchronous video generation API. Use when the user asks to create, generate, or make a video or animation from a description, animate an existing image, or turn a prompt into a short video clip.", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/openrouter-video", "install": ["npx skills add OpenRouterTeam/skills --skill openrouter-video"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:OpenRouterTeam/skills/skills/spawn-ori-eval", "name": "spawn-ori-eval", "kind": "skill", "official": false, "desc": "Spawn Ori as a subprocess to run a throwaway model eval on a pinned harness and model, then relay the results. Use when the user asks which model they should use, wants to compare models, wants to measure whether their agent or prompt does the right thing, wants to catch regressions in agent behavior, or asks how good their current model is. Applies to any codebase in any language. Do not use for plain unit tests that involve no model, and do not use to re-run an eval that already exists (run `o", "repo": "https://github.com/OpenRouterTeam/skills/tree/HEAD/skills/spawn-ori-eval", "install": ["npx skills add OpenRouterTeam/skills --skill spawn-ori-eval"], "stars": 323, "pushed": "2026-11-13T17:03:06Z", "license": null, "plat": "ori", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:huggingface/hf-mcp-server", "name": "hf-mcp-server", "kind": "mcp", "official": null, "desc": "Hugging Face MCP Server", "repo": "https://github.com/huggingface/hf-mcp-server", "install": [], "stars": 317, "pushed": null, "license": null, "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:software-mansion-labs/skills/skills/react-native-best-practices/references/on-device-ai", "name": "on-device-ai", "kind": "skill", "official": false, "desc": "Routes on-device AI tasks in React Native and Expo apps to the react-native-executorch skill. Use when building offline chat, vision, speech, embeddings or retrieval features, choosing pre-exported models, or integrating custom ExecuTorch models and pipelines.", "repo": "https://github.com/software-mansion-labs/skills/tree/HEAD/skills/react-native-best-practices/references/on-device-ai", "install": ["npx skills add software-mansion-labs/skills --skill on-device-ai"], "stars": 303, "pushed": "2026-11-04T15:59:33Z", "license": null, "plat": "react-native-executorch", "live": "Not checked (runs locally)"}, {"id": "skill:software-mansion-labs/skills/skills/react-native-executorch", "name": "react-native-executorch", "kind": "skill", "official": false, "desc": "Build React Native apps with react-native-executorch: use pre-exported AI models and task hooks, integrate custom ExecuTorch .pte models, or build TypeScript inference pipelines from PyTorch model contracts. Use for setup, model selection, inference, export integration and troubleshooting; repository contribution workflows use the library's internal skills.", "repo": "https://github.com/software-mansion-labs/skills/tree/HEAD/skills/react-native-executorch", "install": ["npx skills add software-mansion-labs/skills --skill react-native-executorch"], "stars": 303, "pushed": "2026-11-04T15:59:33Z", "license": null, "plat": "react-native-executorch", "live": "Not checked (runs locally)"}, {"id": "skill:QuinnAho/claudemap/skill", "name": "claudemap-runtime", "kind": "skill", "official": false, "desc": "Internal ClaudeMap runtime for turning a repository into a live architecture map and driving that map during walkthroughs. Prefer the public commands in .claude/commands for normal use.", "repo": "https://github.com/QuinnAho/claudemap/tree/HEAD/skill", "install": ["npx skills add QuinnAho/claudemap --skill claudemap-runtime"], "stars": 295, "pushed": "2026-04-20T19:39:17Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ferro-labs/ai-gateway", "name": "ai-gateway", "kind": "mcp", "official": null, "desc": "Unified AI Gateway for 30+ LLMs (OpenAI, Anthropic, Bedrock, Azure etc) with Caching, Guardrails, A/B test & cost controls. Go-native Fastest & Scalable AI Gateway LiteLLM & Kong AI Gateway alternative.", "repo": "https://github.com/ferro-labs/ai-gateway", "install": [], "stars": 291, "pushed": null, "license": null, "plat": "llms", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:pluveto/daan", "name": "daan", "kind": "mcp", "official": null, "desc": "✨Lightweight LLM Client with MCP 🔌 & Characters 👤", "repo": "https://github.com/pluveto/daan", "install": [], "stars": 280, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:llm4s/llm4s", "name": "llm4s", "kind": "mcp", "official": null, "desc": "Agentic  and LLM Programming in Scala", "repo": "https://github.com/llm4s/llm4s", "install": [], "stars": 279, "pushed": null, "license": null, "plat": "scala", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:noemica-io/open-claude-in-chrome", "name": "open-claude-in-chrome", "kind": "mcp", "official": null, "desc": "Claude in Chrome, reverse-engineered and open-source. No domain blocklist. Any Chromium browser. Same 18 MCP tools, same performance.", "repo": "https://github.com/noemica-io/open-claude-in-chrome", "install": [], "stars": 279, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.nirholas/alibaba-cloud", "name": "Alibaba Cloud DashScope — Qwen", "kind": "mcp", "official": null, "desc": "Alibaba Cloud DashScope MCP: Qwen chat, embeddings, and model discovery on your own account.", "repo": "https://github.com/nirholas/three.ws", "install": ["npm:@three-ws/alibaba-cloud-mcp"], "stars": 278, "pushed": "2026-11-13T00:40:26Z", "license": "Apache-2.0", "plat": "alibaba cloud dashscope", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.nirholas/ibm-x402-mcp", "name": "IBM Granite via x402", "kind": "mcp", "official": null, "desc": "x402 pay-per-use IBM Granite AI: chat, code, embeddings, analysis, forecasting. Pay USDC per call.", "repo": "https://github.com/nirholas/three.ws", "install": ["npm:@three-ws/ibm-x402-mcp"], "stars": 278, "pushed": "2026-11-13T00:40:26Z", "license": "Apache-2.0", "plat": "ibm granite", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.nirholas/ibm-x402-mcp-remote", "name": "IBM Granite x402 Remote", "kind": "mcp", "official": null, "desc": "Pay-per-use IBM Granite AI via x402: chat, code, embeddings, forecasting. USDC on Base or Solana.", "repo": "https://github.com/nirholas/three.ws", "install": ["https://three.ws/api/ibm-mcp"], "stars": 278, "pushed": "2026-11-13T00:40:26Z", "license": "Apache-2.0", "plat": "ibm granite", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.nirholas/ibm-watsonx", "name": "IBM watsonx.ai Granite", "kind": "mcp", "official": null, "desc": "IBM watsonx.ai MCP by three.ws: chat, text generation, embeddings, and tokenization.", "repo": "https://github.com/nirholas/three.ws", "install": ["npm:@three-ws/ibm-watsonx-mcp"], "stars": 278, "pushed": "2026-11-13T00:40:26Z", "license": "Apache-2.0", "plat": "ibm watsonx.ai", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.nirholas/brain-mcp", "name": "three.ws Brain", "kind": "mcp", "official": null, "desc": "List LLM providers and run chat completions through the three.ws multi-provider router.", "repo": "https://github.com/nirholas/three.ws", "install": ["npm:@three-ws/brain-mcp"], "stars": 278, "pushed": "2026-11-13T00:40:26Z", "license": "Apache-2.0", "plat": "three.ws", "live": "Not checked (runs locally)"}, {"id": "skill:ricocc/rico-skills/skills/rico-writer", "name": "rico-writer", "kind": "skill", "official": false, "desc": "中文写作、扩写与去 AI 味改稿。用于文章、推文、产品介绍、评测、教程和评论，以及“写得像人说话”“去 AI 味”“保留判断润色”等请求。保留事实、作者立场和表达习惯，处理模板句、刻意口语、生硬比喻、含混指代和空泛总结。仅做翻译、校对或格式整理时不主动重写。", "repo": "https://github.com/ricocc/rico-skills/tree/HEAD/skills/rico-writer", "install": ["npx skills add ricocc/rico-skills --skill rico-writer"], "stars": 274, "pushed": "2026-11-05T10:04:36Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.fkiene/llmtrim", "name": "llmtrim", "kind": "mcp", "official": null, "desc": "MCP server and proxy that compresses LLM prompts, tool output, and replies to cut token cost.", "repo": "https://github.com/fkiene/llmtrim", "install": ["npm:@llmtrim/cli"], "stars": 273, "pushed": "2026-11-12T14:09:47Z", "license": "MPL-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:aliargun/mcp-server-gemini", "name": "mcp-server-gemini", "kind": "mcp", "official": null, "desc": "MCP server implementation for Google's Gemini API", "repo": "https://github.com/aliargun/mcp-server-gemini", "install": [], "stars": 260, "pushed": null, "license": null, "plat": "google gemini", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:vinkius-labs/mcpfusion", "name": "mcpfusion", "kind": "mcp", "official": null, "desc": "MCP Fusion - The TypeScript framework for secure MCP servers.", "repo": "https://github.com/vinkius-labs/mcpfusion", "install": [], "stars": 256, "pushed": null, "license": null, "plat": "typescript", "live": "Not checked (runs locally)"}, {"id": "skill:Comfy-Org/comfy-skills/claude-code-sdk/skills/comfy-sdk", "name": "comfy-sdk", "kind": "skill", "official": false, "desc": "Build an app that generates images, video or audio through Comfy Router using the Comfy SDK (Python `comfy-sdk` or TypeScript `@comfyorg/sdk`). Use when a task mentions the Comfy SDK, Comfy Router, `models.run`, or adding generation to an app via api.comfy.org. Not for driving the `comfy` CLI, ComfyUI workflows or custom nodes (see the `comfy` skill).", "repo": "https://github.com/Comfy-Org/comfy-skills/tree/HEAD/claude-code-sdk/skills/comfy-sdk", "install": ["npx skills add Comfy-Org/comfy-skills --skill comfy-sdk"], "stars": 251, "pushed": "2026-11-11T06:23:24Z", "license": "MIT", "plat": "comfy cloud", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:RaiAnsar/claude_code-gemini-mcp", "name": "claude_code-gemini-mcp", "kind": "mcp", "official": null, "desc": "Simplified Gemini for Claude Code. ", "repo": "https://github.com/RaiAnsar/claude_code-gemini-mcp", "install": [], "stars": 250, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:coralogix/cx-cli/skills/cx-ai-center", "name": "cx-ai-center", "kind": "skill", "official": false, "desc": "Use this skill for any question or action about the user's AI/GenAI applications or agents — their behavior, prompts/responses, quality, hallucinations, guardrails, security, cost/tokens, errors, evaluations/policies, model pricing, or configuration — including comparing or tracking agents over time. It covers both analyzing AI telemetry (GenAI spans) and managing AI Center config via the `cx ai-center` commands.", "repo": "https://github.com/coralogix/cx-cli/tree/HEAD/skills/cx-ai-center", "install": ["npx skills add coralogix/cx-cli --skill cx-ai-center"], "stars": 243, "pushed": "2026-11-13T08:06:44Z", "license": "Apache-2.0", "plat": "ai/genai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:icoretech/codex-pooler", "name": "codex-pooler", "kind": "mcp", "official": null, "desc": "The full featured self-hosted Codex gateway, for teams, agents and you", "repo": "https://github.com/icoretech/codex-pooler", "install": [], "stars": 242, "pushed": null, "license": null, "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:Shaivpidadi/FreeRide", "name": "freeride", "kind": "skill", "official": false, "desc": "Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json. Optional Groq, Gemini and Cerebras free tiers are opt-in. Use when the user mentions free AI, OpenRouter, model switching, rate limits, or wants to reduce AI costs.", "repo": "https://github.com/Shaivpidadi/FreeRide", "install": ["npx skills add Shaivpidadi/FreeRide --skill freeride"], "stars": 240, "pushed": "2026-10-24T23:26:26Z", "license": "MIT-0", "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "skill:johnpapa/ai-ready/skills/ai-ready", "name": "ai-ready", "kind": "skill", "official": false, "desc": "**ANALYSIS SKILL** — Analyze any repository and generate AI-ready configuration — a canonical AGENTS.md, thin per-tool pointer files, skills, CI workflows, issue templates. WHEN: \\\"make this repo ai-ready\\\", \\\"set up AI config\\\", \\\"add copilot instructions\\\", \\\"prepare this repo for AI contributions\\\", \\\"generate AGENTS.md\\\". INVOKES: glob, grep, view, create, edit for repo analysis and file generation. FOR SINGLE OPERATIONS: use create/edit directly for individual config files.", "repo": "https://github.com/johnpapa/ai-ready/tree/HEAD/skills/ai-ready", "install": ["npx skills add johnpapa/ai-ready --skill ai-ready"], "stars": 238, "pushed": "2026-09-27T21:08:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.aartiq/nowaikit", "name": "ServiceNow MCP Server (NowAIKit)", "kind": "mcp", "official": null, "desc": "ServiceNow MCP server: 450+ tools for Claude, ChatGPT, Copilot & any AI. Read-only by default.", "repo": "https://github.com/aartiq/servicenow-mcp", "install": ["npm:nowaikit"], "stars": 237, "pushed": "2026-09-23T08:28:22Z", "license": "NOASSERTION", "plat": "servicenow", "live": "Not checked (runs locally)"}, {"id": "skill:jamesrochabrun/skills/skills/anthropic-prompt-engineer", "name": "anthropic-prompt-engineer", "kind": "skill", "official": false, "desc": "Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.", "repo": "https://github.com/jamesrochabrun/skills/tree/HEAD/skills/anthropic-prompt-engineer", "install": ["npx skills add jamesrochabrun/skills --skill anthropic-prompt-engineer"], "stars": 233, "pushed": "2026-01-14T07:21:27Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:jamesrochabrun/skills/skills/llm-router", "name": "llm-router", "kind": "skill", "official": false, "desc": "This skill should be used when users want to route LLM requests to different AI providers (OpenAI, Grok/xAI, Groq, DeepSeek, OpenRouter) using SwiftOpenAI-CLI. Use this skill when users ask to \"use grok\", \"ask grok\", \"use groq\", \"ask deepseek\", or any similar request to query a specific LLM provider in agent mode.", "repo": "https://github.com/jamesrochabrun/skills/tree/HEAD/skills/llm-router", "install": ["npx skills add jamesrochabrun/skills --skill llm-router"], "stars": 233, "pushed": "2026-01-14T07:21:27Z", "license": "MIT", "plat": "swiftopenai-cli", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:dewzor/claude-desktop-fedora", "name": "claude-desktop-fedora", "kind": "mcp", "official": null, "desc": "Claude Desktop for Fedora 44 — native RPM built from Anthropic's official Linux x86_64 build. Unmodified upstream payload, Chromium sandbox on, Claude Code + Cowork + MCP working.", "repo": "https://github.com/dewzor/claude-desktop-fedora", "install": [], "stars": 229, "pushed": null, "license": null, "plat": "fedora", "live": "Not checked (runs locally)"}, {"id": "skill:JimLiu/science-skills/skills/managed-model-endpoints", "name": "managed-model-endpoints", "kind": "skill", "official": false, "desc": "Register a model service in the managed family — a local model server container the daemon starts/stops on demand, or a remote upstream model API (https). Read the runbook, allocate a port (local only), compose idempotent start/stop scripts (local only), register once. Load when the user wants a model service available for inference, or when list_compute shows managed endpoints.", "repo": "https://github.com/JimLiu/science-skills/tree/HEAD/skills/managed-model-endpoints", "install": ["npx skills add JimLiu/science-skills --skill managed-model-endpoints"], "stars": 229, "pushed": "2026-10-29T07:23:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:JimLiu/science-skills/skills/product-self-knowledge", "name": "product-self-knowledge", "kind": "skill", "official": false, "desc": "Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude", "repo": "https://github.com/JimLiu/science-skills/tree/HEAD/skills/product-self-knowledge", "install": ["npx skills add JimLiu/science-skills --skill product-self-knowledge"], "stars": 229, "pushed": "2026-10-29T07:23:51Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:JimLiu/science-skills/skills/using-model-endpoint", "name": "using-model-endpoint", "kind": "skill", "official": false, "desc": "Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASE_URL preloaded). Load once a task needs predictions from a registered model endpoint.", "repo": "https://github.com/JimLiu/science-skills/tree/HEAD/skills/using-model-endpoint", "install": ["npx skills add JimLiu/science-skills --skill using-model-endpoint"], "stars": 229, "pushed": "2026-10-29T07:23:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:LKbaba/Claude-code-ChatInWindows", "name": "Claude-code-ChatInWindows", "kind": "mcp", "official": null, "desc": "Full-featured GUI for Claude Code CLI in VS Code — Windows (no WSL) & macOS. Third-party API, MCP plugins, Skills, Hooks, real-time token tracking. Actively maintained.", "repo": "https://github.com/LKbaba/Claude-code-ChatInWindows", "install": [], "stars": 226, "pushed": null, "license": null, "plat": "vs code", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.comet-ml/opik-mcp", "name": "Opik", "kind": "mcp", "official": null, "desc": "Read and score Opik traces, experiments, datasets and prompts from your AI assistant.", "repo": "https://github.com/comet-ml/opik-mcp", "install": ["pypi:opik-mcp", "https://www.comet.com/opik/api/v1/mcp"], "stars": 226, "pushed": "2026-11-06T14:49:26Z", "license": "Apache-2.0", "plat": "opik", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:guardana/guardana", "name": "guardana", "kind": "mcp", "official": null, "desc": "Open-source AI security verification for model artifacts, live endpoints, MCP servers, and recorded agent traces. Reproducible evidence for release decisions.", "repo": "https://github.com/guardana/guardana", "install": [], "stars": 225, "pushed": null, "license": null, "plat": "mcp servers", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:antarikshc/perfetto-mcp", "name": "perfetto-mcp", "kind": "mcp", "official": null, "desc": "This is a Model Context Protocol (MCP) server that gets answers from your Perfetto Traces. It turns natural‑language prompts into focused Perfetto analyses.", "repo": "https://github.com/antarikshc/perfetto-mcp", "install": [], "stars": 223, "pushed": null, "license": null, "plat": "mcp servers", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.MPP32/mpp32-mcp-server", "name": "io.github.MPP32/mpp32-mcp-server", "kind": "mcp", "official": null, "desc": "Payment layer for AI agents. One MCP, five protocols, thousands of paid APIs your agent can call.", "repo": "https://github.com/MPP32/MPP32", "install": ["npm:mpp32-mcp-server"], "stars": 221, "pushed": "2026-08-04T19:09:03Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Eric-Terminal/ETOS-LLM-Studio", "name": "ETOS-LLM-Studio", "kind": "mcp", "official": null, "desc": "A native LLM client for iOS & Apple Watch. Run local GGUF models offline via llama.cpp, or connect to OpenAI/Claude/Gemini. Features local RAG, Model Context Protocol (MCP) tools, Siri Shortcuts, and cross-device sync. Built with Swift.", "repo": "https://github.com/Eric-Terminal/ETOS-LLM-Studio", "install": [], "stars": 219, "pushed": null, "license": null, "plat": "mcp servers", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:inference-gateway/inference-gateway", "name": "inference-gateway", "kind": "mcp", "official": null, "desc": "An open-source, cloud-native, high-performance gateway unifying multiple LLM providers, from local solutions like Ollama to major cloud providers such as OpenAI, Groq, Cohere, Anthropic, Cloudflare and DeepSeek.", "repo": "https://github.com/inference-gateway/inference-gateway", "install": [], "stars": 213, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:RelayPlane/proxy", "name": "proxy", "kind": "mcp", "official": null, "desc": "Local-first LLM proxy that meters what every agent run costs and kills runaways before they drain your budget. Free, MIT.", "repo": "https://github.com/RelayPlane/proxy", "install": [], "stars": 212, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:modular/skills/benchmark-model", "name": "benchmark-model", "kind": "skill", "official": false, "desc": "Benchmark a model served on MAX with the `max benchmark` command: measure throughput (tokens/sec), latency (TTFT, TPOT, inter-token latency), and GPU utilization by driving load against a running `max serve` endpoint. Use this whenever the user wants to benchmark, load-test, or measure the performance of a MAX model, get tokens-per-second / TTFT / TPOT numbers, run a concurrency or request-rate sweep, compare latency vs throughput, size a deployment, or produce benchmark JSON, even if they don't", "repo": "https://github.com/modular/skills/tree/HEAD/benchmark-model", "install": ["npx skills add modular/skills --skill benchmark-model"], "stars": 210, "pushed": "2026-11-13T21:01:00Z", "license": "NOASSERTION", "plat": "max", "live": "Not checked (runs locally)"}, {"id": "skill:modular/skills/debug-model", "name": "debug-model", "kind": "skill", "official": false, "desc": "Debug a MAX model that loads, compiles, serves, and generates tokens but whose output disagrees with a reference implementation. Use whenever parity debugging stalls on scalar taps, the model returns gibberish or wrong greedy tokens, logit cosine is high but argmax differs, or generation is coherent then diverges. Applies during an architecture port, a quantization bring-up, a multi-GPU conversion, or after a MAX upgrade. Triggers on \"parity failure\", \"silent corruption\", \"logits match but token", "repo": "https://github.com/modular/skills/tree/HEAD/debug-model", "install": ["npx skills add modular/skills --skill debug-model"], "stars": 210, "pushed": "2026-11-13T21:01:00Z", "license": "NOASSERTION", "plat": "max", "live": "Not checked (runs locally)"}, {"id": "skill:modular/skills/eval-model", "name": "eval-model", "kind": "skill", "official": false, "desc": "Measures the task accuracy of text models served by MAX using standard benchmarks such as GSM8K, MMLU, HellaSwag, ARC, AIME, GPQA, TruthfulQA, WinoGrande, and BABILong. Use when benchmarking a served model, comparing it with model-card or reference scores, verifying that a new MAX model produces correct answers, or running repeatable dataset evaluations against a MAX OpenAI-compatible endpoint.", "repo": "https://github.com/modular/skills/tree/HEAD/eval-model", "install": ["npx skills add modular/skills --skill eval-model"], "stars": 210, "pushed": "2026-11-13T21:01:00Z", "license": "NOASSERTION", "plat": "max", "live": "Not checked (runs locally)"}, {"id": "skill:modular/skills/import-model", "name": "import-model", "kind": "skill", "official": false, "desc": "Use when importing a new model architecture into MAX from a Hugging Face model ID. Triggers on: \"import a model into MAX\", \"add model to MAX\", \"bring up <HF model> in MAX\". Workflow: inspect the Hugging Face config and modeling code, then scaffold from a similar ModuleV3 architecture. Implement each layer as a max.nn Module that matches HF, serve, then verify against the Hugging Face reference. When the server runs but output is wrong (gibberish, greedy mismatch, coherent-then-diverges), load de", "repo": "https://github.com/modular/skills/tree/HEAD/import-model", "install": ["npx skills add modular/skills --skill import-model"], "stars": 210, "pushed": "2026-11-13T21:01:00Z", "license": "NOASSERTION", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:modular/skills/migrate-max-v2-to-v3", "name": "migrate-max-v2-to-v3", "kind": "skill", "official": false, "desc": "Use when migrating a MAX model from the V2 graph API (max.nn.legacy, TensorValue, explicit Graph construction) to ModuleV3 (max.nn, Tensor, F.lazy() + compile()). Triggers on: \"migrate to ModuleV3\", \"port a max.nn.legacy model to max.experimental\", \"convert TensorValue to Tensor\", \"update my model to the V3 API\", \"ModuleV3 migration\". Workflow: read the concept map, rewrite imports and __call__ signatures, and replace graph building with compile(weights=...). Align weight names with the new modu", "repo": "https://github.com/modular/skills/tree/HEAD/migrate-max-v2-to-v3", "install": ["npx skills add modular/skills --skill migrate-max-v2-to-v3"], "stars": 210, "pushed": "2026-11-13T21:01:00Z", "license": "NOASSERTION", "plat": "max", "live": "Not checked (runs locally)"}, {"id": "skill:modular/skills/profile-model", "name": "profile-model", "kind": "skill", "official": false, "desc": "Profile a model running on MAX to find where it spends time and whether the GPU is saturated. Use when the user asks to \"profile my model,\" \"where is my model spending time,\" \"why is inference slow,\" \"is my GPU being utilized,\" \"how much GPU am I using,\" \"get a kernel breakdown,\" \"capture an nsys/rocprof/ncu trace of max serve,\" or wants to measure MAX inference performance. Works for any model MAX can run — built-in architectures and custom ones loaded with --custom-architectures — from a pip o", "repo": "https://github.com/modular/skills/tree/HEAD/profile-model", "install": ["npx skills add modular/skills --skill profile-model"], "stars": 210, "pushed": "2026-11-13T21:01:00Z", "license": "NOASSERTION", "plat": "max", "live": "Not checked (runs locally)"}, {"id": "skill:modular/skills/serve-model", "name": "serve-model", "kind": "skill", "official": false, "desc": "Serve a model with MAX's `max serve` command: set up the environment (pixi or uv with the max-nightly conda channel / nightly wheel index), point the server at a Hugging Face repo or local checkpoint, target a custom architecture with `--custom-architectures`, and pick the right serve flags for the model. Use this whenever the user wants to run, launch, start, or host a model on MAX, bring up an OpenAI-compatible endpoint, serve a custom/ported architecture, debug a `max serve` startup failure, ", "repo": "https://github.com/modular/skills/tree/HEAD/serve-model", "install": ["npx skills add modular/skills --skill serve-model"], "stars": 210, "pushed": "2026-11-13T21:01:00Z", "license": "NOASSERTION", "plat": "max", "live": "Not checked (runs locally)"}, {"id": "skill:23jmo/jmo-skills/skills/codex", "name": "codex", "kind": "skill", "official": false, "desc": "Hand off a scoped implementation, code review, or technical second opinion to the OpenAI Codex CLI from Claude Code. Use when the user asks to delegate work to Codex or get an independent review.", "repo": "https://github.com/23jmo/jmo-skills/tree/HEAD/skills/codex", "install": ["npx skills add 23jmo/jmo-skills --skill codex"], "stars": 209, "pushed": "2026-10-10T20:02:26Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Corpus-OS/corpusos", "name": "corpusos", "kind": "mcp", "official": null, "desc": "Open-source protocol suite standardizing LLM, Vector, Graph, and Embedding infrastructure across LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, and MCP. 3,330+ conformance tests. One protocol. Any framework. Any provider.", "repo": "https://github.com/Corpus-OS/corpusos", "install": [], "stars": 207, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:rectalogic/langchain-mcp", "name": "langchain-mcp", "kind": "mcp", "official": null, "desc": "Model Context Protocol tool support for LangChain", "repo": "https://github.com/rectalogic/langchain-mcp", "install": [], "stars": 204, "pushed": null, "license": null, "plat": "langchain", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:rootflo/wavefront", "name": "wavefront", "kind": "mcp", "official": null, "desc": "🔥🔥🔥 Enterprise AI Harness, connect your data to AI", "repo": "https://github.com/rootflo/wavefront", "install": [], "stars": 203, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:kangarooking/agnes-free-model-skills/agnes-free-text", "name": "agnes-free-text", "kind": "skill", "official": false, "desc": "Call the free Agnes text model API for chat completions, streaming answers, coding help, tool-calling experiments, and OpenAI-compatible text generation. Use when the user asks for 免费文本, 免费的文本模型, free text API, Agnes text, Agnes-2.0-Flash, free LLM calls, or wants to use a free chat/completion model through an Agent skill.", "repo": "https://github.com/kangarooking/agnes-free-model-skills/tree/HEAD/agnes-free-text", "install": ["npx skills add kangarooking/agnes-free-model-skills --skill agnes-free-text"], "stars": 202, "pushed": "2026-06-01T12:47:31Z", "license": "MIT", "plat": "agnes text", "live": "Not checked (runs locally)"}, {"id": "skill:rodrigohighermind/highermind-code-skills/hm-llm-guardrails", "name": "hm-llm-guardrails", "kind": "skill", "official": false, "desc": "Catálogo de 14 patterns obrigatórios para app que integra LLM (Claude/GPT/Gemini) em produção. Use antes de shippar feature LLM pra produção, quando custo da API explode sem explicação, quando user reclama de \"respostas demoram demais\" ou \"trava no meio\", ao adicionar tool calling, agentes ou chat persistente. Cobre sliding window, lazy client factory, in-flight dedupe, rate limit por endpoint, streaming abort/retry, schema validation no response, token budget explícito, cross-channel safety, co", "repo": "https://github.com/rodrigohighermind/highermind-code-skills/tree/HEAD/hm-llm-guardrails", "install": ["npx skills add rodrigohighermind/highermind-code-skills --skill hm-llm-guardrails"], "stars": 197, "pushed": "2026-06-14T22:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:douglasmonsky/codex-usage-tracker", "name": "codex-usage-tracker", "kind": "mcp", "official": null, "desc": "Local-first MCP tools and dashboard for investigating Codex token usage, credits, costs, caching, and thread patterns.", "repo": "https://github.com/douglasmonsky/codex-usage-tracker", "install": [], "stars": 196, "pushed": null, "license": null, "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:treylom/prompt-engineering-skills/skills/claude-4.7-prompt-strategies", "name": "claude-4-7-prompt-strategies", "kind": "skill", "official": false, "desc": "Use when the user explicitly targets Claude Opus 4.7 or earlier (구세대 first-class 지원) — legacy Claude 프롬프트 전략 레퍼런스의 라우팅 인덱스. 최신 모델(Opus 5·Fable 5·4.8·Sonnet 5)은 claude-fable-5-prompt-strategies.md가 기준 파일. 본문 = references/full.md.", "repo": "https://github.com/treylom/prompt-engineering-skills/tree/HEAD/skills/claude-4.7-prompt-strategies", "install": ["npx skills add treylom/prompt-engineering-skills --skill claude-4-7-prompt-strategies"], "stars": 194, "pushed": "2026-11-10T23:53:40Z", "license": "NOASSERTION", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:treylom/prompt-engineering-skills/instructions/Gems-Prompt-Generator", "name": "gems-prompt-generator", "kind": "skill", "official": false, "desc": "Use when needing Google Gems 프롬프트 생성기. Gems 페르소나용 시스템 프롬프트 자동 생성.", "repo": "https://github.com/treylom/prompt-engineering-skills/tree/HEAD/instructions/Gems-Prompt-Generator", "install": ["npx skills add treylom/prompt-engineering-skills --skill gems-prompt-generator"], "stars": 194, "pushed": "2026-11-10T23:53:40Z", "license": "NOASSERTION", "plat": "google gems", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:cdesktop-ai/cdesktop", "name": "cdesktop", "kind": "mcp", "official": null, "desc": "Open-source Claude Code Desktop alternative. Use any provider/model. Web UI for Claude Code, Codex, Gemini CLI, OpenCode, Hermes.", "repo": "https://github.com/cdesktop-ai/cdesktop", "install": [], "stars": 190, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:johannesbrandenburger/typst-mcp", "name": "typst-mcp", "kind": "mcp", "official": null, "desc": "Typst MCP Server is an MCP (Model Context Protocol) implementation that helps AI models interact with Typst, a markup-based typesetting system. The server provides tools for converting between LaTeX and Typst, validating Typst syntax, and generating images from Typst code.", "repo": "https://github.com/johannesbrandenburger/typst-mcp", "install": [], "stars": 187, "pushed": null, "license": null, "plat": "typst", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.sandbaseai/cli", "name": "SandBase", "kind": "mcp", "official": null, "desc": "Connect MCP clients to 2,000+ AI models without managing provider API keys.", "repo": "https://github.com/sandbaseai/cli", "install": ["npm:@sandbaseai/cli", "https://api.sandbase.ai/v1/mcp"], "stars": 186, "pushed": "2026-11-11T04:59:05Z", "license": "Apache-2.0", "plat": "sandbase", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Ratnesh-181998/AI-Engineer", "name": "AI-Engineer", "kind": "mcp", "official": null, "desc": "AI Engineering Specially Topics- Agentic AI & GenAI  Explanation ", "repo": "https://github.com/Ratnesh-181998/AI-Engineer", "install": [], "stars": 184, "pushed": null, "license": null, "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "mcp:com.mux/mcp", "name": "com.mux/mcp", "kind": "mcp", "official": null, "desc": "The official MCP Server for the Mux API", "repo": "https://github.com/muxinc/mux-node-sdk", "install": ["npm:@mux/mcp", "https://mcp.mux.com"], "stars": 182, "pushed": "2026-11-11T00:46:06Z", "license": "Apache-2.0", "plat": "mux", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:escapeWu/perplexity-ai", "name": "perplexity-ai", "kind": "mcp", "official": null, "desc": "A third-party Perplexity MCP/REST API implementation that leverages Pro account Pool to provide unlimited quota for ask and deep search capabilities.", "repo": "https://github.com/escapeWu/perplexity-ai", "install": [], "stars": 181, "pushed": null, "license": null, "plat": "perplexity ai", "live": "Not checked (runs locally)"}, {"id": "skill:CodeAlive-AI/ai-driven-development/skills/prompt-engineering", "name": "prompt-engineering", "kind": "skill", "official": false, "desc": "Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like \"improve this prompt\", \"write a system prompt\", \"optimize my instructions\", \"help me prompt engineer\", \"audit this prompt\", \"review my prompt\", or when building agentic systems that need structured prompts.", "repo": "https://github.com/CodeAlive-AI/ai-driven-development/tree/HEAD/skills/prompt-engineering", "install": ["npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering"], "stars": 181, "pushed": "2026-11-13T14:48:54Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:rawveg/ollama-mcp", "name": "ollama-mcp", "kind": "mcp", "official": null, "desc": "An MCP Server for Ollama", "repo": "https://github.com/rawveg/ollama-mcp", "install": [], "stars": 179, "pushed": null, "license": null, "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:kky42/codex-as-mcp", "name": "codex-as-mcp", "kind": "mcp", "official": null, "desc": "Convert codex CLI tool to an MCP — unleash the power of GPT-5", "repo": "https://github.com/kky42/codex-as-mcp", "install": [], "stars": 177, "pushed": null, "license": null, "plat": "gpt-5", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:langfuse/mcp-server-langfuse", "name": "mcp-server-langfuse", "kind": "mcp", "official": null, "desc": "Model Context Protocol (MCP) Server for Langfuse Prompt Management. This server allows you to access and manage your Langfuse prompts through the Model Context Protocol.", "repo": "https://github.com/langfuse/mcp-server-langfuse", "install": [], "stars": 176, "pushed": null, "license": null, "plat": "mcp-server-langfuse", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:thevibeworks/claude-code-docs", "name": "claude-code-docs", "kind": "mcp", "official": null, "desc": "Everything Anthropic ships for builders, archived as markdown and refreshed 4× daily. 4,200+ files: API, Claude Code, MCP, support, cookbooks, skills, plugins.", "repo": "https://github.com/thevibeworks/claude-code-docs", "install": [], "stars": 173, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:makoMakoGo/fish-claude", "name": "fish-claude", "kind": "mcp", "official": null, "desc": "Fish's custom settings and tools about claude code, codex, oh-my-pi etc ", "repo": "https://github.com/makoMakoGo/fish-claude", "install": [], "stars": 173, "pushed": "2026-07-17T15:50:29Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:infinitimeless/LMStudio-MCP", "name": "LMStudio-MCP", "kind": "mcp", "official": null, "desc": "A Model Control Protocol (MCP) server that allows Claude to communicate with locally running LLM models via LM Studio.", "repo": "https://github.com/infinitimeless/LMStudio-MCP", "install": [], "stars": 173, "pushed": null, "license": null, "plat": "lm studio", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Vaibhavs10/mcp-with-openai-gpt-oss", "name": "mcp-with-openai-gpt-oss", "kind": "mcp", "official": null, "desc": "", "repo": "https://github.com/Vaibhavs10/mcp-with-openai-gpt-oss", "install": [], "stars": 171, "pushed": null, "license": null, "plat": "openai gpt", "live": "Not checked (runs locally)"}, {"id": "skill:veniceai/skills/skills/venice-characters", "name": "venice-characters", "kind": "skill", "official": false, "desc": "Discover and use Venice public characters (persona-driven system prompts with a suggested model). Covers GET /characters (search/filter/sort/paginate), /characters/{slug}, /characters/{slug}/reviews, the Character and Review schemas, Bearer-API-key-only auth, filter semantics (adult, pro, modelId), and how to apply a character via venice_parameters.character_slug in chat completions.", "repo": "https://github.com/veniceai/skills/tree/HEAD/skills/venice-characters", "install": ["npx skills add veniceai/skills --skill venice-characters"], "stars": 170, "pushed": "2026-11-10T23:48:57Z", "license": "MIT", "plat": "venice", "live": "Not checked (runs locally)"}, {"id": "skill:veniceai/skills/skills/venice-chat", "name": "venice-chat", "kind": "skill", "official": false, "desc": "Call POST /chat/completions on Venice. Covers the OpenAI-compatible request shape, Venice-only venice_parameters (web search, scraping, citations, E2EE, characters, thinking control, X search), anon_user_id, multimodal inputs (images/audio/video/files), tool calls, reasoning controls (reasoning_effort, reasoning.enabled), streaming, prompt caching, structured output, per-model caps, and model feature suffixes.", "repo": "https://github.com/veniceai/skills/tree/HEAD/skills/venice-chat", "install": ["npx skills add veniceai/skills --skill venice-chat"], "stars": 170, "pushed": "2026-11-10T23:48:57Z", "license": "MIT", "plat": "venice", "live": "Not checked (runs locally)"}, {"id": "skill:veniceai/skills/skills/venice-models", "name": "venice-models", "kind": "skill", "official": false, "desc": "Discover Venice models, their capabilities, constraints, and pricing. Covers GET /models (with the ?type filter - text, image, video, music, tts, asr, embedding, upscale, inpaint, decision, all, code), /models/traits, /models/compatibility_mapping, every ModelResponse field (capabilities, constraints, per-type pricing with promotional rates, uncensored, deprecation, voice-changer and voice-cloning specs), and how to use this to pick the right model programmatically.", "repo": "https://github.com/veniceai/skills/tree/HEAD/skills/venice-models", "install": ["npx skills add veniceai/skills --skill venice-models"], "stars": 170, "pushed": "2026-11-10T23:48:57Z", "license": "MIT", "plat": "venice", "live": "Not checked (runs locally)"}, {"id": "skill:veniceai/skills/skills/venice-text-routing", "name": "venice-text-routing", "kind": "skill", "official": false, "desc": "Route a prompt to the right Venice text model based on privacy tier (anonymized / private / TEE / E2EE), modality (vision / audio / video input), capability (reasoning, reasoning effort, code, function calling, X search, large context, structured output), uncensored needs, and cost. Use when a local agent (Claude Code, Hermes, NanoClaw, Codex CLI, etc.) needs to decide whether to handle a prompt locally or escalate to Venice, and if escalating, which Venice text model to call. Sits one level abo", "repo": "https://github.com/veniceai/skills/tree/HEAD/skills/venice-text-routing", "install": ["npx skills add veniceai/skills --skill venice-text-routing"], "stars": 170, "pushed": "2026-11-10T23:48:57Z", "license": "MIT", "plat": "venice", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:juspay/neurolink", "name": "neurolink", "kind": "mcp", "official": null, "desc": "The pipe layer of an AI nervous system — one interface connecting provider neurons to your application, across three inference types: generate, stream, and a calibrated decide (via TypeSafe Jev). MCP-native, voice (TTS/STT/realtime), RAG, memory, file processors. Powers Tara, Yama and Clairvoyance a", "repo": "https://github.com/juspay/neurolink", "install": [], "stars": 169, "pushed": null, "license": null, "plat": "tts", "live": "Not checked (runs locally)"}, {"id": "skill:DojoCodingLabs/remotion-superpowers/skills/generate-clip", "name": "generate-clip", "kind": "skill", "official": false, "desc": "Generate AI video clips for your Remotion project. Create unique footage from text descriptions or animate still images into video — no stock footage needed.", "repo": "https://github.com/DojoCodingLabs/remotion-superpowers/tree/HEAD/skills/generate-clip", "install": ["npx skills add DojoCodingLabs/remotion-superpowers --skill generate-clip"], "stars": 168, "pushed": "2026-10-29T15:38:53Z", "license": "MIT", "plat": "ai video generation", "live": "Not checked (runs locally)"}, {"id": "skill:DojoCodingLabs/remotion-superpowers/skills/generate-image", "name": "generate-image", "kind": "skill", "official": false, "desc": "Generate AI images for your Remotion project. Describe what you need — backgrounds, thumbnails, characters, abstract art — and get production-ready images via AI generation or Replicate models.", "repo": "https://github.com/DojoCodingLabs/remotion-superpowers/tree/HEAD/skills/generate-image", "install": ["npx skills add DojoCodingLabs/remotion-superpowers --skill generate-image"], "stars": 168, "pushed": "2026-10-29T15:38:53Z", "license": "MIT", "plat": "ai image generation", "live": "Not checked (runs locally)"}, {"id": "skill:DojoCodingLabs/remotion-superpowers/skills/review-video", "name": "review-video", "kind": "skill", "official": false, "desc": "Review a Remotion video with AI vision. Starts with a cheap local check of rendered still frames, then has TwelveLabs watch the full render and score it against a JSON rubric (pacing, composition, text legibility, audio sync and more) with timestamped fixes, applies the fixes and re-reviews. The feedback loop that closes the production circle.", "repo": "https://github.com/DojoCodingLabs/remotion-superpowers/tree/HEAD/skills/review-video", "install": ["npx skills add DojoCodingLabs/remotion-superpowers --skill review-video"], "stars": 168, "pushed": "2026-10-29T15:38:53Z", "license": "MIT", "plat": "ai vision", "live": "Not checked (runs locally)"}, {"id": "skill:langchain-ai/langsmith-skills/config/skills/langsmith-graph-evaluator", "name": "langsmith-graph-evaluator", "kind": "skill", "official": false, "desc": "Experimental. Build a LangSmith evaluator as a graph of steps with openevals.graph: code checks, LLM judges and decision models wired together, each traced as its own span and drawn as a graph in LangSmith. Use when an evaluator needs several chained checks, or the user asks for a graph, multi-step or composed evaluator. For a single judge or code check, use langsmith-evaluator.", "repo": "https://github.com/langchain-ai/langsmith-skills/tree/HEAD/config/skills/langsmith-graph-evaluator", "install": ["npx skills add langchain-ai/langsmith-skills --skill langsmith-graph-evaluator"], "stars": 166, "pushed": "2026-11-14T01:59:09Z", "license": "MIT", "plat": "langsmith", "live": "Not checked (runs locally)"}, {"id": "skill:langchain-ai/langsmith-skills/config/skills/langsmith-online-eval-engineering", "name": "langsmith-online-eval-engineering", "kind": "skill", "official": false, "desc": "Design, test, create, and attach LangSmith online evaluators for production traces or conversation threads. Use for workspace evaluators, run or thread rules, sampling, filters, backfills, and evaluator monitoring; use eval-engineering for Harbor tasks and agent benchmarks.", "repo": "https://github.com/langchain-ai/langsmith-skills/tree/HEAD/config/skills/langsmith-online-eval-engineering", "install": ["npx skills add langchain-ai/langsmith-skills --skill langsmith-online-eval-engineering"], "stars": 166, "pushed": "2026-11-14T01:59:09Z", "license": "MIT", "plat": "langsmith", "live": "Not checked (runs locally)"}, {"id": "skill:revfactory/skills/codex-cli", "name": "codex-cli", "kind": "skill", "official": false, "desc": "OpenAI Codex CLI(`codex`) 사용 스킬. 터미널에서 OpenAI 코딩 에이전트를 호출해 비대화형 작업(`codex exec`), 코드 리뷰(`codex review`), 세션 재개(`codex resume`), MCP 서버 등록(`codex mcp`)을 수행한다. 사용 시점: 사용자가 'codex', 'codex cli', 'codex exec', 'OpenAI 코덱스', 'GPT 로 한 번 더', 'codex 로 두 번째 의견', 'codex 로 리뷰', 'codex 로 패치', 'OpenAI 모델로 코딩 시켜', 'codex 백그라운드 실행', 'codex JSON 출력', 'codex 샌드박스', 'codex MCP 등록' 등을 언급할 때. Claude Code 자체 작업에 OpenAI 의 두 번째 시각을 더하거나, 별도 모델로 동일 작업을 비교할 때 반드시 이 스킬을 사용한다. 단, ChatGPT 웹/앱·OpenAI Responses API 직접 호출·gpt", "repo": "https://github.com/revfactory/skills/tree/HEAD/codex-cli", "install": ["npx skills add revfactory/skills --skill codex-cli"], "stars": 163, "pushed": "2026-10-12T08:12:18Z", "license": null, "plat": "codex-cli", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:fiddlecube/compliant-llm", "name": "compliant-llm", "kind": "mcp", "official": null, "desc": "Build Secure and Compliant AI agents and MCP Servers. YC W23", "repo": "https://github.com/fiddlecube/compliant-llm", "install": [], "stars": 163, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:deus-h/claudeus-wp-mcp", "name": "claudeus-wp-mcp", "kind": "mcp", "official": null, "desc": "Claudeus WordPress MCP Server", "repo": "https://github.com/deus-h/claudeus-wp-mcp", "install": [], "stars": 161, "pushed": null, "license": null, "plat": "wordpress", "live": "Not checked (runs locally)"}, {"id": "skill:yzlnew/infra-skills/slime-user", "name": "slime-user", "kind": "skill", "official": false, "desc": "Guide for using SLIME (LLM post-training framework for RL Scaling). Use when working with SLIME for reinforcement learning training of language models, including setup, configuration, training execution, multi-turn interactions, custom reward models, tool calling scenarios, or troubleshooting SLIME workflows. Covers GRPO, GSPO, PPO, Reinforce++, multi-agent RL, VLM training, FSDP/Megatron backends, SGLang integration, dynamic sampling, and custom generation functions.", "repo": "https://github.com/yzlnew/infra-skills/tree/HEAD/slime-user", "install": ["npx skills add yzlnew/infra-skills --skill slime-user"], "stars": 158, "pushed": "2026-07-09T09:35:22Z", "license": null, "plat": "slime", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:teunlao/swift-ai-sdk", "name": "swift-ai-sdk", "kind": "mcp", "official": null, "desc": "Unified AI SDK for Swift, bringing the power of Vercel AI SDK to Apple platforms", "repo": "https://github.com/teunlao/swift-ai-sdk", "install": [], "stars": 157, "pushed": null, "license": null, "plat": "swift", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:OpenDataMCP/OpenDataMCP", "name": "OpenDataMCP", "kind": "mcp", "official": null, "desc": "Connect any Open Data to any LLM with Model Context Protocol.", "repo": "https://github.com/OpenDataMCP/OpenDataMCP", "install": [], "stars": 156, "pushed": null, "license": null, "plat": "open data", "live": "Not checked (runs locally)"}, {"id": "skill:sundial-org/skills/skills/neuro-symbolic-reasoning", "name": "neuro-symbolic-reasoning", "kind": "skill", "official": false, "desc": "Neuro-symbolic AI combining LLMs with symbolic solvers. Use when exploring neuro-symbolic approaches (ideation, no code) or implementing solver integrations (code).", "repo": "https://github.com/sundial-org/skills/tree/HEAD/skills/neuro-symbolic-reasoning", "install": ["npx skills add sundial-org/skills --skill neuro-symbolic-reasoning"], "stars": 153, "pushed": "2026-07-15T23:46:34Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:pydantic-ai", "name": "pydantic-ai", "kind": "claude-plugin", "official": null, "desc": "Write accurate Pydantic AI code from the start. Up-to-date patterns, decision trees, and common gotchas for agents, tools, structured output, streaming, and multi-agent apps.", "repo": "https://github.com/pydantic/skills.git", "install": ["claude plugin install pydantic-ai@claude-plugins-official"], "stars": 153, "pushed": "2026-10-28T22:17:07Z", "license": "MIT", "plat": "pydantic", "live": "Not checked (runs locally)"}, {"id": "skill:sundial-org/skills/skills/tinker", "name": "tinker", "kind": "skill", "official": false, "desc": "Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, multimodal models including Thinking Machines' Inkling (vision, audio, thinking effort), and both high-level Cookbook patterns and low-level API usage.", "repo": "https://github.com/sundial-org/skills/tree/HEAD/skills/tinker", "install": ["npx skills add sundial-org/skills --skill tinker"], "stars": 153, "pushed": "2026-07-15T23:46:34Z", "license": null, "plat": "tinker", "live": "Not checked (runs locally)"}, {"id": "skill:sundial-org/skills/skills/tinker-training-cost", "name": "tinker-training-cost", "kind": "skill", "official": false, "desc": "Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.", "repo": "https://github.com/sundial-org/skills/tree/HEAD/skills/tinker-training-cost", "install": ["npx skills add sundial-org/skills --skill tinker-training-cost"], "stars": 153, "pushed": "2026-07-15T23:46:34Z", "license": null, "plat": "tinker", "live": "Not checked (runs locally)"}, {"id": "skill:sundial-org/skills/skills/training-data-curation", "name": "training-data-curation", "kind": "skill", "official": false, "desc": "Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.", "repo": "https://github.com/sundial-org/skills/tree/HEAD/skills/training-data-curation", "install": ["npx skills add sundial-org/skills --skill training-data-curation"], "stars": 153, "pushed": "2026-07-15T23:46:34Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:xiaonancs/codex-source-analysis", "name": "codex-source-analysis", "kind": "mcp", "official": null, "desc": "OpenAI Codex 源码深度研究 (Rust + TS + Python)：2 总纲 + 25 章 + 3 附录, ~51 万字 + 149 张 Mermaid 架构图", "repo": "https://github.com/xiaonancs/codex-source-analysis", "install": [], "stars": 152, "pushed": null, "license": null, "plat": "openai codex", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:LKbaba/Grok-mcp", "name": "Grok-mcp", "kind": "mcp", "official": null, "desc": "", "repo": "https://github.com/LKbaba/Grok-mcp", "install": [], "stars": 151, "pushed": null, "license": null, "plat": "grok", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.0xzr/freellmpool", "name": "freellmpool", "kind": "mcp", "official": null, "desc": "OpenAI-compatible MCP gateway cataloging 22 LLM providers, with routing, failover, and tools.", "repo": "https://github.com/0xzr/freellmpool", "install": ["pypi:freellmpool"], "stars": 149, "pushed": "2026-11-06T19:05:13Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "skill:avenoxai/avenoxskills/skills/limit", "name": "limit", "kind": "skill", "official": false, "desc": "Show Claude and Codex subscription usage windows in one colored screen, live where possible. Use when the user asks \"limit\", \"quota\", \"how much usage is left\", or before choosing which model/pool to delegate a task to.", "repo": "https://github.com/avenoxai/avenoxskills/tree/HEAD/skills/limit", "install": ["npx skills add avenoxai/avenoxskills --skill limit"], "stars": 149, "pushed": "2026-11-10T11:39:56Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:avenoxai/avenoxskills/skills/omp-fleet", "name": "omp-fleet", "kind": "skill", "official": false, "desc": "Standalone runner + fleet orchestrator for Oh My Pi (`omp`), a coding-agent CLI that reaches your existing OpenAI Codex subscription through a different harness. Does THREE things and always EXECUTES them (never just describes): (1) general code tasks via `omp -p`, (2) cheap high-volume context gathering on the 25x-cheaper model tier, and (3) parallel fan-out — both in-process subagents and multi-process lanes. Defaults locked: provider pinned to your own subscription, never a metered fallback. ", "repo": "https://github.com/avenoxai/avenoxskills/tree/HEAD/skills/omp-fleet", "install": ["npx skills add avenoxai/avenoxskills --skill omp-fleet"], "stars": 149, "pushed": "2026-11-10T11:39:56Z", "license": "MIT", "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/aicore-skills/skills/aicore-admin-resources", "name": "aicore-admin-resources", "kind": "skill", "official": false, "desc": "Verify and configure SAP AI Core credentials, retrieve auth tokens, and manage resource groups. WHEN: user wants to check if AI Core is set up, configure credentials, get a Bearer token, fix authentication errors, run a setup health check, or list, create, update, or delete resource groups. DO NOT USE FOR: managing deployments — use `aicore-lifecycle-management`; listing foundation models — use `genai-hub-foundation-models`.", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/aicore-skills/skills/aicore-admin-resources", "install": ["npx skills add SAP/ai-skills-library --skill aicore-admin-resources"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap ai core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/aicore-skills/skills/aicore-lifecycle-management", "name": "aicore-lifecycle-management", "kind": "skill", "official": false, "desc": "Manage SAP AI Core lifecycle resources: scenarios, executables, configurations, and deployments. WHEN: user wants to list or inspect scenarios or executables, create or list configurations, create or list or stop or delete or patch deployments, get deployment logs, or remove duplicate LLM deployments. DO NOT USE FOR: listing foundation models or getting inference examples — use `genai-hub-foundation-models`; managing resource groups, secrets, or GitOps resources — use `aicore-admin-resources`.", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/aicore-skills/skills/aicore-lifecycle-management", "install": ["npx skills add SAP/ai-skills-library --skill aicore-lifecycle-management"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap ai core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/skills/aicore-sdk-java", "name": "aicore-sdk-java", "kind": "skill", "official": false, "desc": "Generate working Java boilerplate for SAP GenAI Hub using the SAP AI SDK for Java (com.sap.ai.sdk). Covers chat completion, streaming, multi-turn conversations, prompt templates, content filtering, data masking, embeddings, RAG/grounding, prompt registry, and Spring Boot streaming. WHEN: user asks for Java or Kotlin code that calls a GenAI Hub model, wants a Java starter project, asks how to use com.sap.ai.sdk in their app, or asks how to integrate AI Core into a Spring Boot or plain Java applic", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/skills/aicore-sdk-java", "install": ["npx skills add SAP/ai-skills-library --skill aicore-sdk-java"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap genai hub", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/skills/aicore-sdk-js", "name": "aicore-sdk-js", "kind": "skill", "official": false, "desc": "Generate working TypeScript/JavaScript boilerplate for SAP GenAI Hub using @sap-ai-sdk/orchestration, @sap-ai-sdk/foundation-models, @sap-ai-sdk/openai (native OpenAI SDK), @sap-ai-sdk/langchain (LangChain), @sap-ai-sdk/document-grounding (RAG), @sap-ai-sdk/prompt-registry, @sap-ai-sdk/llm-batch (batch jobs), @sap-ai-sdk/rpt (tabular prediction), or @sap-ai-sdk/context-registry (data destinations, tabular artifacts, scenario configurations). WHEN: user asks for TypeScript, JavaScript, Node.js, o", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/skills/aicore-sdk-js", "install": ["npx skills add SAP/ai-skills-library --skill aicore-sdk-js"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap genai hub", "live": "Not checked (runs locally)"}, {"id": "skill:replicate/skills/skills/build-models", "name": "build-models", "kind": "skill", "official": false, "desc": "Package and build custom AI models with Cog for deployment on Replicate. Use when creating a cog.yaml or predict.py, defining model inputs and outputs, loading model weights at setup time, building Docker images for ML models, serving locally with cog serve or cog predict, or porting a HuggingFace, GitHub, or ComfyUI model to run on Replicate. Trigger on phrases like \"build a model\", \"package a model\", \"create a Cog model\", \"wrap a model\", \"containerize an AI model\", \"predict.py\", \"cog.yaml\", \"B", "repo": "https://github.com/replicate/skills/tree/HEAD/skills/build-models", "install": ["npx skills add replicate/skills --skill build-models"], "stars": 148, "pushed": "2026-06-04T17:06:37Z", "license": "Apache-2.0", "plat": "replicate", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/clean-core-skills/skills/clean-core-data", "name": "clean-core-data", "kind": "skill", "official": false, "desc": "Use this skill when the user asks questions about SAP Clean Core Data — including what clean core data is, the five data pillars (data strategy, data governance, data quality, data volume efficiency, data protection), master data management with SAP Master Data Governance, data quality dimensions and the Data Quality Index, data profiling with SAP Information Steward, data volume efficiency and tiering, data lifecycle management and archiving with SAP ILM, data protection principles, SAP Busines", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/clean-core-skills/skills/clean-core-data", "install": ["npx skills add SAP/ai-skills-library --skill clean-core-data"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap clean core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/clean-core-skills/skills/clean-core-extensibility", "name": "clean-core-extensibility", "kind": "skill", "official": false, "desc": "Use this skill when the user asks questions about SAP Clean Core Extensibility — including extension levels (A–D), on-stack vs. side-by-side extensibility, ABAP Cloud development model, ATC setup, KPI measurement (clean core share, technical debt score, unused code share, business modifications), Governance & Maturity Assessment practices (EXT-GOV-01 through EXT-SYS-05), RISE with SAP Methodology, Project Kernseife, the SAP API Policy, SAP Build and Joule Studio, or Get Clean / Stay Clean patter", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/clean-core-skills/skills/clean-core-extensibility", "install": ["npx skills add SAP/ai-skills-library --skill clean-core-extensibility"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap clean core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/clean-core-skills/skills/clean-core-extensibility-atc-assessment", "name": "clean-core-extensibility-atc-assessment", "kind": "skill", "official": false, "desc": "Generates a Clean Core ATC Assessment Dashboard from an SAP ATC findings ZIP export. Produces a self-contained HTML report in SAP Horizon look & feel covering: Technical Debt Score, Potential Score (successor-aware), Clean Core Level Distribution (Level B/C/D per object using worst-finding logic), top objects and packages by debt score, top objects by Level D (P1 error) findings, top referenced SAP objects by impact, and per-package top-3 referenced SAP objects. Use this skill whenever the user ", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/clean-core-skills/skills/clean-core-extensibility-atc-assessment", "install": ["npx skills add SAP/ai-skills-library --skill clean-core-extensibility-atc-assessment"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap clean core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/clean-core-skills/skills/clean-core-general", "name": "clean-core-general", "kind": "skill", "official": false, "desc": "Use this skill when the user asks broad or introductory questions about SAP Clean Core — including what clean core is, why it matters, the five principles (Extensibility, Integration, Data, Business Processes, Operations), the Autonomous Enterprise vision, RISE with SAP Methodology, general KPI measurement across all principles, the cross-principle Governance & Maturity Assessment, how to start a clean core journey, or how to explain clean core to stakeholders. Also use when the user asks about ", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/clean-core-skills/skills/clean-core-general", "install": ["npx skills add SAP/ai-skills-library --skill clean-core-general"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap clean core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/clean-core-skills/skills/clean-core-integration", "name": "clean-core-integration", "kind": "skill", "official": false, "desc": "Use this skill when the user asks questions about SAP Clean Core Integration — including what clean core integration is, the four integration levels A–D, the three architectural patterns (SAP Integration Suite, API-managed connectivity, event-driven architecture), the SAP API policy, integration design guidelines, the ICoE, the SSB, SAP ISAM, stay-clean and get-clean journeys, modernization of IDocs/RFCs/BAPIs, SAP LeanIX as integration repository, end-to-end monitoring with SAP Cloud ALM and AI", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/clean-core-skills/skills/clean-core-integration", "install": ["npx skills add SAP/ai-skills-library --skill clean-core-integration"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap clean core", "live": "Not checked (runs locally)"}, {"id": "skill:replicate/skills/skills/compare-models", "name": "compare-models", "kind": "skill", "official": false, "desc": "Compare Replicate models by cost, speed, quality, and capabilities.", "repo": "https://github.com/replicate/skills/tree/HEAD/skills/compare-models", "install": ["npx skills add replicate/skills --skill compare-models"], "stars": 148, "pushed": "2026-06-04T17:06:37Z", "license": "Apache-2.0", "plat": "replicate", "live": "Not checked (runs locally)"}, {"id": "skill:replicate/skills/skills/find-models", "name": "find-models", "kind": "skill", "official": false, "desc": "Find AI models on Replicate using search and curated collections.", "repo": "https://github.com/replicate/skills/tree/HEAD/skills/find-models", "install": ["npx skills add replicate/skills --skill find-models"], "stars": 148, "pushed": "2026-06-04T17:06:37Z", "license": "Apache-2.0", "plat": "replicate", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/aicore-skills/skills/genai-hub-foundation-models", "name": "genai-hub-foundation-models", "kind": "skill", "official": false, "desc": "List and explore foundation models available in SAP AI Core GenAI Hub: browse supported providers, model names, versions, capabilities, and executable IDs needed to create deployments. WHEN: user wants to see what models are available, find an executable-id or model-name for deployment, browse providers (Azure OpenAI, Anthropic, Google, Mistral, Cohere, etc.), check model capabilities or context length, get the correct model-name to pass to create_llm_deployment, or get curl inference examples f", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/aicore-skills/skills/genai-hub-foundation-models", "install": ["npx skills add SAP/ai-skills-library --skill genai-hub-foundation-models"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap ai core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/aicore-skills/skills/genai-hub-orchestration", "name": "genai-hub-orchestration", "kind": "skill", "official": false, "desc": "Configure and use the SAP AI Core Orchestration service: build prompt templates, add content filtering, set up document grounding (RAG) pipelines, and call models via the Harmonized API. WHEN: user asks about the orchestration service, Harmonized API, prompt templating with variables, content filtering, grounding, RAG pipeline, document grounding, or building a pipeline that wraps an LLM. DO NOT USE FOR: creating an orchestration deployment — use `aicore-lifecycle-management` for that; credentia", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/aicore-skills/skills/genai-hub-orchestration", "install": ["npx skills add SAP/ai-skills-library --skill genai-hub-orchestration"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap ai core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/aicore-skills/skills/genai-hub-prompt-evaluation", "name": "genai-hub-prompt-evaluation", "kind": "skill", "official": false, "desc": "Run GenAI evaluations on SAP AI Core: benchmark LLMs and prompts against a test dataset using system-defined and custom (LLM-as-judge) metrics, then retrieve aggregate results. Supports all three evaluation types — prompt registry, orchestration registry, and grounding (RAG) orchestration registry. WHEN: user wants to evaluate or benchmark an LLM / prompt / orchestration config, run a GenAI evaluation, create or list evaluation metrics, define a custom LLM-as-a-judge metric, create a prompt temp", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/aicore-skills/skills/genai-hub-prompt-evaluation", "install": ["npx skills add SAP/ai-skills-library --skill genai-hub-prompt-evaluation"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap ai core", "live": "Not checked (runs locally)"}, {"id": "skill:SAP/ai-skills-library/plugins/aicore-skills/skills/genai-hub-tabular-orchestration", "name": "genai-hub-tabular-orchestration", "kind": "skill", "official": false, "desc": "Manage the SAP AI Core Context Registry — data destinations (HDL connections), tabular artifacts (CSV/Parquet/Delta files as queryable tables), and scenario configurations (named artifact bundles). Also interactively build and send tabular prediction requests using the SAP AI Core Tabular Prediction service (sap-rpt-1-small / sap-rpt-1-large). Use this skill whenever the user mentions context registry, data destinations, tabular artifacts, scenario configurations, TCR, HDL connections, or wants ", "repo": "https://github.com/SAP/ai-skills-library/tree/HEAD/plugins/aicore-skills/skills/genai-hub-tabular-orchestration", "install": ["npx skills add SAP/ai-skills-library --skill genai-hub-tabular-orchestration"], "stars": 148, "pushed": "2026-11-11T10:28:17Z", "license": "Apache-2.0", "plat": "sap ai core", "live": "Not checked (runs locally)"}, {"id": "skill:replicate/skills/skills/prompt-images", "name": "prompt-images", "kind": "skill", "official": false, "desc": "Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features.", "repo": "https://github.com/replicate/skills/tree/HEAD/skills/prompt-images", "install": ["npx skills add replicate/skills --skill prompt-images"], "stars": 148, "pushed": "2026-06-04T17:06:37Z", "license": "Apache-2.0", "plat": "replicate", "live": "Not checked (runs locally)"}, {"id": "skill:replicate/skills/skills/prompt-videos", "name": "prompt-videos", "kind": "skill", "official": false, "desc": "Prompting techniques for AI video generation models on Replicate. Use when writing prompts for video models or building video generation features.", "repo": "https://github.com/replicate/skills/tree/HEAD/skills/prompt-videos", "install": ["npx skills add replicate/skills --skill prompt-videos"], "stars": 148, "pushed": "2026-06-04T17:06:37Z", "license": "Apache-2.0", "plat": "replicate", "live": "Not checked (runs locally)"}, {"id": "skill:replicate/skills/skills/publish-models", "name": "publish-models", "kind": "skill", "official": false, "desc": "Push and publish custom AI models to Replicate, and set up CI/CD for releasing new model versions safely. Use when running cog push, deploying a model to Replicate, releasing a new version, validating a model with cog-safe-push before publishing, configuring a Replicate deployment, setting up GitHub Actions for model releases, or porting a community model to an official one. Trigger on phrases like \"push a model to Replicate\", \"publish a model\", \"deploy a model\", \"release a new version\", \"cog pu", "repo": "https://github.com/replicate/skills/tree/HEAD/skills/publish-models", "install": ["npx skills add replicate/skills --skill publish-models"], "stars": 148, "pushed": "2026-06-04T17:06:37Z", "license": "Apache-2.0", "plat": "replicate", "live": "Not checked (runs locally)"}, {"id": "skill:replicate/skills/skills/run-models", "name": "run-models", "kind": "skill", "official": false, "desc": "Run AI models on Replicate via predictions, webhooks, and streaming.", "repo": "https://github.com/replicate/skills/tree/HEAD/skills/run-models", "install": ["npx skills add replicate/skills --skill run-models"], "stars": 148, "pushed": "2026-06-04T17:06:37Z", "license": "Apache-2.0", "plat": "replicate", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:lacvietanh/aki-mcp-sv", "name": "aki-mcp-sv", "kind": "mcp", "official": null, "desc": "Custom MCP [Connector] for AI chat web [Claude|ChatGPT|Grok...] use Files/Shell on your computer directly", "repo": "https://github.com/lacvietanh/aki-mcp-sv", "install": [], "stars": 145, "pushed": null, "license": null, "plat": "claude|chatgpt|grok", "live": "Not checked (runs locally)"}, {"id": "skill:jangviktor-web/likeskill", "name": "like-perspective", "kind": "skill", "official": false, "desc": "李可老中医（1930-2013）思维操作系统。基于13个来源（11本著作+2篇访谈）蒸馏， 含423个医案、120+首方剂、92种症状路由、24种假证识别。 核心能力：急危重症救治（心衰/中风/休克）、经方剂量体系、六经辨证、假证识别。 触发词：「李可」「用李可的视角」「李可会怎么看」「心衰怎么急救」「附子用量」「经方剂量」「假阳证」「破格救心汤」「六经辨证」。 注意：大剂量用药需在有经验的中医师指导下使用，本skill仅供学习参考。", "repo": "https://github.com/jangviktor-web/likeskill", "install": ["npx skills add jangviktor-web/likeskill --skill like-perspective"], "stars": 145, "pushed": "2026-08-23T01:20:02Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:com.skillmd/skillmd", "name": "com.skillmd/skillmd", "kind": "mcp", "official": null, "desc": "Search, fetch, lint, and install Agent Skills (SKILL.md) from the SkillMD registry.", "repo": "https://github.com/skillmds/SkillMD", "install": ["npm:get-skill", "https://api.skillmd.com/mcp"], "stars": 144, "pushed": "2026-11-10T10:56:53Z", "license": "MIT", "plat": "skillmd", "live": "Not checked (runs locally)"}, {"id": "skill:isjiamu/jiamu-skills/sales-ai-assistant", "name": "sales-ai-assistant", "kind": "skill", "official": false, "desc": "帮助销售人员将业务场景转化为AI提示词并自动生成销售内容（邮件、方案、分析等），无需学习如何编写提示词", "repo": "https://github.com/isjiamu/jiamu-skills/tree/HEAD/sales-ai-assistant", "install": ["npx skills add isjiamu/jiamu-skills --skill sales-ai-assistant"], "stars": 143, "pushed": "2026-07-14T04:13:21Z", "license": "MIT", "plat": "sales", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:centminmod/gemini-cli-mcp-server", "name": "gemini-cli-mcp-server", "kind": "mcp", "official": null, "desc": "", "repo": "https://github.com/centminmod/gemini-cli-mcp-server", "install": [], "stars": 142, "pushed": null, "license": null, "plat": "gemini", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:LKbaba/Gemini-mcp", "name": "Gemini-mcp", "kind": "mcp", "official": null, "desc": "MCP server implementation for Google's Gemini API", "repo": "https://github.com/LKbaba/Gemini-mcp", "install": [], "stars": 141, "pushed": null, "license": null, "plat": "google's gemini api", "live": "Not checked (runs locally)"}, {"id": "skill:Mark393295827/third-brain-v7-skills/skills/cognitive-compile", "name": "cognitive-compile", "kind": "skill", "official": false, "desc": "Use when source material must be transformed into a compact, evidence-aware model for learning, decisions, or an Obsidian concept note.", "repo": "https://github.com/Mark393295827/third-brain-v7-skills/tree/HEAD/skills/cognitive-compile", "install": ["npx skills add Mark393295827/third-brain-v7-skills --skill cognitive-compile"], "stars": 140, "pushed": "2026-09-19T09:08:54Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:sugarforever/01coder-agent-skills/skills/codex-cli", "name": "codex-cli", "kind": "skill", "official": false, "desc": "Use when an agent needs to delegate a task to the OpenAI Codex CLI from another agent environment such as Claude Code, OpenClaw, or similar. Covers checking whether Codex CLI is installed, running one-off Codex prompts with `codex exec`, resuming sessions, collecting outputs, attaching images or files as input with `-i`/stdin, and handling Codex image generation including finding and reporting generated image file paths.", "repo": "https://github.com/sugarforever/01coder-agent-skills/tree/HEAD/skills/codex-cli", "install": ["npx skills add sugarforever/01coder-agent-skills --skill codex-cli"], "stars": 139, "pushed": "2026-10-19T23:56:46Z", "license": "MIT", "plat": "openai codex", "live": "Not checked (runs locally)"}, {"id": "skill:black-forest-labs/skills/skills/bfl-api", "name": "bfl-api", "kind": "skill", "official": false, "desc": "BFL FLUX API integration guide covering endpoints, async polling patterns, rate limiting, error handling, webhooks, and regional endpoints with Python and TypeScript code examples.", "repo": "https://github.com/black-forest-labs/skills/tree/HEAD/skills/bfl-api", "install": ["npx skills add black-forest-labs/skills --skill bfl-api"], "stars": 137, "pushed": "2026-09-09T20:54:01Z", "license": "MIT", "plat": "bfl flux", "live": "Not checked (runs locally)"}, {"id": "skill:alint-dev/alint/.agents/skills/xsai", "name": "xsai", "kind": "skill", "official": false, "desc": "Use this skill when the user is building with `xsai` or any `@xsai/*` package, or is evaluating xsAI for a small OpenAI-compatible workflow with text generation, streaming, tool calling, structured output, embeddings, image generation, speech synthesis, or transcription.", "repo": "https://github.com/alint-dev/alint/tree/HEAD/.agents/skills/xsai", "install": ["npx skills add alint-dev/alint --skill xsai"], "stars": 137, "pushed": "2026-11-06T18:43:40Z", "license": "MIT", "plat": "xsai", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.lawrence3699/jev-style", "name": "Jev-Style", "kind": "mcp", "official": null, "desc": "Local 2B and 0.8B decision models: calibrated yes/no, choice and score answers for your agent.", "repo": "https://github.com/lawrence3699/jev-style", "install": ["pypi:jev-style"], "stars": 134, "pushed": "2026-11-04T22:14:36Z", "license": "Apache-2.0", "plat": "jev-style", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:mclenhard/mcp-evals", "name": "mcp-evals", "kind": "mcp", "official": null, "desc": "A Node.js package and GitHub Action for evaluating MCP (Model Context Protocol) tool implementations using LLM-based scoring. This helps ensure your MCP server's tools are working correctly and performing well.", "repo": "https://github.com/mclenhard/mcp-evals", "install": [], "stars": 134, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:appautomaton/agent-designer/skills/collaborating-with-antigravity", "name": "collaborating-with-antigravity", "kind": "skill", "official": false, "desc": "Delegate tasks to Google's Antigravity CLI (agy) for prototyping, debugging, code review, and research. Supports multi-turn sessions via SESSION_ID. Successor to the retired Gemini CLI.", "repo": "https://github.com/appautomaton/agent-designer/tree/HEAD/skills/collaborating-with-antigravity", "install": ["npx skills add appautomaton/agent-designer --skill collaborating-with-antigravity"], "stars": 133, "pushed": "2026-09-13T03:35:57Z", "license": null, "plat": "antigravity", "live": "Not checked (runs locally)"}, {"id": "skill:appautomaton/agent-designer/skills/collaborating-with-claude", "name": "collaborating-with-claude", "kind": "skill", "official": false, "desc": "Delegate tasks to Claude Code CLI for prototyping, debugging, and code review. Supports multi-turn sessions via SESSION_ID.", "repo": "https://github.com/appautomaton/agent-designer/tree/HEAD/skills/collaborating-with-claude", "install": ["npx skills add appautomaton/agent-designer --skill collaborating-with-claude"], "stars": 133, "pushed": "2026-09-13T03:35:57Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:appautomaton/agent-designer/skills/collaborating-with-codex", "name": "collaborating-with-codex", "kind": "skill", "official": false, "desc": "Delegate tasks to Codex CLI for prototyping, debugging, code review, implementation handoff, cross-model second opinions, and multi-turn Codex sessions via SESSION_ID.", "repo": "https://github.com/appautomaton/agent-designer/tree/HEAD/skills/collaborating-with-codex", "install": ["npx skills add appautomaton/agent-designer --skill collaborating-with-codex"], "stars": 133, "pushed": "2026-09-13T03:35:57Z", "license": null, "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "skill:appautomaton/agent-designer/skills/collaborating-with-grok", "name": "collaborating-with-grok", "kind": "skill", "official": false, "desc": "Delegate tasks to Grok CLI (xAI) for prototyping, debugging, code review, research, and cross-model second opinions. Supports multi-turn sessions via SESSION_ID.", "repo": "https://github.com/appautomaton/agent-designer/tree/HEAD/skills/collaborating-with-grok", "install": ["npx skills add appautomaton/agent-designer --skill collaborating-with-grok"], "stars": 133, "pushed": "2026-09-13T03:35:57Z", "license": null, "plat": "grok", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ehmo/gum", "name": "gum", "kind": "mcp", "official": null, "desc": "CLI and MCP server for discovering and calling Google APIs from agents and scripts.", "repo": "https://github.com/ehmo/gum", "install": [], "stars": 133, "pushed": null, "license": null, "plat": "google apis", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:MiniMax-AI/MiniMax-MCP-JS", "name": "MiniMax-MCP-JS", "kind": "mcp", "official": null, "desc": "Official MiniMax Model Context Protocol (MCP) JavaScript implementation that provides seamless integration with MiniMax's powerful AI capabilities including image generation, video generation, text-to-speech, and voice cloning APIs.", "repo": "https://github.com/MiniMax-AI/MiniMax-MCP-JS", "install": [], "stars": 132, "pushed": null, "license": null, "plat": "minimax", "live": "Not checked (runs locally)"}, {"id": "skill:Drix10/ai-resources/skills/ai-news", "name": "ai-news", "kind": "skill", "official": false, "desc": "Answers \"what's new in AI\", \"latest on <model/company/topic>\", \"AI news this week\", \"who raised\" and similar questions about recent AI models, companies, funding rounds, tools and research, using the AI Resources daily briefings (blogs.drix10.com, CC BY 4.0) with cited sources. Use whenever the user asks for recent AI news or developments.", "repo": "https://github.com/Drix10/ai-resources/tree/HEAD/skills/ai-news", "install": ["npx skills add Drix10/ai-resources --skill ai-news"], "stars": 130, "pushed": "2026-11-12T10:07:28Z", "license": "CC-BY-4.0", "plat": "ai resources", "live": "Not checked (runs locally)"}, {"id": "skill:apollographql/skills/skills/apollo-server", "name": "apollo-server", "kind": "skill", "official": false, "desc": "Guide for building GraphQL servers with Apollo Server 5.x. Use this skill when: (1) setting up a new Apollo Server project, (2) writing resolvers or defining GraphQL schemas, (3) implementing authentication or authorization, (4) creating plugins or custom data sources, (5) troubleshooting Apollo Server errors or performance issues.", "repo": "https://github.com/apollographql/skills/tree/HEAD/skills/apollo-server", "install": ["npx skills add apollographql/skills --skill apollo-server"], "stars": 130, "pushed": "2026-11-06T10:59:43Z", "license": "MIT", "plat": "graphql", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:thtskaran/claude-code-analysis", "name": "claude-code-analysis", "kind": "mcp", "official": null, "desc": "We read all 512K lines of Claude Code's accidentally exposed source. 82 docs, 15 diagrams, every subsystem mapped — from the hidden YOLO safety classifier to multi-agent swarms.", "repo": "https://github.com/thtskaran/claude-code-analysis", "install": [], "stars": 130, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:apollographql/skills/skills/graphql-operations", "name": "graphql-operations", "kind": "skill", "official": false, "desc": "Guide for writing GraphQL operations (queries, mutations, fragments) following best practices. Use this skill when: (1) writing GraphQL queries or mutations, (2) organizing operations with fragments, (3) optimizing data fetching patterns, (4) setting up type generation or linting, (5) reviewing operations for efficiency.", "repo": "https://github.com/apollographql/skills/tree/HEAD/skills/graphql-operations", "install": ["npx skills add apollographql/skills --skill graphql-operations"], "stars": 130, "pushed": "2026-11-06T10:59:43Z", "license": "MIT", "plat": "graphql", "live": "Not checked (runs locally)"}, {"id": "skill:apollographql/skills/skills/graphql-schema", "name": "graphql-schema", "kind": "skill", "official": false, "desc": "Guide for designing and changing GraphQL schemas following industry best practices. Use this skill whenever you create or edit GraphQL SDL, such as a `schema.graphql` file or `typeDefs`, including when: (1) designing a new GraphQL schema or API, (2) adding or changing types, fields, arguments, mutations, or descriptions in an existing schema, (3) reviewing existing schema for improvements, (4) deciding on type structures or nullability, (5) implementing pagination or error patterns, (6) ensuring", "repo": "https://github.com/apollographql/skills/tree/HEAD/skills/graphql-schema", "install": ["npx skills add apollographql/skills --skill graphql-schema"], "stars": 130, "pushed": "2026-11-06T10:59:43Z", "license": "MIT", "plat": "graphql", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:DannyMac180/mcp-think-tool", "name": "mcp-think-tool", "kind": "mcp", "official": null, "desc": "An MCP server implementing the think tool for Claude", "repo": "https://github.com/DannyMac180/mcp-think-tool", "install": [], "stars": 130, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:cnemri/google-genai-skills/skills/google-genai-sdk-python", "name": "google-genai-sdk-python", "kind": "skill", "official": false, "desc": "Expert guidance for writing Python code using the official Google GenAI SDK (google-genai) for Gemini API and Vertex AI. Use for text generation, multimodal inputs, reasoning, tools, and media generation.", "repo": "https://github.com/cnemri/google-genai-skills/tree/HEAD/skills/google-genai-sdk-python", "install": ["npx skills add cnemri/google-genai-skills --skill google-genai-sdk-python"], "stars": 129, "pushed": "2026-02-06T21:44:44Z", "license": "MIT", "plat": "google", "live": "Not checked (runs locally)"}, {"id": "skill:gnurio/nurijanian-skills/skills/verbalized-sampling", "name": "verbalized-sampling", "kind": "skill", "official": false, "desc": "Generate diverse outputs by prompting for a probability distribution instead of a single response. Implements Verbalized Sampling (VS) from Zhang et al. 2025 — a training-free technique that counteracts LLM mode collapse caused by typicality bias in alignment data. Use when the task needs genuine diversity: creative writing, brainstorming/ideation, synthetic data generation, persona/dialogue simulation, adversarial examples, open-ended QA with multiple valid answers, or any situation where \"gene", "repo": "https://github.com/gnurio/nurijanian-skills/tree/HEAD/skills/verbalized-sampling", "install": ["npx skills add gnurio/nurijanian-skills --skill verbalized-sampling"], "stars": 129, "pushed": "2026-08-13T10:40:06Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:smartcontractkit/chainlink-agent-skills/chainlink-confidential-ai-attester-skill", "name": "chainlink-confidential-ai-attester-skill", "kind": "skill", "official": false, "desc": "Runs LLM inference over a private document inside a TEE, returns an attested result, and keeps the raw document in the TEE. No document upload means chainlink-cre-skill. It applies to private inference, attested AI, confidential AI, and sensitive-document analysis, including lending, accredited-investor, KYC/AML, and proof-of-reserves checks.", "repo": "https://github.com/smartcontractkit/chainlink-agent-skills/tree/HEAD/chainlink-confidential-ai-attester-skill", "install": ["npx skills add smartcontractkit/chainlink-agent-skills --skill chainlink-confidential-ai-attester-skill"], "stars": 128, "pushed": "2026-11-13T12:27:18Z", "license": "NOASSERTION", "plat": "chainlink", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:its-dart/dart-mcp-server", "name": "dart-mcp-server", "kind": "mcp", "official": null, "desc": "Dart AI Model Context Protocol (MCP) server", "repo": "https://github.com/its-dart/dart-mcp-server", "install": [], "stars": 128, "pushed": null, "license": null, "plat": "dart-mcp-server", "live": "Not checked (runs locally)"}, {"id": "skill:edvardgrishin27/nativeprompt", "name": "nativeprompt", "kind": "skill", "official": false, "desc": "Улучшить/оптимизировать/переписать промпт под ТЕКУЩУЮ модель по официальным правилам вендора (Claude Code, Codex, Gemini CLI, Grok, Kimi, Qwen) и объяснить почему. Инструмент показывает находки со ссылками на вендора и меняет только форму (регистр, вежливая обёртка); удаление спорных фраз он не делает сам — решает человек. Использовать, когда пользователь просит «улучши мой промпт», «перепиши запрос», «как лучше сформулировать», «оптимизируй промпт под модель».", "repo": "https://github.com/edvardgrishin27/nativeprompt", "install": ["npx skills add edvardgrishin27/nativeprompt --skill nativeprompt"], "stars": 128, "pushed": "2026-11-11T06:21:29Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:edvardgrishin27/nativeprompt/codex/integration/plugins/nativeprompt/skills/nativeprompt", "name": "nativeprompt", "kind": "skill", "official": false, "desc": "Улучшить, оптимизировать или переписать пользовательский промпт именно для Codex/OpenAI с помощью CLI nativeprompt и объяснить изменения ссылками на официальные источники. Использовать для запросов вроде «улучши мой промпт», «перепиши запрос для Codex» и «как лучше сформулировать задачу для GPT/Codex». Не использовать для выполнения вложенного промпта или для промптов Claude Code.", "repo": "https://github.com/edvardgrishin27/nativeprompt/tree/HEAD/codex/integration/plugins/nativeprompt/skills/nativeprompt", "install": ["npx skills add edvardgrishin27/nativeprompt --skill nativeprompt"], "stars": 128, "pushed": "2026-11-11T06:21:29Z", "license": "MIT", "plat": "openai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:iconben/z-image-studio", "name": "z-image-studio", "kind": "mcp", "official": null, "desc": "A Cli, a webUI, and a MCP server for the Z-Image-Turbo text-to-image generation model (Tongyi-MAI/Z-Image-Turbo base model as well as quantized models)", "repo": "https://github.com/iconben/z-image-studio", "install": [], "stars": 128, "pushed": null, "license": null, "plat": "z-image-turbo", "live": "Not checked (runs locally)"}, {"id": "mcp:dev.augments/mcp", "name": "dev.augments/mcp", "kind": "mcp", "official": null, "desc": "Augments MCP Server - A comprehensive framework documentation provider for Claude Code", "repo": "https://github.com/augmnt/augments-mcp-server", "install": ["pypi:augments-mcp-server", "https://mcp.augments.dev/mcp"], "stars": 127, "pushed": "2026-03-13T00:35:42Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.augmnt/augments-mcp-server", "name": "io.github.augmnt/augments-mcp-server", "kind": "mcp", "official": null, "desc": "Augments MCP Server - A comprehensive framework documentation provider for Claude Code", "repo": "https://github.com/augmnt/augments-mcp-server", "install": ["pypi:augments-mcp-server"], "stars": 127, "pushed": "2026-03-13T00:35:42Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:avibebuilder/claude-prime/.claude/skills/optimus-prime", "name": "optimus-prime", "kind": "skill", "official": false, "desc": "Generates a Claude Code configuration tailored to a specific project. Use whenever the user wants to prime a project, set up claude for a repo, bootstrap claude config, or re-prime/refresh an already-primed project (for example after `/prime-sync` pulled new starter content). Triggers on 'prime', 'prime this project', 'optimus-prime', 're-prime', 'refresh claude config', 'regenerate CLAUDE.md', 'set up claude for this repo'. Deeply analyzes the real codebase and builds project-specific skills, r", "repo": "https://github.com/avibebuilder/claude-prime/tree/HEAD/.claude/skills/optimus-prime", "install": ["npx skills add avibebuilder/claude-prime --skill optimus-prime"], "stars": 125, "pushed": "2026-05-16T19:59:38Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:avibebuilder/claude-prime/.claude/skills/prime-sync", "name": "prime-sync", "kind": "skill", "official": false, "desc": "Syncs Claude config between prime repo and target projects. Trigger on 'sync', 'prime-sync', 'push config', 'pull config', 'update prime', 'sync claude config'. Push mode deploys to targets; pull mode imports changes back.", "repo": "https://github.com/avibebuilder/claude-prime/tree/HEAD/.claude/skills/prime-sync", "install": ["npx skills add avibebuilder/claude-prime --skill prime-sync"], "stars": 125, "pushed": "2026-05-16T19:59:38Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:avibebuilder/claude-prime/.claude/skills/skill-creator", "name": "skill-creator", "kind": "skill", "official": false, "desc": "Use when the user wants to work on a Claude Code skill file (SKILL.md): writing one from scratch, testing whether an existing one works well, running evals or benchmarks, improving its instructions, or fixing why it isn't triggering. Triggers on: 'make a skill for X', 'test this skill', 'run evals on my SKILL.md', 'touch-skill', sharing a SKILL.md and asking if it's ready to ship. The key signal is intent to create, validate, or improve a skill — not just mention one. Do NOT trigger for general ", "repo": "https://github.com/avibebuilder/claude-prime/tree/HEAD/.claude/skills/skill-creator", "install": ["npx skills add avibebuilder/claude-prime --skill skill-creator"], "stars": 125, "pushed": "2026-05-16T19:59:38Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:augmentedstartups/VisionCraft-MCP-Server", "name": "VisionCraft-MCP-Server", "kind": "mcp", "official": null, "desc": "VisionCraft MCP delivers up-to-date, specialized computer vision and Gen-AI knowledge directly to Claude and other AI assistants.", "repo": "https://github.com/augmentedstartups/VisionCraft-MCP-Server", "install": [], "stars": 125, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-adapters-multimodal", "name": "dspy-adapters-multimodal", "kind": "skill", "official": false, "desc": "Use for DSPy adapter selection, JSONAdapter, XMLAdapter, ChatAdapter, native function calling, structured outputs, and multimodal inputs like dspy.Image or dspy.Audio.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-adapters-multimodal", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-adapters-multimodal"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-advanced-module-composition", "name": "dspy-advanced-module-composition", "kind": "skill", "official": false, "desc": "Use for composing DSPy modules with Ensemble, MultiChainComparison, ensemble voting, sequential pipelines, and multi-program workflows.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-advanced-module-composition", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-advanced-module-composition"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-better-together", "name": "dspy-better-together", "kind": "skill", "official": false, "desc": "Use for BetterTogether, prompt plus weight optimization, fine-tuning sequences, and strategy chains like p -> w -> p.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-better-together", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-better-together"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-bootstrap-fewshot", "name": "dspy-bootstrap-fewshot", "kind": "skill", "official": false, "desc": "Use for BootstrapFewShot, bootstrapped demonstrations, teacher-model demos, and low-data DSPy prompt optimization.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-bootstrap-fewshot", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-bootstrap-fewshot"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-custom-module-design", "name": "dspy-custom-module-design", "kind": "skill", "official": false, "desc": "Use for creating custom DSPy modules, extending dspy.Module, reusable components, stateful modules, serialization, and module testing.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-custom-module-design", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-custom-module-design"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-debugging-observability", "name": "dspy-debugging-observability", "kind": "skill", "official": false, "desc": "Use for debugging DSPy programs, inspect_history, tracing LLM calls, custom callbacks, observability, monitoring, and cost tracking.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-debugging-observability", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-debugging-observability"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-embedding-retrieval", "name": "dspy-embedding-retrieval", "kind": "skill", "official": false, "desc": "Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-embedding-retrieval", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-embedding-retrieval"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-evaluation-suite", "name": "dspy-evaluation-suite", "kind": "skill", "official": false, "desc": "Use for evaluating DSPy programs with Evaluate, answer_exact_match, SemanticF1, custom metrics, baselines, and program comparisons.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-evaluation-suite", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-evaluation-suite"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-finetune-bootstrap", "name": "dspy-finetune-bootstrap", "kind": "skill", "official": false, "desc": "Use for BootstrapFinetune, fine-tuning DSPy models, teacher-student distillation, weight optimization, and lower-cost deployment.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-finetune-bootstrap", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-finetune-bootstrap"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-gepa-reflective", "name": "dspy-gepa-reflective", "kind": "skill", "official": false, "desc": "Use for GEPA reflective optimization, ReAct agent optimization, feedback metrics, LLM reflection, and execution trajectories.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-gepa-reflective", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-gepa-reflective"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-haystack-integration", "name": "dspy-haystack-integration", "kind": "skill", "official": false, "desc": "Use for integrating DSPy with Haystack, optimizing Haystack prompts, improving retrieval pipelines, and extracting DSPy prompts.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-haystack-integration", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-haystack-integration"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-mcp-tool-integration", "name": "dspy-mcp-tool-integration", "kind": "skill", "official": false, "desc": "Use for MCP tools with DSPy, Model Context Protocol servers, dspy.Tool.from_mcp_tool, and ReAct agents over MCP-compatible tools.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-mcp-tool-integration", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-mcp-tool-integration"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-miprov2-optimizer", "name": "dspy-miprov2-optimizer", "kind": "skill", "official": false, "desc": "Use for MIPROv2, Bayesian optimization, instruction and demo tuning, and high-performance DSPy program optimization.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-miprov2-optimizer", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-miprov2-optimizer"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-optimize-anything", "name": "dspy-optimize-anything", "kind": "skill", "official": false, "desc": "Use for GEPA optimize_anything on text artifacts such as code, prompts, agent architectures, configs, and non-DSPy optimization targets.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-optimize-anything", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-optimize-anything"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-optimizer-selection", "name": "dspy-optimizer-selection", "kind": "skill", "official": false, "desc": "Use to choose or compare DSPy optimizers including LabeledFewShot, BootstrapFewShot, MIPROv2, SIMBA, GEPA, BootstrapFinetune, Ensemble, and BetterTogether.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-optimizer-selection", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-optimizer-selection"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-output-refinement-constraints", "name": "dspy-output-refinement-constraints", "kind": "skill", "official": false, "desc": "Use for dspy.Refine, dspy.BestOfN, output constraints, validation, reward functions, and iterative output refinement.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-output-refinement-constraints", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-output-refinement-constraints"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-production-deployment", "name": "dspy-production-deployment", "kind": "skill", "official": false, "desc": "Use for deploying DSPy with save/load, configure_cache, restrict_pickle, track_usage, async execution, streaming, and production runtime controls.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-production-deployment", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-production-deployment"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-rag-pipeline", "name": "dspy-rag-pipeline", "kind": "skill", "official": false, "desc": "Use for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and grounded DSPy answers.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-rag-pipeline", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-rag-pipeline"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-react-agent-builder", "name": "dspy-react-agent-builder", "kind": "skill", "official": false, "desc": "Use for ReAct agents, tool-calling agents, dspy.ReAct, multi-step reasoning and acting, and GEPA agent optimization.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-react-agent-builder", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-react-agent-builder"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-reasoning-modules", "name": "dspy-reasoning-modules", "kind": "skill", "official": false, "desc": "Use for DSPy reasoning modules including RLM, ProgramOfThought, CodeAct, Parallel, sandboxed execution, and long-context workflows.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-reasoning-modules", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-reasoning-modules"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-signature-designer", "name": "dspy-signature-designer", "kind": "skill", "official": false, "desc": "Use for DSPy signatures, InputField, OutputField, typed inputs and outputs, signature classes, and Pydantic-style structured schemas.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-signature-designer", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-signature-designer"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OmidZamani/dspy-skills/skills/dspy-simba-optimizer", "name": "dspy-simba-optimizer", "kind": "skill", "official": false, "desc": "Use for SIMBA optimization, mini-batch introspective optimization, self-reflective rules, stochastic ascent, and numeric-metric optimization.", "repo": "https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-simba-optimizer", "install": ["npx skills add OmidZamani/dspy-skills --skill dspy-simba-optimizer"], "stars": 124, "pushed": "2026-06-23T08:02:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.houtini-ai/lm", "name": "io.github.houtini-ai/lm", "kind": "mcp", "official": null, "desc": "Connect Claude to any OpenAI-compatible LLM endpoint and offload routine work to a local model.", "repo": "https://github.com/houtini-ai/houtini-lm", "install": ["npm:@houtini/lm"], "stars": 122, "pushed": "2026-11-07T05:43:21Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:neo4j-contrib/neo4j-skills/neo4j-genai-plugin-skill", "name": "neo4j-genai-plugin-skill", "kind": "skill", "official": false, "desc": "Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.ve", "repo": "https://github.com/neo4j-contrib/neo4j-skills/tree/HEAD/neo4j-genai-plugin-skill", "install": ["npx skills add neo4j-contrib/neo4j-skills --skill neo4j-genai-plugin-skill"], "stars": 122, "pushed": "2026-11-09T08:06:18Z", "license": "MIT", "plat": "neo4j", "live": "Not checked (runs locally)"}, {"id": "skill:neo4j-contrib/neo4j-skills/neo4j-graphrag-skill", "name": "neo4j-graphrag-skill", "kind": "skill", "official": false, "desc": "Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrieval_query Cypher fragments, query_params, filters, GraphRAG pipeline wiring (GraphRAG + LLM + prompt), all LLM providers (OpenAI, Anthropic, Gemini/VertexAI, Bedrock, Cohere, Mistral, Ollama)", "repo": "https://github.com/neo4j-contrib/neo4j-skills/tree/HEAD/neo4j-graphrag-skill", "install": ["npx skills add neo4j-contrib/neo4j-skills --skill neo4j-graphrag-skill"], "stars": 122, "pushed": "2026-11-09T08:06:18Z", "license": "MIT", "plat": "neo4j", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:DeepL/deepl-mcp-server", "name": "deepl-mcp-server", "kind": "mcp", "official": null, "desc": "A Model Context Protocol (MCP) server that provides translation capabilities using the DeepL API.", "repo": "https://github.com/DeepL/deepl-mcp-server", "install": [], "stars": 120, "pushed": null, "license": null, "plat": "deepl", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:sparesparrow/mcp-prompts", "name": "mcp-prompts", "kind": "mcp", "official": null, "desc": "Model Context Protocol server for managing, storing, and providing prompts and prompt templates for LLM interactions. ", "repo": "https://github.com/sparesparrow/mcp-prompts", "install": [], "stars": 120, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.NVIDIA/elements", "name": "NVIDIA Elements", "kind": "mcp", "official": null, "desc": "NVIDIA Elements UI design system and agent tools for AI/ML, robotics, and autonomous vehicles.", "repo": "https://github.com/NVIDIA/elements", "install": ["npm:@nvidia-elements/cli"], "stars": 119, "pushed": "2026-11-13T03:23:22Z", "license": "Apache-2.0", "plat": "nvidia elements", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:mai-yyy/multi-llm-mcp", "name": "multi-llm-mcp", "kind": "mcp", "official": null, "desc": "一个让 Claude Code 调用 Codex 干活，并可以同时调用多个模型（GPT、Kimi、DeepSeek 等）的 MCP 工具。", "repo": "https://github.com/mai-yyy/multi-llm-mcp", "install": [], "stars": 118, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/aider-1h-ttl", "name": "aider-1h-ttl", "kind": "skill", "official": false, "desc": "Aider uses 5min TTL by default and works around long pauses with keepalive pings. Wire up the 1h TTL beta instead.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/aider-1h-ttl", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill aider-1h-ttl"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/aider-cache-default-on", "name": "aider-cache-default-on", "kind": "skill", "official": false, "desc": "Aider's --cache-prompts is off by default. Flip it on for supported models.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/aider-cache-default-on", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill aider-cache-default-on"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/cline-fix-volatile-msg", "name": "cline-fix-volatile-msg", "kind": "skill", "official": false, "desc": "Ladder-aware Cline Anthropic caching — verify the rolling read/write ladder on the wire, then add the tools breakpoint and tune TTL. Updated for the 2026-08 AI-SDK monorepo.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/cline-fix-volatile-msg", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill cline-fix-volatile-msg"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/cline-openai-cache-key", "name": "cline-openai-cache-key", "kind": "skill", "official": false, "desc": "Cline OpenAI native provider sends no prompt_cache_key. Add a stable per-task key so cached_tokens stops being zero.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/cline-openai-cache-key", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill cline-openai-cache-key"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/cline-pin-timestamp", "name": "cline-pin-timestamp", "kind": "skill", "official": false, "desc": "Cline's system prompt includes a timestamp that may be recomputed per request, invalidating the system-prompt cache.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/cline-pin-timestamp", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill cline-pin-timestamp"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/continue-enable-defaults", "name": "continue-enable-defaults", "kind": "skill", "official": false, "desc": "Continue's prompt caching is opt-in via config and off by default. Flip the default to systemAndTools.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/continue-enable-defaults", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill continue-enable-defaults"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/continue-fix-volatile-msg", "name": "continue-fix-volatile-msg", "kind": "skill", "official": false, "desc": "Ladder-aware Continue Anthropic caching — verify the rolling ladder on the wire, then enable it by default and add TTL coverage.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/continue-fix-volatile-msg", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill continue-fix-volatile-msg"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/continue-gemini-explicit", "name": "continue-gemini-explicit", "kind": "skill", "official": false, "desc": "Continue's Gemini provider doesn't use the cachedContents API at all. Add explicit caching for sessions over the minimum token threshold.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/continue-gemini-explicit", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill continue-gemini-explicit"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/opencode-bedrock-doc-blocks", "name": "opencode-bedrock-doc-blocks", "kind": "skill", "official": false, "desc": "OpenCode places cachePoint on Bedrock messages containing DocumentBlocks, which produces a \"nothing available to cache\" error.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/opencode-bedrock-doc-blocks", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill opencode-bedrock-doc-blocks"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/opencode-detect-openai-compat", "name": "opencode-detect-openai-compat", "kind": "skill", "official": false, "desc": "OpenCode's caching detection misses OpenAI-compatible proxies routing to Anthropic/Bedrock. Broaden the predicate.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/opencode-detect-openai-compat", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill opencode-detect-openai-compat"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/opencode-mistral-cache-key", "name": "opencode-mistral-cache-key", "kind": "skill", "official": false, "desc": "OpenCode doesn't set prompt_cache_key for Mistral models, missing the documented 10% cached-token discount.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/opencode-mistral-cache-key", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill opencode-mistral-cache-key"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/roo-bedrock-custom-arn", "name": "roo-bedrock-custom-arn", "kind": "skill", "official": false, "desc": "Roo Code's Bedrock provider silently disables caching for custom ARNs. Populate cachableFields to fix.", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/roo-bedrock-custom-arn", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill roo-bedrock-custom-arn"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:OnlyTerp/prompt-cache-skills/skills/roo-fix-volatile-msg", "name": "roo-fix-volatile-msg", "kind": "skill", "official": false, "desc": "Ladder-aware Roo Code Anthropic caching — verify the rolling read/write ladder on the wire, then close the real gaps (Vertex 4-block budget, MiniMax path).", "repo": "https://github.com/OnlyTerp/prompt-cache-skills/tree/HEAD/skills/roo-fix-volatile-msg", "install": ["npx skills add OnlyTerp/prompt-cache-skills --skill roo-fix-volatile-msg"], "stars": 117, "pushed": "2026-08-29T02:25:21Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:trina-app/trina-ax", "name": "trina-ax", "kind": "mcp", "official": null, "desc": "Set up Claude Code once. Your whole company gets it. Self-hosted Claude Code and Codex with your company files, team skills, MCP and per-person folder permissions.", "repo": "https://github.com/trina-app/trina-ax", "install": [], "stars": 117, "pushed": "2026-11-14T05:52:10Z", "license": "AGPL-3.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:JochenYang/luma-mcp", "name": "luma-mcp", "kind": "mcp", "official": null, "desc": "多模型视觉理解 MCP 服务器，为不支持图片理解的 AI 编码模型提供视觉能力：分析截图、报错、UI 与文档，可接入多家主流视觉大模型。Multi-model vision MCP server that adds image understanding to AI coding models without native vision — analyze screenshots, errors, UI and documents via major vision LLM providers.", "repo": "https://github.com/JochenYang/luma-mcp", "install": [], "stars": 116, "pushed": null, "license": null, "plat": "vision models", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:vectorize-io/vectorize-mcp-server", "name": "vectorize-mcp-server", "kind": "mcp", "official": null, "desc": "Official Vectorize MCP Server", "repo": "https://github.com/vectorize-io/vectorize-mcp-server", "install": [], "stars": 116, "pushed": null, "license": null, "plat": "vectorize", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:mcpgod/cli", "name": "cli", "kind": "mcp", "official": null, "desc": "Fine-grained control over model context protocol (MCP) clients, servers, and tools. Context is God.", "repo": "https://github.com/mcpgod/cli", "install": [], "stars": 115, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:neondatabase/agent-skills/plugins/neon-postgres/skills/neon-ai-gateway", "name": "neon-ai-gateway", "kind": "skill", "official": false, "desc": "One API and one credential for frontier and open-source LLMs and embedding models, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, generate embeddings, add AI/chat/an agent to their app, route between model providers (OpenAI, Anthropic, Google/Gemini, Meta, Alibaba, and more), or avoid juggling separate provider API keys and accounts — especially when they already use Neon and want AI requests to branch with their project. Works with the OpenAI SDK, A", "repo": "https://github.com/neondatabase/agent-skills/tree/HEAD/plugins/neon-postgres/skills/neon-ai-gateway", "install": ["npx skills add neondatabase/agent-skills --skill neon-ai-gateway"], "stars": 113, "pushed": "2026-11-12T18:57:31Z", "license": "Apache-2.0", "plat": "databricks", "live": "Not checked (runs locally)"}, {"id": "skill:mizzlelover/cida", "name": "cida", "kind": "skill", "official": false, "desc": "辞达 · 现代中文高质量对话型表达与平台内容设计引擎。用于中文写作、重写、润色、诊断、 口述/访谈成文、书面自然化、公众号/小红书/知乎/微博/X/视频口播/播客/社群等场景适配， 以及按用户真实文本校准个人风格。先确定事实与立场边界，再建立平台任务契约、比较表达策略、 设计读者价值兑现，最后完成文本质量与平台适配双自检。不做去AI味检测对抗、虚假钩子、 平台流量承诺或任何在世作者的个人风格模仿。", "repo": "https://github.com/mizzlelover/cida", "install": ["npx skills add mizzlelover/cida --skill cida"], "stars": 111, "pushed": "2026-11-05T07:19:25Z", "license": "MIT", "plat": "辞达", "live": "Not checked (runs locally)"}, {"id": "skill:tronghieu/agent-skills/skills/system-prompt-creator", "name": "system-prompt-creator", "kind": "skill", "official": false, "desc": "Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say \"sy", "repo": "https://github.com/tronghieu/agent-skills/tree/HEAD/skills/system-prompt-creator", "install": ["npx skills add tronghieu/agent-skills --skill system-prompt-creator"], "stars": 110, "pushed": "2026-10-31T08:58:30Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wsimmonds/claude-nextjs-skills/vercel-ai-sdk", "name": "vercel-ai-sdk", "kind": "skill", "official": false, "desc": "Guide for Vercel AI SDK v5 implementation patterns including generateText, streamText, useChat hook, tool calling, embeddings, and MCP integration. Use when implementing AI chat interfaces, streaming responses, tool/function calling, text embeddings, or working with convertToModelMessages and toUIMessageStreamResponse. Activates for AI SDK integration, useChat hook usage, message streaming, or tool calling tasks.", "repo": "https://github.com/wsimmonds/claude-nextjs-skills/tree/HEAD/vercel-ai-sdk", "install": ["npx skills add wsimmonds/claude-nextjs-skills --skill vercel-ai-sdk"], "stars": 110, "pushed": "2025-10-24T11:05:39Z", "license": "MIT", "plat": "vercel", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.justyns/silverbullet-ai-mcp", "name": "io.github.justyns/silverbullet-ai-mcp", "kind": "mcp", "official": null, "desc": "Exposes silverbullet-ai tools from a SilverBullet space to MCP clients", "repo": "https://github.com/justyns/silverbullet-ai", "install": ["npm:silverbullet-ai-mcp"], "stars": 109, "pushed": "2026-11-11T19:41:57Z", "license": "AGPL-3.0", "plat": "silverbullet", "live": "Not checked (runs locally)"}, {"id": "skill:share-skills/pi/claude-code/pi-en", "name": "pi-en", "kind": "skill", "official": false, "desc": "PI Cognitive AI. Trigger: $pi/coding/dev/code/architecture/API/refactor/debug/bug/error/exception/crash/timeout/performance/optimization/test/compile/git/release/verify/review/CR/product/requirements/ops/growth/design/team/support, or deep/2+ failures/looping/stuck/giving-up/retry/nevermind", "repo": "https://github.com/share-skills/pi/tree/HEAD/claude-code/pi-en", "install": ["npx skills add share-skills/pi --skill pi-en"], "stars": 109, "pushed": "2026-06-23T06:37:23Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:share-skills/pi/claude-code/pi-en-progressive", "name": "pi-en-progressive", "kind": "skill", "official": false, "desc": "PI Cognitive AI. Trigger: $pi/coding/dev/code/architecture/API/refactor/debug/bug/error/exception/crash/timeout/performance/optimization/test/compile/git/release/verify/review/CR/product/requirements/ops/growth/design/team/support, or deep/2+ failures/looping/stuck/giving-up/retry/nevermind", "repo": "https://github.com/share-skills/pi/tree/HEAD/claude-code/pi-en-progressive", "install": ["npx skills add share-skills/pi --skill pi-en-progressive"], "stars": 109, "pushed": "2026-06-23T06:37:23Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:share-skills/pi/claude-code/pi-progressive", "name": "pi-progressive", "kind": "skill", "official": false, "desc": "PI 智行合一。触发：$pi/编程/开发/dev/code/代码/实现/架构/API/重构/调试/debug/bug/报错/异常/崩溃/超时/性能/优化/测试/test/编译/compile/git/make/发布/验证/审查/review/CR/产品/需求/运营/增长/创意/设计/协作/团队/沟通/交互/陪伴/情感，或深度/deep/失败2+次/反复失败/打转/卡住/言退/再试试/换个参数/算了", "repo": "https://github.com/share-skills/pi/tree/HEAD/claude-code/pi-progressive", "install": ["npx skills add share-skills/pi --skill pi-progressive"], "stars": 109, "pushed": "2026-06-23T06:37:23Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:chengzhongwei/Prompt-sensei", "name": "prompt-sensei", "kind": "skill", "official": false, "desc": "Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.", "repo": "https://github.com/chengzhongwei/Prompt-sensei", "install": ["npx skills add chengzhongwei/Prompt-sensei --skill prompt-sensei"], "stars": 109, "pushed": "2026-07-13T18:05:27Z", "license": "Apache-2.0", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:andromanpro/1c-ai-connector", "name": "1c-ai-connector", "kind": "mcp", "official": null, "desc": "ИИкона — AI-коннектор для 1С:Предприятие 8.3: LLM-провайдеры, агентская петля (function calling), RAG, MCP-сервер, мониторинг ошибок с ИИ-анализом, генератор диаграмм", "repo": "https://github.com/andromanpro/1c-ai-connector", "install": [], "stars": 108, "pushed": null, "license": null, "plat": "1c-ai-connector", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Nayjest/ai-microcore", "name": "ai-microcore", "kind": "mcp", "official": null, "desc": "A handy lib for smooth interaction with large language models (LLMs) and crafting AI apps.", "repo": "https://github.com/Nayjest/ai-microcore", "install": [], "stars": 108, "pushed": null, "license": null, "plat": "ai-microcore", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:duke7able/gemini-mcp-desktop-client", "name": "gemini-mcp-desktop-client", "kind": "mcp", "official": null, "desc": "first gemini based desktop client for MCP", "repo": "https://github.com/duke7able/gemini-mcp-desktop-client", "install": [], "stars": 108, "pushed": null, "license": null, "plat": "gemini", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jonigl/ollama-mcp-bridge", "name": "ollama-mcp-bridge", "kind": "mcp", "official": null, "desc": "Extend the Ollama API with dynamic AI tool integration from multiple MCP (Model Context Protocol) servers. Fully compatible, transparent, and developer-friendly, ideal for building powerful local LLM applications, AI agents, and custom chatbots", "repo": "https://github.com/jonigl/ollama-mcp-bridge", "install": [], "stars": 107, "pushed": null, "license": null, "plat": "ollama", "live": "Not checked (runs locally)"}, {"id": "skill:BerriAI/litellm-skills/add-key", "name": "add-key", "kind": "skill", "official": false, "desc": "Generate a new API key on a live LiteLLM proxy. Asks for alias, scope (user/team), budget, models, and expiry, then calls POST /key/generate. Use when the user wants to create, generate, or provision an API key on a LiteLLM proxy instance.", "repo": "https://github.com/BerriAI/litellm-skills/tree/HEAD/add-key", "install": ["npx skills add BerriAI/litellm-skills --skill add-key"], "stars": 106, "pushed": "2026-05-07T18:10:42Z", "license": "MIT", "plat": "litellm", "live": "Not checked (runs locally)"}, {"id": "skill:BerriAI/litellm-skills/add-model", "name": "add-model", "kind": "skill", "official": false, "desc": "Add a new model to a live LiteLLM proxy. Walks the user through picking a provider, entering the deployment name and credentials, calls POST /model/new, then test-calls the model to confirm it routes correctly. Use when the user wants to add, register, deploy, or configure a new model on a LiteLLM proxy instance.", "repo": "https://github.com/BerriAI/litellm-skills/tree/HEAD/add-model", "install": ["npx skills add BerriAI/litellm-skills --skill add-model"], "stars": 106, "pushed": "2026-05-07T18:10:42Z", "license": "MIT", "plat": "litellm", "live": "Not checked (runs locally)"}, {"id": "skill:BerriAI/litellm-skills/delete-key", "name": "delete-key", "kind": "skill", "official": false, "desc": "Delete one or more API keys from a live LiteLLM proxy. Ask for the key(s) and confirm before calling POST /key/delete.", "repo": "https://github.com/BerriAI/litellm-skills/tree/HEAD/delete-key", "install": ["npx skills add BerriAI/litellm-skills --skill delete-key"], "stars": 106, "pushed": "2026-05-07T18:10:42Z", "license": "MIT", "plat": "litellm", "live": "Not checked (runs locally)"}, {"id": "skill:BerriAI/litellm-skills/delete-model", "name": "delete-model", "kind": "skill", "official": false, "desc": "Delete a model from a live LiteLLM proxy. Asks for the model name or model_id and confirms before calling POST /model/delete. Use when the user wants to remove, delete, or unregister a model from a LiteLLM proxy instance.", "repo": "https://github.com/BerriAI/litellm-skills/tree/HEAD/delete-model", "install": ["npx skills add BerriAI/litellm-skills --skill delete-model"], "stars": 106, "pushed": "2026-05-07T18:10:42Z", "license": "MIT", "plat": "litellm", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:peterkrueck/mcp-gemini-assistant", "name": "mcp-gemini-assistant", "kind": "mcp", "official": null, "desc": "MCP Gemini - Coding Assistant for Claude Code", "repo": "https://github.com/peterkrueck/mcp-gemini-assistant", "install": [], "stars": 106, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:TheOrcDev/skills/skills/orc-me", "name": "orc-me", "kind": "skill", "official": false, "desc": "Respond as a blunt, decisive orc — short sentences, no hedging, lead with the verdict, full technical accuracy preserved. Use when the user says \"orc-me\", \"orc mode\", \"talk like an orc\", \"be blunt\", \"be decisive\", or invokes /orc-me. Stay in orc voice until told to stop.", "repo": "https://github.com/TheOrcDev/skills/tree/HEAD/skills/orc-me", "install": ["npx skills add TheOrcDev/skills --skill orc-me"], "stars": 106, "pushed": "2026-11-02T11:56:20Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:BerriAI/litellm-skills/update-key", "name": "update-key", "kind": "skill", "official": false, "desc": "Update an existing API key on a live LiteLLM proxy. Ask for the key and what to change (budget, models, expiry), then call POST /key/update.", "repo": "https://github.com/BerriAI/litellm-skills/tree/HEAD/update-key", "install": ["npx skills add BerriAI/litellm-skills --skill update-key"], "stars": 106, "pushed": "2026-05-07T18:10:42Z", "license": "MIT", "plat": "litellm", "live": "Not checked (runs locally)"}, {"id": "skill:BerriAI/litellm-skills/update-model", "name": "update-model", "kind": "skill", "official": false, "desc": "Update an existing model on a live LiteLLM proxy. Ask for the model_id and what to change (API key, base URL, etc.), then call POST /model/update.", "repo": "https://github.com/BerriAI/litellm-skills/tree/HEAD/update-model", "install": ["npx skills add BerriAI/litellm-skills --skill update-model"], "stars": 106, "pushed": "2026-05-07T18:10:42Z", "license": "MIT", "plat": "litellm", "live": "Not checked (runs locally)"}, {"id": "skill:BerriAI/litellm-skills/view-usage", "name": "view-usage", "kind": "skill", "official": false, "desc": "Query spend and token activity on a live LiteLLM proxy. Shows daily usage broken down by user, team, org, tag, job, or model. Use when the user wants to see costs, token counts, request volume, or job-level attribution for a given date range.", "repo": "https://github.com/BerriAI/litellm-skills/tree/HEAD/view-usage", "install": ["npx skills add BerriAI/litellm-skills --skill view-usage"], "stars": 106, "pushed": "2026-05-07T18:10:42Z", "license": "MIT", "plat": "litellm", "live": "Not checked (runs locally)"}, {"id": "skill:jha0313/skills_repo/improve-token-efficiency", "name": "improve-token-efficiency", "kind": "skill", "official": false, "desc": "Claude Code 세션 JSONL 로그를 파싱하여 토큰/컨텍스트 효율 리포트(HTML 대시보드 + $ 절감안)를 생성하는 스킬. '토큰 효율 분석', '세션 효율', '비용 분석', 'Claude Code usage report', 'analyze token efficiency', 'show session cost', 'improve token efficiency', 'efficiency score', 'how much did I spend on Claude', 'session report' 등의 요청에 반드시 트리거할 것. 사용자가 레포의 Claude Code 사용 패턴/비용/효율을 알고 싶어하거나 어떻게 줄일지 문의할 때 사용. 단일 세션이 아니라 '여러 세션 전체'에 대한 집계·점수화·시각화가 필요한 모든 경우에 적용.", "repo": "https://github.com/jha0313/skills_repo/tree/HEAD/improve-token-efficiency", "install": ["npx skills add jha0313/skills_repo --skill improve-token-efficiency"], "stars": 105, "pushed": "2026-09-22T07:33:37Z", "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:PerryLink/jevcore", "name": "jevcore", "kind": "mcp", "official": null, "desc": "TypeSafe Jev for DeepSeek Harness, the Model Context Protocol, and plain Node: typed judgments instead of prose, offline by default.", "repo": "https://github.com/PerryLink/jevcore", "install": [], "stars": 105, "pushed": null, "license": null, "plat": "deepseek", "live": "Not checked (runs locally)"}, {"id": "skill:render-oss/skills/skills/render-mcp", "name": "render-mcp", "kind": "skill", "official": false, "desc": "Connects and configures the Render MCP server for AI coding tools—setup per tool (Cursor, Claude Code, Codex), authentication, workspace selection, capability discovery, and troubleshooting. Use when MCP is not configured, list_services() fails, the user asks about Render MCP setup, or an action skill needs MCP but it's not connected yet. Trigger terms: MCP, Render MCP, list_services, MCP setup, MCP server, OAuth, API key, Bearer token, mcp.render.com, workspace selection.", "repo": "https://github.com/render-oss/skills/tree/HEAD/skills/render-mcp", "install": ["npx skills add render-oss/skills --skill render-mcp"], "stars": 105, "pushed": "2026-11-03T18:29:03Z", "license": "MIT", "plat": "render", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:devnoname120/codexify", "name": "codexify", "kind": "mcp", "official": null, "desc": "Bring Codex inside ChatGPT for unlimited vibecoding —  no Codex quota used!", "repo": "https://github.com/devnoname120/codexify", "install": [], "stars": 104, "pushed": null, "license": null, "plat": "codex", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:libre-webui/libre-webui", "name": "libre-webui", "kind": "mcp", "official": null, "desc": "A local-first workspace for chat, private knowledge, artifacts, and isolated model-driven work. Self-hosted. Provider-flexible. Apache 2.0.", "repo": "https://github.com/libre-webui/libre-webui", "install": [], "stars": 104, "pushed": null, "license": null, "plat": "local-first workspace", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:hlpun/Train-in-Silence", "name": "Train-in-Silence", "kind": "mcp", "official": null, "desc": "The first Task-Aware MCP server and automated VRAM calculator for LLM fine-tuning. Instantly snipe the cheapest, fastest GPUs across 10+ cloud providers.", "repo": "https://github.com/hlpun/Train-in-Silence", "install": [], "stars": 104, "pushed": null, "license": null, "plat": "llm fine-tuning", "live": "Not checked (runs locally)"}, {"id": "skill:AsyrafHussin/agent-skills/skills/laravel-ai-sdk", "name": "laravel-ai-sdk", "kind": "skill", "official": false, "desc": "Laravel AI SDK for building AI-powered features. Use when creating agents, generating images or audio, working with embeddings, vector search, or testing AI features. Triggers on tasks involving laravel/ai, AI agents, tool-calling, structured output, streaming, embeddings, reranking, or AI faking in tests.", "repo": "https://github.com/AsyrafHussin/agent-skills/tree/HEAD/skills/laravel-ai-sdk", "install": ["npx skills add AsyrafHussin/agent-skills --skill laravel-ai-sdk"], "stars": 103, "pushed": "2026-08-27T12:29:22Z", "license": "MIT", "plat": "laravel", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.stabgan/openrouter-multimodal", "name": "io.github.stabgan/openrouter-multimodal", "kind": "mcp", "official": null, "desc": "Chat with 300+ LLMs via OpenRouter. Analyze and generate images, audio, and video from MCP.", "repo": "https://github.com/stabgan/openrouter-mcp-multimodal", "install": ["npm:@stabgan/openrouter-mcp-multimodal", "oci:docker.io/stabgan/openrouter-mcp-multimodal:4.5.1"], "stars": 102, "pushed": "2026-11-12T11:07:47Z", "license": "Apache-2.0", "plat": "openrouter", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:jeecgboot/jeecg-ai", "name": "jeecg-ai", "kind": "mcp", "official": null, "desc": "【AI应用平台】一个AI应用与零代码结合体，旨在帮助开发者快速构建和部署AI 应用和零代码搭建！功能涵盖：AI应用、零代码搭建、AI报表、AI大屏、AI仪表盘、Chat2BI报表", "repo": "https://github.com/jeecgboot/jeecg-ai", "install": [], "stars": 102, "pushed": null, "license": null, "plat": "jeecg", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:philschmid/gemini-api-docs-mcp", "name": "gemini-api-docs-mcp", "kind": "mcp", "official": null, "desc": "", "repo": "https://github.com/philschmid/gemini-api-docs-mcp", "install": [], "stars": 101, "pushed": null, "license": null, "plat": "gemini", "live": "Not checked (runs locally)"}, {"id": "skill:obie/skills/skills/mcp-oauth-setup", "name": "mcp-oauth-setup", "kind": "skill", "official": false, "desc": "Implement MCP server authentication with OAuth Dynamic Client Registration (RFC 7591), Authorization Server Metadata Discovery (RFC 8414), and generalized per-agent credential support. This skill should be used when building admin UIs that let users connect to third-party MCP servers, whether they use OAuth (Linear, Sentry, Granola), bearer tokens (Render, custom APIs), or API keys. Covers the full flow: metadata discovery, client registration, PKCE authorization, token exchange, token refresh, ", "repo": "https://github.com/obie/skills/tree/HEAD/skills/mcp-oauth-setup", "install": ["npx skills add obie/skills --skill mcp-oauth-setup"], "stars": 101, "pushed": "2026-10-20T12:09:36Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:TimeCyber/MCP-X-web", "name": "MCP-X-web", "kind": "mcp", "official": null, "desc": "MCP-X 是一个**企业级 AI 中台**，集成了 AI 对话、文案生成、视频生成、图片生成、前端应用构建、MCP市场、Agent智能体等多种创作工具，为企业和开发者提供一站式的 AIGC 工作台解决方案。", "repo": "https://github.com/TimeCyber/MCP-X-web", "install": [], "stars": 101, "pushed": null, "license": null, "plat": "mcp-x", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.whistlegraph/aesthetic-computer", "name": "io.github.whistlegraph/aesthetic-computer", "kind": "mcp", "official": null, "desc": "MCP server for aesthetic.computer - publish JavaScript, KidLisp art, and clock melodies", "repo": "https://github.com/whistlegraph/aesthetic-computer", "install": ["npm:@aesthetic.computer/mcp"], "stars": 91, "pushed": "2026-11-13T04:47:06Z", "license": "ISC", "plat": "aesthetic.computer", "live": "Not checked (runs locally)"}, {"id": "mcp:com.keboola/mcp", "name": "com.keboola/mcp", "kind": "mcp", "official": null, "desc": "Connect your AI assistants to Keboola and expose your data, transformations, SQL queries, ...", "repo": "https://github.com/keboola/mcp-server", "install": ["pypi:keboola-mcp-server", "https://mcp.us-east4.gcp.keboola.com/mcp"], "stars": 85, "pushed": "2026-11-13T11:48:40Z", "license": "MIT", "plat": "keboola", "live": "Not checked (runs locally)"}, {"id": "mcp:com.pulsemcp/langfuse", "name": "com.pulsemcp/langfuse", "kind": "mcp", "official": null, "desc": "MCP server for Langfuse LLM observability — trace and observation analysis.", "repo": "https://github.com/pulsemcp/mcp-servers", "install": ["npm:langfuse-observability-mcp-server"], "stars": 81, "pushed": "2026-10-09T17:32:15Z", "license": "MIT", "plat": "langfuse", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.tomohiro-owada/devrag", "name": "io.github.tomohiro-owada/devrag", "kind": "mcp", "official": null, "desc": "Lightweight local RAG MCP server. 40x token reduction.", "repo": "https://github.com/tomohiro-owada/devrag", "install": [], "stars": 64, "pushed": "2026-04-15T22:43:14Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.sshahzaiib/agy-bridge", "name": "io.github.sshahzaiib/agy-bridge", "kind": "mcp", "official": null, "desc": "Delegate heavy tasks from Claude Code to the Antigravity CLI (Gemini) with quota-aware failover.", "repo": "https://github.com/sshahzaiib/agy-bridge", "install": ["npm:agy-bridge"], "stars": 63, "pushed": "2026-10-28T21:18:02Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:ai.smithery/kkjdaniel-bgg-mcp", "name": "ai.smithery/kkjdaniel-bgg-mcp", "kind": "mcp", "official": null, "desc": "BGG MCP provides access to the BoardGameGeek API through the Model Context Protocol, enabling retr…", "repo": "https://github.com/kkjdaniel/bgg-mcp", "install": ["https://server.smithery.ai/@kkjdaniel/bgg-mcp/mcp"], "stars": 60, "pushed": "2026-10-15T11:51:45Z", "license": "MIT", "plat": "boardgamegeek", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.runapi-builder/runapi-mcp", "name": "io.github.runapi-builder/runapi-mcp", "kind": "mcp", "official": null, "desc": "130+ AI models for image, video, music, and audio — 18 model families, one RunAPI account.", "repo": "https://github.com/runapi-ai/mcp", "install": ["npm:@runapi.ai/mcp", "https://mcp.runapi.ai/mcp"], "stars": 56, "pushed": "2026-11-10T14:49:17Z", "license": "Apache-2.0", "plat": "runapi", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.rafsilva85/credit-optimizer", "name": "Credit Optimizer v5 for Manus AI", "kind": "mcp", "official": null, "desc": "Optimizes Manus AI credit usage by 30-75% via intelligent model routing and smart testing.", "repo": "https://github.com/rafsilva85/credit-optimizer-v5", "install": ["pypi:mcp-credit-optimizer"], "stars": 53, "pushed": "2026-05-25T00:08:44Z", "license": "MIT", "plat": "manus ai", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.qwe4559999/scopus-mcp", "name": "io.github.qwe4559999/scopus-mcp", "kind": "mcp", "official": null, "desc": "A Model Context Protocol server for the Elsevier Scopus API", "repo": "https://github.com/qwe4559999/scopus-mcp", "install": ["pypi:scopus-mcp"], "stars": 53, "pushed": "2026-05-17T06:36:58Z", "license": "MIT", "plat": "elsevier scopus", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.paiml/pmcp", "name": "io.github.paiml/pmcp", "kind": "mcp", "official": null, "desc": "High-quality Rust SDK for Model Context Protocol (MCP) with full TypeScript SDK compatibility", "repo": "https://github.com/paiml/rust-mcp-sdk", "install": [], "stars": 51, "pushed": "2026-11-09T07:02:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Anuj7411/sipcode", "name": "Sipcode", "kind": "mcp", "official": null, "desc": "Context hygiene for Claude Code: drift detection, re-read dedup, and token analytics.", "repo": "https://github.com/Anuj7411/sipcode", "install": ["npm:sipcode"], "stars": 46, "pushed": "2026-10-16T08:23:38Z", "license": "MIT", "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.crit-labs/crit", "name": "crit", "kind": "mcp", "official": null, "desc": "Build, evaluate and serve a bespoke LLM inference engine for your own GPU with crit's commands.", "repo": "https://github.com/crit-labs/crit", "install": ["pypi:crit"], "stars": 45, "pushed": "2026-11-12T11:12:52Z", "license": "Apache-2.0", "plat": "crit", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.f/prompts.chat-mcp", "name": "prompts.chat MCP Server", "kind": "mcp", "official": null, "desc": "Search and retrieve AI prompts from prompts.chat, the social platform for AI prompts.", "repo": "https://github.com/f/prompts.chat-mcp", "install": ["npm:@fkadev/prompts.chat-mcp"], "stars": 40, "pushed": "2026-03-28T21:22:49Z", "license": null, "plat": "prompts.chat", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:togetherai-skills", "name": "togetherai-skills", "kind": "claude-plugin", "official": null, "desc": "Agent Skills for Together AI platform — inference, training, embeddings, audio, video, images, function calling, and infrastructure. Covers serverless chat completions, image/video generation, fine-tuning, batch inference, evaluations, sandboxes, dedicated endpoints, and GPU clusters.", "repo": "https://github.com/togethercomputer/skills.git", "install": ["claude plugin install togetherai-skills@claude-plugins-official"], "stars": 40, "pushed": "2026-11-06T22:14:38Z", "license": "MIT", "plat": "together ai", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.DeanWard/HAL", "name": "io.github.DeanWard/HAL", "kind": "mcp", "official": null, "desc": "HAL (HTTP API Layer) - An MCP server that provides HTTP API capabilities to Large Language Models", "repo": "https://github.com/DeanWard/HAL", "install": ["npm:hal-mcp"], "stars": 38, "pushed": "2025-10-22T09:49:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:ai.wavespeed/mcp", "name": "WaveSpeed", "kind": "mcp", "official": null, "desc": "Run any model on the live WaveSpeed catalog: image, video, audio, 3D.", "repo": "https://github.com/WaveSpeedAI/mcp-server", "install": ["npm:@wavespeed/mcp", "https://api.wavespeed.ai/mcp"], "stars": 37, "pushed": "2026-11-10T11:01:46Z", "license": "MIT", "plat": "wavespeed", "live": "Not checked (runs locally)"}, {"id": "mcp:com.kernelcad/kernelcad", "name": "kernelCAD", "kind": "mcp", "official": null, "desc": "CAD in ChatGPT and Claude: editable parts, STEP/STL/drawings, in-chat 3D, automatic checks.", "repo": "https://github.com/w1ne/kernelCAD-web", "install": ["https://mcp.kernelcad.com/mcp"], "stars": 36, "pushed": "2026-11-13T13:01:56Z", "license": "MIT", "plat": "kernelcad", "live": "Live · 3210 ms · 81 tools · free to try: `get_asset` answered without a key (checked 2026-11-13)"}, {"id": "mcp:io.github.houtini-ai/gemini", "name": "io.github.houtini-ai/gemini", "kind": "mcp", "official": null, "desc": "Model Context Protocol server for Google Gemini AI with chat, research, and grounding", "repo": "https://github.com/houtini-ai/gemini-mcp", "install": ["npm:@houtini/gemini-mcp"], "stars": 34, "pushed": "2026-10-31T04:43:56Z", "license": "Apache-2.0", "plat": "google gemini ai", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.eater2/ai-agents-api-library", "name": "AI Agents API Library", "kind": "mcp", "official": null, "desc": "Find APIs and MCP servers for AI agents: auth, free tier, MCP endpoint, docs, last-checked date.", "repo": "https://github.com/eater2/ai_agents_api_library", "install": ["npm:ai-agents-api-library", "https://ai-agents-api-library.vercel.app/mcp"], "stars": 33, "pushed": "2026-11-12T13:16:35Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:datarobot-agent-skills", "name": "datarobot-agent-skills", "kind": "claude-plugin", "official": null, "desc": "DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and external agent monitoring.", "repo": "https://github.com/datarobot-oss/datarobot-agent-skills.git", "install": ["claude plugin install datarobot-agent-skills@claude-plugins-official"], "stars": 33, "pushed": "2026-11-12T15:01:18Z", "license": "Apache-2.0", "plat": "datarobot", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.pubnub/mcp-server", "name": "PubNub MCP Server", "kind": "mcp", "official": null, "desc": "PubNub Model Context Protocol MCP Server for Cursor and Claude", "repo": "https://github.com/pubnub/pubnub-mcp-server", "install": ["npm:@pubnub/mcp"], "stars": 33, "pushed": "2026-11-12T18:40:06Z", "license": "NOASSERTION", "plat": "cursor, claude", "live": "Not checked (runs locally)"}, {"id": "mcp:dev.tokensafe/tokensafe", "name": "TokenSafe", "kind": "mcp", "official": null, "desc": "Reduce your AI bills by up to 99% locally: focused code, warm prompt caches & zero secret leaks.", "repo": "https://github.com/tokensafe-dev/tokensafe", "install": ["npm:tokensafe", "pypi:tokensafe", "https://api.tokensafe.dev/mcp"], "stars": 31, "pushed": "2026-11-13T02:50:58Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.himorishige/hatago-mcp-hub", "name": "io.github.himorishige/hatago-mcp-hub", "kind": "mcp", "official": null, "desc": "Unified MCP Hub for managing multiple Model Context Protocol servers", "repo": "https://github.com/himorishige/hatago-mcp-hub", "install": ["npm:@himorishige/hatago-mcp-hub"], "stars": 27, "pushed": "2025-09-14T15:08:57Z", "license": "MIT", "plat": "mcp hub", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.arikusi/deepseek", "name": "io.github.arikusi/deepseek", "kind": "mcp", "official": null, "desc": "MCP server for DeepSeek AI with chat, reasoning, sessions, function calling, and cost tracking", "repo": "https://github.com/arikusi/deepseek-mcp-server", "install": ["npm:@arikusi/deepseek-mcp-server", "npm:@arikusi/deepseek-mcp-server"], "stars": 26, 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"mcp:xyz.phase.ai/gateway", "name": "Phase Router", "kind": "mcp", "official": null, "desc": "OpenAI-compatible LLM gateway: pay per request with an API key, signed receipt for each call.", "repo": null, "install": ["https://mcp.ai.phase.xyz/mcp"], "stars": null, "pushed": null, "license": null, "plat": "openai", "live": "Live · 831 ms · 7 tools · `list_models` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:io.github.phrase/mcp", "name": "Phrase MCP", "kind": "mcp", "official": null, "desc": "Phrase MCP server: language intelligence platform for translation, terminology, and quality.", "repo": null, "install": ["https://mcp.eu.phrase.com", "https://mcp.us.phrase.com"], "stars": null, "pushed": null, "license": null, "plat": "", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:ai.piishield/pii-shield", "name": "PII Shield", "kind": "mcp", "official": null, "desc": "Redact names, IDs, emails, phones and card numbers before they reach the model; restore them 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