[{"id": "skill:K-Dense-AI/scientific-agent-skills/skills/primekg", "name": "primekg", "kind": "skill", "official": false, "desc": "Queries a pinned Precision Medicine Knowledge Graph (PrimeKG) CSV for typed gene, drug, disease, and phenotype nodes, direct associations, disease context, and one- or two-hop paths. Use for PrimeKG reproducibility, biological association lookup, and hypothesis generation with relation and data provenance preserved.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/primekg", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill primekg"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "primekg", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/umap-learn", "name": "umap-learn", "kind": "skill", "official": false, "desc": "Applies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/umap-learn", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill umap-learn"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "umap-learn", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-devops-cloud/skills/azure-search-documents-py", "name": "azure-search-documents-py", "kind": "skill", "official": false, "desc": "Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-devops-cloud/skills/azure-search-documents-py", "install": ["npx skills add sickn33/agentic-awesome-skills --skill azure-search-documents-py"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "azure ai search", "live": "Not checked (runs locally)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-data-engineering-platform/skills/embedding-strategies", "name": "embedding-strategies", "kind": "skill", "official": false, "desc": "Guide to selecting and optimizing embedding models for vector search applications.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-engineering-platform/skills/embedding-strategies", "install": ["npx skills add sickn33/agentic-awesome-skills --skill embedding-strategies"], "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-data-engineering-platform/skills/vector-database-engineer", "name": "vector-database-engineer", "kind": "skill", "official": false, "desc": "Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-engineering-platform/skills/vector-database-engineer", "install": ["npx skills add sickn33/agentic-awesome-skills --skill vector-database-engineer"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/business-analytics/skills/data-storytelling", "name": "data-storytelling", "kind": "skill", "official": false, "desc": "Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/business-analytics/skills/data-storytelling", "install": ["npx skills add wshobson/agents --skill data-storytelling"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "data", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/llm-application-dev/skills/embedding-strategies", "name": "embedding-strategies", "kind": "skill", "official": false, "desc": "Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/embedding-strategies", "install": ["npx skills add wshobson/agents --skill embedding-strategies"], "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/hybrid-search-implementation", "name": "hybrid-search-implementation", "kind": "skill", "official": false, "desc": "Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/hybrid-search-implementation", "install": ["npx skills add wshobson/agents --skill hybrid-search-implementation"], "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/similarity-search-patterns", "name": "similarity-search-patterns", "kind": "skill", "official": false, "desc": "Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/similarity-search-patterns", "install": ["npx skills add wshobson/agents --skill similarity-search-patterns"], "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/vector-index-tuning", "name": "vector-index-tuning", "kind": "skill", "official": false, "desc": "Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/vector-index-tuning", "install": ["npx skills add wshobson/agents --skill vector-index-tuning"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/embedding-anisotropy-check", "name": "embedding-anisotropy-check", "kind": "skill", "official": false, "desc": "Use before reporting any cosine similarity computed over embeddings or transformer hidden states, whether the agent computed the vectors or loaded them. Measures the anisotropy of each side, centres by the population mean, and scores matched pairs against a permuted floor taken from the same comparison. Use when comparing one model''s embeddings across precisions, runtimes or layers, when evaluating retrieval, probes or adapters, or before calling a similarity high, two conditions in agreement, ", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/embedding-anisotropy-check", "install": ["npx skills add github/awesome-copilot --skill embedding-anisotropy-check"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:mukul975/Anthropic-Cybersecurity-Skills/skills/assessing-vector-and-embedding-weaknesses", "name": "assessing-vector-and-embedding-weaknesses", "kind": "skill", "official": false, "desc": "Test RAG vector stores (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) for embedding inversion, cross-tenant data leakage, and data poisoning per OWASP LLM08:2025. Use when performing an authorized security assessment of a RAG pipeline's retrieval layer or auditing multi-tenant vector-store isolation.", "repo": "https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/HEAD/skills/assessing-vector-and-embedding-weaknesses", "install": ["npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill assessing-vector-and-embedding-weaknesses"], "stars": 36105, "pushed": "2026-08-31T04:32:44Z", "license": "Apache-2.0", "plat": "pinecone", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/rag-chroma", "name": "rag-chroma", "kind": "skill", "official": false, "desc": "Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/rag-chroma", "install": ["npx skills add davila7/claude-code-templates --skill rag-chroma"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "chroma", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/rag-faiss", "name": "rag-faiss", "kind": "skill", "official": false, "desc": "Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/rag-faiss", "install": ["npx skills add davila7/claude-code-templates --skill rag-faiss"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "faiss", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/rag-pinecone", "name": "rag-pinecone", "kind": "skill", "official": false, "desc": "Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/rag-pinecone", "install": ["npx skills add davila7/claude-code-templates --skill rag-pinecone"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "pinecone", "live": "Not checked (runs locally)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/ai-research/rag-qdrant", "name": "rag-qdrant", "kind": "skill", "official": false, "desc": "High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/ai-research/rag-qdrant", "install": ["npx skills add davila7/claude-code-templates --skill rag-qdrant"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "qdrant", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/google-cloud-solution-hybrid-search-alloydb", "name": "google-cloud-solution-hybrid-search-alloydb", "kind": "skill", "official": false, "desc": "Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T u", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/google-cloud-solution-hybrid-search-alloydb", "install": ["npx skills add google/skills --skill google-cloud-solution-hybrid-search-alloydb"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "google cloud", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/google-cloud-solution-rag-enterprise-search-gke-sqldb", "name": "google-cloud-solution-rag-enterprise-search-gke-sqldb", "kind": "skill", "official": false, "desc": "Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database ", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/google-cloud-solution-rag-enterprise-search-gke-sqldb", "install": ["npx skills add google/skills --skill google-cloud-solution-rag-enterprise-search-gke-sqldb"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "google cloud", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Canner/WrenAI", "name": "WrenAI", "kind": "mcp", "official": null, "desc": "GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.", "repo": "https://github.com/Canner/WrenAI", "install": [], "stars": 18235, "pushed": null, "license": null, "plat": "wrenai", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/15-rag/chroma", "name": "chroma", "kind": "skill", "official": false, "desc": "Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/15-rag/chroma", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill chroma"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/15-rag/faiss", "name": "faiss", "kind": "skill", "official": false, "desc": "Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/15-rag/faiss", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill faiss"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/15-rag/pinecone", "name": "pinecone", "kind": "skill", "official": false, "desc": "Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/15-rag/pinecone", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill pinecone"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Orchestra-Research/AI-Research-SKILLs/15-rag/qdrant", "name": "qdrant-vector-search", "kind": "skill", "official": false, "desc": "High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.", "repo": "https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/15-rag/qdrant", "install": ["npx skills add Orchestra-Research/AI-Research-SKILLs --skill qdrant-vector-search"], "stars": 14526, "pushed": "2026-10-11T02:56:34Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:brycewang-stanford/Auto-Empirical-Research-Skills/skills/25-HosungYou-Diverga/skills/i3", "name": "i3", "kind": "skill", "official": false, "desc": "RAG Builder with Parallel Document Processing Vector database construction with local embeddings (zero cost) Handles PDF download, text extraction, chunking, and vector database creation Absorbed B5 (Parallel Document Processor) capabilities Use when: building RAG, creating vector database, downloading PDFs, embedding documents, batch processing Triggers: build RAG, create vector database, download PDFs, embed documents, batch PDF processing", "repo": "https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/HEAD/skills/25-HosungYou-Diverga/skills/i3", "install": ["npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill i3"], "stars": 5013, "pushed": "2026-11-09T13:27:49Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/creating-data-visualizations", "name": "creating-data-visualizations", "kind": "skill", "official": false, "desc": "Create analytical charts and plots from existing data. Use for exploratory or reporting visuals such as bars, lines, scatters, and dashboards; not for publication-grade scientific figures or AI-generated schematics.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/creating-data-visualizations", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill creating-data-visualizations"], "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/geopandas", "name": "geopandas", "kind": "skill", "official": false, "desc": "Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between dat", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/geopandas", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill geopandas"], "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/matchms", "name": "matchms", "kind": "skill", "official": false, "desc": "Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/matchms", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill matchms"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "matchms", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/plotly", "name": "plotly", "kind": "skill", "official": false, "desc": "Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/plotly", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill plotly"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "plotly", "live": "Not checked (runs locally)"}, {"id": "skill:scenario-labs/skills/skills/scenario-sparc3d", "name": "scenario-sparc3d", "kind": "skill", "official": false, "desc": "Use when turning images or photos into 3D meshes with Sparc3D models on Scenario via MCP: single-image or multi-view image-to-3D, watertight meshes for game assets, AR/VR, or 3D printing, mesh-only versus PBR-textured output, face count targets, resolution tiers, or reconstructing human heads and busts with the Portrait variants. Keywords: Sparc3D 2.1 and 2.0, Hitem3D, image to 3D, img23d, multiview, watertight, PBR, GLB, OBJ, STL, FBX.", "repo": "https://github.com/scenario-labs/skills/tree/HEAD/skills/scenario-sparc3d", "install": ["npx skills add scenario-labs/skills --skill scenario-sparc3d"], "stars": 3764, "pushed": "2026-11-13T14:09:04Z", "license": "MIT", "plat": "sparc3d", "live": "Not checked (runs locally)"}, {"id": "skill:google-deepmind/science-skills/skills/alphafold_database_fetch_and_analyze", "name": "alphafold-database-fetch-and-analyze", "kind": "skill", "official": false, "desc": "Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.", "repo": "https://github.com/google-deepmind/science-skills/tree/HEAD/skills/alphafold_database_fetch_and_analyze", "install": ["npx skills add google-deepmind/science-skills --skill alphafold-database-fetch-and-analyze"], "stars": 3472, "pushed": "2026-11-10T18:46:47Z", "license": "Apache-2.0", "plat": "alphafold", "live": "Not checked (runs locally)"}, {"id": "skill:google-deepmind/science-skills/skills/alphagenome_single_variant_analysis", "name": "alphagenome-single-variant-analysis", "kind": "skill", "official": false, "desc": "Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing ", "repo": "https://github.com/google-deepmind/science-skills/tree/HEAD/skills/alphagenome_single_variant_analysis", "install": ["npx skills add google-deepmind/science-skills --skill alphagenome-single-variant-analysis"], "stars": 3472, "pushed": "2026-11-10T18:46:47Z", "license": "Apache-2.0", "plat": "alphagenome", "live": "Not checked (runs locally)"}, {"id": "skill:google-deepmind/science-skills/skills/alphagenome_variant_impact_score", "name": "alphagenome-variant-impact-score", "kind": "skill", "official": false, "desc": "Score, annotate, and analyze the functional impact of genetic variants using AlphaGenome Variant Impact (AVI) scores. Query variants in chr:pos:ref>alt format, annotate VCF/tabular callsets, perform saturation mutagenesis window scans (1-based closed chr:start-end), and extract GENCODE v46 GTF gene/exon/junction coordinates all via the AlphaGenome Atlas API.", "repo": "https://github.com/google-deepmind/science-skills/tree/HEAD/skills/alphagenome_variant_impact_score", "install": ["npx skills add google-deepmind/science-skills --skill alphagenome-variant-impact-score"], "stars": 3472, "pushed": "2026-11-10T18:46:47Z", "license": "Apache-2.0", "plat": "alphagenome", "live": "Not checked (runs locally)"}, {"id": "skill:microsoft/skills/.github/plugins/azure-skills/skills/azure-kusto", "name": "azure-kusto", "kind": "skill", "official": false, "desc": "Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-skills/skills/azure-kusto", "install": ["npx skills add microsoft/skills --skill azure-kusto"], "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-resource-manager-cosmosdb-dotnet", "name": "azure-resource-manager-cosmosdb-dotnet", "kind": "skill", "official": false, "desc": "Azure Resource Manager SDK for Cosmos DB in .NET. Use for MANAGEMENT PLANE operations: creating/managing Cosmos DB accounts, databases, containers, throughput settings, and RBAC via Azure Resource Manager. NOT for data plane operations (CRUD on documents) - use Microsoft.Azure.Cosmos for that. Triggers: \"Cosmos DB account\", \"create Cosmos account\", \"manage Cosmos resources\", \"ARM Cosmos\", \"CosmosDBAccountResource\", \"provision Cosmos DB\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-cosmosdb-dotnet", "install": ["npx skills add microsoft/skills --skill azure-resource-manager-cosmosdb-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-dotnet/skills/azure-search-documents-dotnet", "name": "azure-search-documents-dotnet", "kind": "skill", "official": false, "desc": "Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: \"Azure Search .NET\", \"SearchClient\", \"SearchIndexClient\", \"vector search C#\", \"semantic search .NET\", \"hybrid search\", \"Azure.Search.Documents\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-dotnet/skills/azure-search-documents-dotnet", "install": ["npx skills add microsoft/skills --skill azure-search-documents-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-python/skills/azure-search-documents-py", "name": "azure-search-documents-py", "kind": "skill", "official": false, "desc": "Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: \"azure-search-documents\", \"SearchClient\", \"SearchIndexClient\", \"vector search\", \"hybrid search\", \"semantic search\".", "repo": "https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-search-documents-py", "install": ["npx skills add microsoft/skills --skill azure-search-documents-py"], "stars": 3203, "pushed": "2026-11-13T22:48:25Z", "license": "MIT", "plat": "azure", "live": "Not checked (runs locally)"}, {"id": "skill:plugin87/ux-ui-agent-skills/.claude/skills/data-dashboard", "name": "data-dashboard", "kind": "skill", "official": false, "desc": "Build a dense, data-heavy screen - an analytics console or a trading terminal - with real charts drawn from tokens: candlestick, volume, depth, stacked area, waterfall, scatter, correlation matrix, donut, gauge, sparklines, heatmaps and order books. Use when the request is a dashboard, terminal, monitoring view, data console, or any screen whose job is to show many numbers at once. Covers the layout sequence, the SVG geometry, the accessibility contract per chart type, and the contrast traps tha", "repo": "https://github.com/plugin87/ux-ui-agent-skills/tree/HEAD/.claude/skills/data-dashboard", "install": ["npx skills add plugin87/ux-ui-agent-skills --skill data-dashboard"], "stars": 3171, "pushed": "2026-11-10T07:20:20Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-copy-number-cnv-visualization", "name": "bio-copy-number-cnv-visualization", "kind": "skill", "official": false, "desc": "Visualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-copy-number-cnv-visualization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-copy-number-cnv-visualization"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:open-gitagent/opengap/examples/nvidia-deep-researcher/skills/knowledge-retrieval", "name": "knowledge-retrieval", "kind": "skill", "official": false, "desc": "Semantic search over ingested documents using RAG (LlamaIndex/ChromaDB or Foundational RAG)", "repo": "https://github.com/open-gitagent/opengap/tree/HEAD/examples/nvidia-deep-researcher/skills/knowledge-retrieval", "install": ["npx skills add open-gitagent/opengap --skill knowledge-retrieval"], "stars": 2988, "pushed": "2026-07-02T21:15:45Z", "license": "MIT", "plat": "llamaindex", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ModernRelay/omnigraph", "name": "omnigraph", "kind": "mcp", "official": null, "desc": "Object-storage native graph database with branching and typed ontology", "repo": "https://github.com/ModernRelay/omnigraph", "install": [], "stars": 2447, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/arcgis-to-portaljs", "name": "arcgis-to-portaljs", "kind": "skill", "official": false, "desc": "Migrate a whole ArcGIS Hub site into a PortalJS Arc portal end-to-end. Harvests the Hub /data.json (DCAT-US) inventory, exports every FeatureService layer through the ArcGIS REST query API with resultOffset paging, converts each to the serverless dual tier (PMTiles render + GeoParquet query) with tabular items to Parquet, pushes everything to Cloudflare R2 via Git LFS, appends dual-tier datasets.json entries, and writes a source-vs-derived parity report. Use to move a City or sector ArcGIS Hub o", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/arcgis-to-portaljs", "install": ["npx skills add datopian/portaljs --skill arcgis-to-portaljs"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "arcgis-to-portaljs", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-add-chart", "name": "portaljs-add-chart", "kind": "skill", "official": false, "desc": "Add a chart (line, bar, area, pie, or scatter) to a dataset's showcase in a PortalJS portal. Installs recharts, writes a reusable Chart component, and renders it in the showcase Views section. Use when visualizing a dataset already registered in datasets.json.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-add-chart", "install": ["npx skills add datopian/portaljs --skill portaljs-add-chart"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-add-chart", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-add-dataset", "name": "portaljs-add-dataset", "kind": "skill", "official": false, "desc": "Add a dataset (CSV, TSV, JSON, or GeoJSON) to an existing PortalJS portal. Appends an entry to datasets.json so the catalog and showcase render it automatically; routes the data by source (local file vs remote URL) — R2 via Git LFS by default, remote URLs by passthrough. Use when registering a new dataset in a scaffolded portal.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-add-dataset", "install": ["npx skills add datopian/portaljs --skill portaljs-add-dataset"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-add-dataset", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-add-dcat", "name": "portaljs-add-dcat", "kind": "skill", "official": false, "desc": "Make a PortalJS portal harvestable by national/EU/US open-data portals — emit standards-compliant DCAT catalog feeds (DCAT 2/3, DCAT-AP, DCAT-US, national profiles) in JSON-LD, Turtle, and RDF/XML at build, with autodiscovery and per-profile conformance checking. Use when a portal needs to be harvested by data.europa.eu, data.gov, or a national open-data catalog.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-add-dcat", "install": ["npx skills add datopian/portaljs --skill portaljs-add-dcat"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-add-dcat", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-add-geo", "name": "portaljs-add-geo", "kind": "skill", "official": false, "desc": "Auto-ingest a geospatial file (GeoJSON, Shapefile, GeoPackage, KML/KMZ, FlatGeobuf, CSV-with-geometry) into a PortalJS portal on the user's own machine, with no server. Normalizes CRS to EPSG:4326, derives a PMTiles render tier and a GeoParquet query tier, pushes all three artifacts to Cloudflare R2 via Git LFS, and appends one dual-tier datasets.json entry the showcase auto-renders. Use when the source is a vector geo format that needs a map or spatial-query view.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-add-geo", "install": ["npx skills add datopian/portaljs --skill portaljs-add-geo"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-add-geo", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-add-map", "name": "portaljs-add-map", "kind": "skill", "official": false, "desc": "Render a GeoJSON dataset on an interactive Leaflet map in the Views section of a dataset's showcase. Installs react-leaflet and a Map component, then renders the map for the chosen dataset. Use when a dataset's data is geographic and a map view is needed alongside the showcase's default metadata and download.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-add-map", "install": ["npx skills add datopian/portaljs --skill portaljs-add-map"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-add-map", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-add-resource", "name": "portaljs-add-resource", "kind": "skill", "official": false, "desc": "Add another file (resource) to an EXISTING dataset in a PortalJS portal — a data dictionary, methodology, or an additional data file. Turns a single-file dataset into a multi-resource one; the showcase renders a section per resource. Use when a dataset needs a second file, such as a data dictionary, methodology doc, or an additional period's data.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-add-resource", "install": ["npx skills add datopian/portaljs --skill portaljs-add-resource"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-add-resource", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-architect", "name": "portaljs-architect", "kind": "skill", "official": false, "desc": "Recommend a data-portal architecture (storage, compute, catalog, access, hosting, metadata) from stated needs, then hand off to the build skills. The advisory entry point. Use when starting a new data-portal project and the underlying architecture has not yet been decided.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-architect", "install": ["npx skills add datopian/portaljs --skill portaljs-architect"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-architect", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-check-data-quality", "name": "portaljs-check-data-quality", "kind": "skill", "official": false, "desc": "Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-check-data-quality", "install": ["npx skills add datopian/portaljs --skill portaljs-check-data-quality"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-check-data-quality", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-connect-ckan", "name": "portaljs-connect-ckan", "kind": "skill", "official": false, "desc": "Wire a scaffolded PortalJS portal to a CKAN backend over its API. Generates a tiny server-side fetch client (no runtime dependency) and feeds the /search catalog and /@namespace/slug showcases from CKAN instead of datasets.json. Use when connecting an existing portal to a live CKAN instance instead of a static manifest.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-connect-ckan", "install": ["npx skills add datopian/portaljs --skill portaljs-connect-ckan"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-connect-ckan", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-define-schema", "name": "portaljs-define-schema", "kind": "skill", "official": false, "desc": "Define a dataset's metadata profile — infer a Frictionless Table Schema from its data, add Data Package metadata (license, sources, keywords), and write it into datasets.json so the showcase renders a typed field table. Extend or customize via the L0-L3 profile ladder. Use when a registered dataset needs field types, constraints, or catalog metadata before publishing.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-define-schema", "install": ["npx skills add datopian/portaljs --skill portaljs-define-schema"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-define-schema", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-deploy", "name": "portaljs-deploy", "kind": "skill", "official": false, "desc": "Deploy a PortalJS portal to PortalJS Arc — Datopian-managed static hosting on Cloudflare. Builds a static export, uploads it, and returns a live SLUG.arc.portaljs.com URL. One command, one target. Use when a portal is ready to publish or redeploy to a live URL.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-deploy", "install": ["npx skills add datopian/portaljs --skill portaljs-deploy"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-deploy", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-migrate", "name": "portaljs-migrate", "kind": "skill", "official": false, "desc": "Migrate (harvest) datasets between open-data platforms. Reads CKAN, a DCAT-US /data.json catalog (DKAN, ArcGIS Hub, data.gov), a DCAT / DCAT-AP RDF feed (JSON-LD, Turtle, or RDF/XML), Socrata, OpenDataSoft, or an ArcGIS FeatureServer, and writes them to a static PortalJS catalog or pushes them into a CKAN instance over its API. Use when moving datasets from an external open-data platform into a PortalJS portal, or bridging one CKAN instance to another.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-migrate", "install": ["npx skills add datopian/portaljs --skill portaljs-migrate"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-migrate", "live": "Not checked (runs locally)"}, {"id": "skill:datopian/portaljs/skills/portaljs-new-portal", "name": "portaljs-new-portal", "kind": "skill", "official": false, "desc": "Scaffold a new PortalJS data portal from a brief. Copies the canonical template from examples/portaljs-catalog and substitutes project tokens. Use when starting a brand-new data portal project from scratch.", "repo": "https://github.com/datopian/portaljs/tree/HEAD/skills/portaljs-new-portal", "install": ["npx skills add datopian/portaljs --skill portaljs-new-portal"], "stars": 2370, "pushed": "2026-10-28T12:04:20Z", "license": "MIT", "plat": "portaljs-new-portal", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/analytics", "name": "analytics", "kind": "skill", "official": false, "desc": "When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions \"set up tracking,\" \"GA4,\" \"Google Analytics,\" \"event tracking,\" \"UTM parameters,\" \"tag manager,\" \"GTM,\" \"analytics implementation,\" \"tracking plan,\" \"how do I measure this,\" \"Mixpanel,\" \"Segment,\" \"are my events firing,\" or \"analytics isn't working.\" Use this whenever someone asks how to know if something is working or wants to measure marketing results. For getting conversions in", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/analytics", "install": ["npx skills add LeoYeAI/agent-master-skills --skill analytics"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "google analytics", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/analyticscli-ts-sdk", "name": "analyticscli-ts-sdk", "kind": "skill", "official": false, "desc": "Use when integrating or upgrading the AnalyticsCLI TypeScript SDK in web, TypeScript, React Native, or Expo apps.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/analyticscli-ts-sdk", "install": ["npx skills add LeoYeAI/agent-master-skills --skill analyticscli-ts-sdk"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "analyticscli", "live": "Not checked (runs locally)"}, {"id": "skill:timescale/pg-aiguide/skills/pgvector-semantic-search", "name": "pgvector-semantic-search", "kind": "skill", "official": false, "desc": "Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets", "repo": "https://github.com/timescale/pg-aiguide/tree/HEAD/skills/pgvector-semantic-search", "install": ["npx skills add timescale/pg-aiguide --skill pgvector-semantic-search"], "stars": 1883, "pushed": "2026-11-12T20:58:38Z", "license": "Apache-2.0", "plat": "postgresql", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:qdrant/mcp-server-qdrant", "name": "mcp-server-qdrant", "kind": "mcp", "official": null, "desc": "An official Qdrant Model Context Protocol (MCP) server implementation", "repo": "https://github.com/qdrant/mcp-server-qdrant", "install": [], "stars": 1570, "pushed": null, "license": null, "plat": "qdrant", "live": "Not checked (runs locally)"}, {"id": "skill:langchain-ai/langchain-skills/config/skills/langchain-rag", "name": "langchain-rag", "kind": "skill", "official": false, "desc": "INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).", "repo": "https://github.com/langchain-ai/langchain-skills/tree/HEAD/config/skills/langchain-rag", "install": ["npx skills add langchain-ai/langchain-skills --skill langchain-rag"], "stars": 1340, "pushed": "2026-11-13T22:56:27Z", "license": "MIT", "plat": "chroma", "live": "Not checked (runs locally)"}, {"id": "mcp:com.arcadedb/mcp-server", "name": "ArcadeDB MCP Server", "kind": "mcp", "official": null, "desc": "Built-in MCP server for ArcadeDB multi-model database (graph, document, vector, time-series)", "repo": "https://github.com/ArcadeData/arcadedb", "install": ["oci:docker.io/arcadedata/arcadedb:26.4.1-SNAPSHOT"], "stars": 1250, "pushed": "2026-11-13T03:11:46Z", "license": "Apache-2.0", "plat": "arcadedb", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:orneryd/NornicDB", "name": "NornicDB", "kind": "mcp", "official": null, "desc": "Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes while adding intelligent features like schemas, managed embeddings, reranking+llm, GPU accel, Au", "repo": "https://github.com/orneryd/NornicDB", "install": [], "stars": 931, "pushed": null, "license": null, "plat": "neo4j, qdrant", "live": "Not checked (runs locally)"}, {"id": "skill:njzjz/nsfc-agent-skills/nsfc-figure", "name": "nsfc-figure", "kind": "skill", "official": false, "desc": "为 NSFC 申请书创建或重绘概念图、研究内容关系图和技术路线图；当交付物需要可编辑源图、稳定版式或可验证导出时，优先生成 Draw.io XML。纯数据统计图、照片处理或论文原图复用不应触发本 skill。", "repo": "https://github.com/njzjz/nsfc-agent-skills/tree/HEAD/nsfc-figure", "install": ["npx skills add njzjz/nsfc-agent-skills --skill nsfc-figure"], "stars": 787, "pushed": "2026-08-27T09:06:49Z", "license": null, "plat": "nsfc", "live": "Not checked (runs locally)"}, {"id": "skill:dbt-labs/dbt-agent-skills/skills/dbt-extras/skills/creating-mermaid-dbt-dag", "name": "creating-mermaid-dbt-dag", "kind": "skill", "official": false, "desc": "Generates a Mermaid flowchart diagram of dbt model lineage using MCP tools, manifest.json, or direct code parsing as fallbacks. Use when visualizing dbt model lineage and dependencies as a Mermaid diagram in markdown format.", "repo": "https://github.com/dbt-labs/dbt-agent-skills/tree/HEAD/skills/dbt-extras/skills/creating-mermaid-dbt-dag", "install": ["npx skills add dbt-labs/dbt-agent-skills --skill creating-mermaid-dbt-dag"], "stars": 763, "pushed": "2026-11-06T10:17:14Z", "license": "Apache-2.0", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:chroma-core/chroma-mcp", "name": "chroma-mcp", "kind": "mcp", "official": null, "desc": "A Model Context Protocol (MCP) server implementation that provides database capabilities for Chroma", "repo": "https://github.com/chroma-core/chroma-mcp", "install": [], "stars": 604, "pushed": null, "license": null, "plat": "chroma", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/antv-g2-chart", "name": "antv-g2-chart", "kind": "skill", "official": false, "desc": "Use this skill whenever the user wants to create, customize, or troubleshoot G2 v5 chart visualizations. Triggers include: any mention of 'G2', 'antv g2', '@antv/g2', 'G2 chart', 'G2 可视化', or requests to produce charts like bar charts (柱状图), line charts (折线图), pie charts (饼图), scatter plots (散点图), area charts (面积图), heatmap (热力图), radar charts (雷达图), treemap (矩形树图), funnel charts (漏斗图), sankey diagrams (桑基图), gauge (仪表盘), wordcloud (词云), boxplot (箱线图), as well as G2-specific topics like encode c", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/antv-g2-chart", "install": ["npx skills add antvis/chart-visualization-skills --skill antv-g2-chart"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "antv g2", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/antv-g6-graph", "name": "antv-g6-graph", "kind": "skill", "official": false, "desc": "Use this skill whenever the user wants to create, customize, or troubleshoot G6 v5 graph/network visualizations. Triggers include: any mention of 'G6', 'antv g6', '@antv/g6', 'G6 graph', 'G6 图', '网络图', '关系图', '拓扑图', '树形图', '流程图', '思维导图', '鱼骨图', '力导向图', 'force graph', 'network visualization', 'node-edge diagram', 'graph layout', 'tree layout', 'dagre layout', 'mindmap', 'social network', or requests about G6 node styles, edge types, behaviors, plugins, layouts, combos, or data structures. Also us", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/antv-g6-graph", "install": ["npx skills add antvis/chart-visualization-skills --skill antv-g6-graph"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "antv g6", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/antv-gpt-vis", "name": "antv-gpt-vis", "kind": "skill", "official": false, "desc": "Recommend and generate appropriate data visualization charts using the GPT-Vis library. Supports two output modes — (1) Syntax mode, which generates Syntax or JSON configurations, and (2) Code mode, which generates complete runnable code. Supports 26 chart types.", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/antv-gpt-vis", "install": ["npx skills add antvis/chart-visualization-skills --skill antv-gpt-vis"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "gpt-vis", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/antv-infographic", "name": "antv-infographic", "kind": "skill", "official": false, "desc": "Create beautiful infographics based on given text content. Use when users request to create infographics.", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/antv-infographic", "install": ["npx skills add antvis/chart-visualization-skills --skill antv-infographic"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "antv", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/antv-t8-ntv", "name": "antv-t8-ntv", "kind": "skill", "official": false, "desc": "Generate structured narrative text visualizations from data using T8 Syntax. Use when users want to create data interpretation reports, summaries, or structured articles with semantic entity annotations. T8 is designed for unstructured data visualization where T stands for Text and 8 represents a byte of 8 bits, symbolizing deep insights beneath the text.", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/antv-t8-ntv", "install": ["npx skills add antvis/chart-visualization-skills --skill antv-t8-ntv"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "t8", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/antv-x6-editor", "name": "antv-x6-editor", "kind": "skill", "official": false, "desc": "Use this skill whenever the user wants to create, customize, or troubleshoot X6 v3 graph editor diagrams. Triggers include: any mention of 'X6', 'antv x6', '@antv/x6', 'X6 editor', 'X6 图编辑', '流程图', 'DAG', 'ER图', '实体关系图', '血缘图', '组织架构图', 'UML类图', 'flowchart', 'DAG diagram', 'ER diagram', 'lineage graph', 'org chart', 'network topology', 'stencil', 'drag-and-drop editor', 'port connection', 'node port edge', 'graph editor', 'diagram editor', or requests about X6 node/edge styling, plugins (Selecti", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/antv-x6-editor", "install": ["npx skills add antvis/chart-visualization-skills --skill antv-x6-editor"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "antv x6", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/chart-visualization", "name": "chart-visualization", "kind": "skill", "official": false, "desc": "将数据可视化为图表。当用户需要生成柱状图、折线图、饼图、散点图、雷达图、桑基图、思维导图、流程图等图表时调用此技能，通过 curl 工具调用 AntV API 生成图表图片", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/chart-visualization", "install": ["npx skills add antvis/chart-visualization-skills --skill chart-visualization"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "antv", "live": "Not checked (runs locally)"}, {"id": "skill:antvis/chart-visualization-skills/skills/gpt-vis", "name": "gpt-vis", "kind": "skill", "official": false, "desc": "推荐并生成合适的数据可视化图表，使用 GPT-Vis 库。支持两种输出模式：（1）语法模式——生成 Syntax 或 JSON 配置；（2）代码模式——生成完整的运行代码。支持 26 种图表类型。", "repo": "https://github.com/antvis/chart-visualization-skills/tree/HEAD/skills/gpt-vis", "install": ["npx skills add antvis/chart-visualization-skills --skill gpt-vis"], "stars": 534, "pushed": "2026-09-22T01:46:48Z", "license": "MIT", "plat": "gpt-vis", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:redis/redis-vl-python", "name": "redis-vl-python", "kind": "mcp", "official": null, "desc": "Redis Vector Library. The AI-native Python client for Redis.", "repo": "https://github.com/redis/redis-vl-python", "install": [], "stars": 437, "pushed": null, "license": null, "plat": "redis", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.brycewang-stanford/statspai", "name": "StatsPAI", "kind": "mcp", "official": null, "desc": "Econometrics and causal inference (DiD, IV, RD, synthetic control, DML) with Stata/R parity grades", "repo": "https://github.com/brycewang-stanford/StatsPAI", "install": ["pypi:statspai"], "stars": 374, "pushed": "2026-11-13T09:45:21Z", "license": "MIT", "plat": "statspai", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:rush-db/rushdb", "name": "rushdb", "kind": "mcp", "official": null, "desc": "RushDB is a self-describing, graph-native database that helps AI agents reason over data with confidence: progressively introspect what's queryable—without guesswork.", "repo": "https://github.com/rush-db/rushdb", "install": [], "stars": 357, "pushed": null, "license": null, "plat": "rushdb", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:braincrew-lab/langconnect-client", "name": "langconnect-client", "kind": "mcp", "official": null, "desc": "A Modern GUI Interface for Vector Database Management(Supports MCP integration)", "repo": "https://github.com/braincrew-lab/langconnect-client", "install": [], "stars": 325, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:lyonzin/knowledge-rag", "name": "knowledge-rag", "kind": "mcp", "official": null, "desc": "Local RAG server for Claude Code, Cursor and other MCP clients: hybrid BM25 + vector search with cross-encoder reranking on by default, 13 MCP tools, 33 file types including code, re-indexing on file changes. One Python process with embedded ChromaDB and FastEmbed ONNX: no Docker, database server or", "repo": "https://github.com/lyonzin/knowledge-rag", "install": [], "stars": 306, "pushed": null, "license": null, "plat": "anthropic", "live": "Not checked (runs locally)"}, {"id": "skill:metadist/synaplan-desktop/skills/bundled/chart", "name": "chart", "kind": "skill", "official": false, "desc": "Turn a CSV into a clean bar or line chart, embedded as an SVG in a standalone HTML page (multi-series, light and dark, no dependencies).", "repo": "https://github.com/metadist/synaplan-desktop/tree/HEAD/skills/bundled/chart", "install": ["npx skills add metadist/synaplan-desktop --skill chart"], "stars": 297, "pushed": "2026-10-27T18:32:50Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.geolens-io/geolens", "name": "GeoLens", "kind": "mcp", "official": null, "desc": "Read-only access to a self-hosted GeoLens spatial catalog: datasets, features, maps, sandboxed SQL.", "repo": "https://github.com/geolens-io/geolens", "install": ["pypi:geolens-mcp"], "stars": 297, "pushed": "2026-11-13T03:05:11Z", "license": "Apache-2.0", "plat": "geolens", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:qdrant-skills", "name": "qdrant-skills", "kind": "claude-plugin", "official": null, "desc": "Agent skills for Qdrant vector search covering scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage across Python, TypeScript, Rust, Go, .NET, and Java.", "repo": "https://github.com/qdrant/skills.git", "install": ["claude plugin install qdrant-skills@claude-plugins-official"], "stars": 270, "pushed": "2026-11-11T15:20:23Z", "license": "Apache-2.0", "plat": "qdrant", "live": "Not checked (runs locally)"}, {"id": "skill:jeecgboot/skills/jeecg-onlchart", "name": "jeecg-onlchart", "kind": "skill", "official": false, "desc": "Use when user asks to create/edit Online graph charts, data visualization, or says \"创建图表\", \"生成图表\", \"新建图表\", \"做一个图表\", \"online图表\", \"数据图表\", \"柱状图\", \"折线图\", \"饼图\", \"统计图\", \"可视化\", \"chart\", \"graph\", \"create chart\", \"generate chart\", \"bar chart\", \"line chart\", \"pie chart\". Also triggers when user describes chart requirements like \"做一个销售柱状图\" or mentions data visualization like \"用图表展示男女比例\".", "repo": "https://github.com/jeecgboot/skills/tree/HEAD/jeecg-onlchart", "install": ["npx skills add jeecgboot/skills --skill jeecg-onlchart"], "stars": 250, "pushed": "2026-10-14T16:09:45Z", "license": "Apache-2.0", "plat": "jeecgboot", "live": "Not checked (runs locally)"}, {"id": "skill:jeecgboot/skills/jeecg-onlreport", "name": "jeecg-onlreport", "kind": "skill", "official": false, "desc": "Use when user asks to create/edit/query Online reports, SQL reports, data reports, or says \"创建报表\", \"生成报表\", \"新建报表\", \"查询报表\", \"online报表\", \"SQL报表\", \"数据报表\", \"统计报表\", \"create report\", \"generate report\", \"data report\". Also triggers when user describes report requirements like \"做一个销售统计报表\", mentions JeecgBoot cgreport/online report, or says \"查看现有报表\" / \"列出所有报表\". This skill handles Online 报表 (SQL-driven data display/reports), not Online forms (cgform) or designer forms (desform).", "repo": "https://github.com/jeecgboot/skills/tree/HEAD/jeecg-onlreport", "install": ["npx skills add jeecgboot/skills --skill jeecg-onlreport"], "stars": 250, "pushed": "2026-10-14T16:09:45Z", "license": "Apache-2.0", "plat": "jeecgboot", "live": "Not checked (runs locally)"}, {"id": "skill:jeecgboot/skills/jimubi-bigscreen", "name": "jimubi-bigscreen", "kind": "skill", "official": false, "desc": "Use when user asks to create/design a big screen (大屏), full-screen data visualization, or says \"创建大屏\", \"生成大屏\", \"新建大屏\", \"设计大屏\", \"做一个大屏\", \"BI大屏\", \"数据大屏\", \"可视化大屏\", \"监控大屏\", \"create big screen\", \"design big screen\", \"BI visualization big screen\". Also triggers when user describes big screen requirements like \"做一个销售数据大屏\" or mentions full-screen display like \"展厅展示\", \"监控室大屏\". Make sure to use this skill for big screens (大屏) — NOT dashboards (仪表盘/看板), which use a completely different layout and styling s", "repo": "https://github.com/jeecgboot/skills/tree/HEAD/jimubi-bigscreen", "install": ["npx skills add jeecgboot/skills --skill jimubi-bigscreen"], "stars": 250, "pushed": "2026-10-14T16:09:45Z", "license": "Apache-2.0", "plat": "jimubi", "live": "Not checked (runs locally)"}, {"id": "skill:jeecgboot/skills/jimureport", "name": "jimureport", "kind": "skill", "official": false, "desc": "积木报表生成器 — 自然语言描述报表需求或提供截图，自动生成积木报表（支持数据报表、打印报表、分组报表、循环报表、数据填报等全类型）。Use when user says \"积木报表\", \"jmreport\", \"Excel报表\", \"数据填报\", \"可视化报表\", \"打印报表\", \"分组报表\", \"循环报表\", \"按照截图生成报表\", \"创建积木报表\", \"做一个可视化报表\", \"积木设计器\", \"create jimureport\", \"visual report\". Also triggers when user describes report requirements involving Excel-like layouts, data binding with #{}, or multi-sheet reports, or provides a screenshot to generate a report.", "repo": "https://github.com/jeecgboot/skills/tree/HEAD/jimureport", "install": ["npx skills add jeecgboot/skills --skill jimureport"], "stars": 250, "pushed": "2026-10-14T16:09:45Z", "license": "Apache-2.0", "plat": "jimureport", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:zilliztech/mcp-server-milvus", "name": "mcp-server-milvus", "kind": "mcp", "official": null, "desc": "Model Context Protocol Servers for Milvus", "repo": "https://github.com/zilliztech/mcp-server-milvus", "install": [], "stars": 245, "pushed": null, "license": null, "plat": "milvus", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:judegomila/OnCo", "name": "OnCo", "kind": "mcp", "official": null, "desc": "OnCo: total information dominance on cancer. A public, cited knowledge graph of oncology with a website, JSON API, MCP server and CLI: one page per cancer, treatment, target, trial, institution, person and idea.", "repo": "https://github.com/judegomila/OnCo", "install": [], "stars": 245, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.VictoriaMetrics-Community/mcp-victoriametrics", "name": "io.github.VictoriaMetrics-Community/mcp-victoriametrics", "kind": "mcp", "official": null, "desc": "MCP Server for VictoriaMetrics. Provides integration with VictoriaMetrics API and documentation", "repo": "https://github.com/VictoriaMetrics-Community/mcp-victoriametrics", "install": ["oci:ghcr.io/victoriametrics-community/mcp-victoriametrics:1.18.0"], "stars": 244, "pushed": "2026-11-12T11:38:47Z", "license": "Apache-2.0", "plat": "victoriametrics", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.VictoriaMetrics/mcp-victoriametrics", "name": "io.github.VictoriaMetrics/mcp-victoriametrics", "kind": "mcp", "official": null, "desc": "MCP Server for VictoriaMetrics. Provides integration with VictoriaMetrics API and documentation", "repo": "https://github.com/VictoriaMetrics/mcp-victoriametrics", "install": ["oci:ghcr.io/victoriametrics/mcp-victoriametrics:1.120.1"], "stars": 244, "pushed": "2026-11-12T11:38:47Z", "license": "Apache-2.0", "plat": "victoriametrics", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:stevereiner/flexible-graphrag", "name": "flexible-graphrag", "kind": "mcp", "official": null, "desc": "GraphRAG, RAG, LlamaIndex, LangChain, 15 Prop Graph, 4 RDF, 10 Vector, 3 Search DBs. 14 data srcs, auto sync, Alfresco,  Nuxeo, SharePoint, Box, CMIS, S3,GCS,Azure, web. KG auto-bld, Ontologies, 13 LLM providers, Docling, LlamaParse, LiteParse, Hybrid Search, AI Chat. TypeScript React, Vue, Angular,", "repo": "https://github.com/stevereiner/flexible-graphrag", "install": [], "stars": 201, "pushed": null, "license": null, "plat": "graphrag", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:samyama-ai/samyama-graph", "name": "samyama-graph", "kind": "mcp", "official": null, "desc": "Rust graph-vector database: OpenCypher (99.9% of evaluated TCK scenarios pass), vector search, graph algorithms, RESP + HTTP. LDBC SNB Interactive and BI complete; 1B edges on one machine.", "repo": "https://github.com/samyama-ai/samyama-graph", "install": [], "stars": 187, "pushed": null, "license": null, "plat": "opencypher", "live": "Not checked (runs locally)"}, {"id": "skill:veniceai/skills/skills/venice-embeddings", "name": "venice-embeddings", "kind": "skill", "official": false, "desc": "Call POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), text-only input (token arrays rejected), per-input and batch limits, per-model dimensions/privacy, the text-embedding-ada-002 alias, API-key privacy gating, OpenAI/LangChain compatibility, response compression (gzip/br), and practical usage for retrieval, clustering, and RAG.", "repo": "https://github.com/veniceai/skills/tree/HEAD/skills/venice-embeddings", "install": ["npx skills add veniceai/skills --skill venice-embeddings"], "stars": 170, "pushed": "2026-11-10T23:48:57Z", "license": "MIT", "plat": "venice", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:weaviate/mcp-server-weaviate", "name": "mcp-server-weaviate", "kind": "mcp", "official": null, "desc": "MCP (Model Context Protocol) server for Weaviate", "repo": "https://github.com/weaviate/mcp-server-weaviate", "install": [], "stars": 162, "pushed": null, "license": null, "plat": "weaviate", "live": "Not checked (runs locally)"}, {"id": "skill:yzlnew/infra-skills/megatron-memory-estimator", "name": "megatron-memory-estimator", "kind": "skill", "official": false, "desc": "Estimate GPU memory usage for Megatron-based MoE (Mixture of Experts) and dense models. Use when users need to (1) estimate memory from HuggingFace model configs (DeepSeek-V3, Qwen, etc.), (2) plan GPU resource allocation for training, (3) compare different parallelism strategies (TP/PP/EP/CP), (4) determine if a model fits in available GPU memory, or (5) optimize training configurations for memory efficiency.", "repo": "https://github.com/yzlnew/infra-skills/tree/HEAD/megatron-memory-estimator", "install": ["npx skills add yzlnew/infra-skills --skill megatron-memory-estimator"], "stars": 158, "pushed": "2026-07-09T09:35:22Z", "license": null, "plat": "megatron", "live": "Not checked (runs locally)"}, {"id": "skill:sundial-org/skills/skills/visualization-skill", "name": "cs448b-visualization", "kind": "skill", "official": false, "desc": "Data visualization design based on Stanford CS448B. Use for: (1) choosing chart types, (2) selecting visual encodings, (3) critiquing visualizations, (4) building D3.js visualizations, (5) designing interactions/animations, (6) choosing colors, (7) visualizing networks, (8) visualizing text. Covers Bertin, Mackinlay, Cleveland & McGill.", "repo": "https://github.com/sundial-org/skills/tree/HEAD/skills/visualization-skill", "install": ["npx skills add sundial-org/skills --skill cs448b-visualization"], "stars": 153, "pushed": "2026-07-15T23:46:34Z", "license": null, "plat": "d3.js", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/audit-ml-pipeline", "name": "audit-ml-pipeline", "kind": "skill", "official": false, "desc": "Audit a persisted skore report read-only and produce a reusable digest. Do not execute it. Run `materialize.py` once. Never call `skore.evaluate` or `project.put`. TRIGGER when a completed run needs an audit, the user asks to audit, show, or re-audit an experiment, a re-run overwrote the same `put()` key, or they want a narrative of a past experiment without `journal/ideas/` files. STOP when `status.setup.pending` is non-empty (load `setup-ml-project`), or when there is no approved design, no re", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/audit-ml-pipeline", "install": ["npx skills add probabl-ai/skore-skills --skill audit-ml-pipeline"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/build-ml-pipeline", "name": "build-ml-pipeline", "kind": "skill", "official": false, "desc": "Declare a skrub DataOps graph from the data source to the predictor after framing is locked. Stateless steps use `.skb.apply_func`. Stateful steps use `.skb.apply`. After the graph exists, attach the locked splitter and any non-default score. No fit, tune, persistence, or `skore.evaluate`. TRIGGER when writing or editing any link from data source to predictor (loaders, preprocessing, features, composition, the final estimator), including a pure-Python function on that path, a step added or reord", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/build-ml-pipeline", "install": ["npx skills add probabl-ai/skore-skills --skill build-ml-pipeline"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skrub", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/evaluate-ml-pipeline", "name": "evaluate-ml-pipeline", "kind": "skill", "official": false, "desc": "Evaluate one sklearn-compatible learner with the locked validation scheme and persist structured skore reports. The splitter and any non-default score are already on the DataOp from `build-ml-pipeline`. This skill writes the call without `splitter=`, persists the report, and records the locator. When the locked split is the training table and test table that shipped with the data, fit on the training table, then pass `splitter=\"prefit\"` with only the test table. When `model-ml-pipeline` dispatch", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/evaluate-ml-pipeline", "install": ["npx skills add probabl-ai/skore-skills --skill evaluate-ml-pipeline"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/frame-ml-problem", "name": "frame-ml-problem", "kind": "skill", "official": false, "desc": "Record the problem, deployment setting, metric, baseline, and fold count before any model code. Ask every missing decision in one turn, from `frame show`. Does not write Python, estimator hyperparameters, or splitter constructors. TRIGGER when the user asks which metric to compare on, how new rows should be split, which baseline to use, or says a problem constraint changed. Not when they ask to run evaluation or CV. HOW TO USE: run `python -m skore_skills frame show`. Read each reference named i", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/frame-ml-problem", "install": ["npx skills add probabl-ai/skore-skills --skill frame-ml-problem"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/manage-ml-backlog", "name": "manage-ml-backlog", "kind": "skill", "official": false, "desc": "Record experiment outcomes and triage idea files. A promoted idea moves from the Ideas table into a Backlog row. Record an experiment outcome in History and triage idea files. Summarize open, discarded, and aside ideas in the JOURNAL Ideas table. Move a row into Backlog when it is promoted, and keep the idea file. Also supports the model-entry selection mode: show real B<N> rows supplied by the deterministic CLI and consume one into a proposal. Trigger after audit, when a run finishes, when the ", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/manage-ml-backlog", "install": ["npx skills add probabl-ai/skore-skills --skill manage-ml-backlog"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/model-ml-pipeline", "name": "model-ml-pipeline", "kind": "skill", "official": false, "desc": "Require locked problem framing, write the first baseline design note for approval, then coordinate later choices, build, smoke testing, evaluation, and audit. On a first landing, write the locked baseline design note and ask for approval. Later landings offer only the choices the workspace justifies (an EDA proposal, Backlog, discussion). A missing modeling lock loads `frame-ml-problem` and stops. After a design is approved, coordinate build (pytest smoke is a build sub-step), the user's Evaluat", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/model-ml-pipeline", "install": ["npx skills add probabl-ai/skore-skills --skill model-ml-pipeline"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/research-ml-practice", "name": "research-ml-practice", "kind": "skill", "official": false, "desc": "Literature research for an ML methodology concern. Trigger when explore-ml-data or build-ml-pipeline load this skill. Not for routine profiling or a single API signature — use `api get` for symbols. Not a session owner — callers distill and write project files. HOW TO USE: skip intake fields the caller already supplied. Abstract the problem class before searching (never the dataset proper name). Survey extra analyses or depth a named concern; search distinct angles, fetch primary sources, follow", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/research-ml-practice", "install": ["npx skills add probabl-ai/skore-skills --skill research-ml-practice"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/review-ml-choices", "name": "review-ml-choices", "kind": "skill", "official": false, "desc": "Show stored project choices and re-enter the skill that can change one. Trigger when the user asks what we decided, what the current settings are, or to change a stored project choice. SKIP an explicit sync, export, or “a constraint changed” request — those load sync-ml-reports, export-ml-project, or frame-ml-problem directly. HOW TO USE: run `review choices`, show that board, then load one owning skill. Do not policy set from here.", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/review-ml-choices", "install": ["npx skills add probabl-ai/skore-skills --skill review-ml-choices"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/review-ml-experiment", "name": "review-ml-experiment", "kind": "skill", "official": false, "desc": "Read the stored report, then write one idea file and one Ideas row per candidate. Trigger after a successful evaluate, on \"review this stem\", or when review consent is audit or proceed. Do not write History, Backlog, or a design note.", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/review-ml-experiment", "install": ["npx skills add probabl-ai/skore-skills --skill review-ml-experiment"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/search-ml-literature", "name": "search-ml-literature", "kind": "skill", "official": false, "desc": "Search scientific and technical sources and write one idea file and one Ideas row for the direction the user confirms. Comes after review, beside the backlog. Do not call research-ml-practice. Do not write History, Backlog, or a design note. Do not pick a winner.", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/search-ml-literature", "install": ["npx skills add probabl-ai/skore-skills --skill search-ml-literature"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "skill:probabl-ai/skore-skills/skills/setup-ml-project", "name": "setup-ml-project", "kind": "skill", "official": false, "desc": "Ask only the pending setup pieces. When Git is selected, commit the current files before setup writes only if that check finds files to preserve. Trigger when the user asks to set up or bootstrap a workspace, or when a pipeline stage finds status.setup.pending non-empty. The question tells the user to select each piece they want. It starts by saying that before any modeling, the workspace should be set up. Do not say the order the pieces will run. Do not offer a piece that is already done or dec", "repo": "https://github.com/probabl-ai/skore-skills/tree/HEAD/skills/setup-ml-project", "install": ["npx skills add probabl-ai/skore-skills --skill setup-ml-project"], "stars": 145, "pushed": "2026-11-09T23:25:33Z", "license": "BSD-3-Clause", "plat": "skore", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:sib-swiss/sparql-llm", "name": "sparql-llm", "kind": "mcp", "official": null, "desc": "🦜✨ Chat system, MCP server, and reusable components to improve LLMs capabilities when generating SPARQL queries", "repo": "https://github.com/sib-swiss/sparql-llm", "install": [], "stars": 134, "pushed": null, "license": null, "plat": "sparql", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:litegraphdb/litegraph", "name": "litegraph", "kind": "mcp", "official": null, "desc": "LiteGraph is a multi-modal AI data platform - a property graph with relational, vector, and MCP support, to power knowledge and AI persistence and retrieval", "repo": "https://github.com/litegraphdb/litegraph", "install": [], "stars": 133, "pushed": null, "license": null, "plat": "litegraph", "live": "Not checked (runs locally)"}, {"id": "skill:dkyazzentwatwa/chatgpt-skills/crypto-ta-analyzer", "name": "crypto-ta-analyzer", "kind": "skill", "official": false, "desc": "Run multi-indicator technical analysis on crypto or market OHLCV data. Use for deterministic trend, momentum, volume, and divergence analysis.", "repo": "https://github.com/dkyazzentwatwa/chatgpt-skills/tree/HEAD/crypto-ta-analyzer", "install": ["npx skills add dkyazzentwatwa/chatgpt-skills --skill crypto-ta-analyzer"], "stars": 132, "pushed": "2026-04-08T00:45:38Z", "license": null, "plat": "crypto data", "live": "Not checked (runs locally)"}, {"id": "skill:dkyazzentwatwa/chatgpt-skills/geo-toolkit", "name": "geo-toolkit", "kind": "skill", "official": false, "desc": "Parse, convert, geocode, visualize, and measure geographic data. Use for address cleanup, geo file conversion, mapping, and distance workflows.", "repo": "https://github.com/dkyazzentwatwa/chatgpt-skills/tree/HEAD/geo-toolkit", "install": ["npx skills add dkyazzentwatwa/chatgpt-skills --skill geo-toolkit"], "stars": 132, "pushed": "2026-04-08T00:45:38Z", "license": null, "plat": "geographic data", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.posecode-dev/posecode-mcp", "name": "io.github.posecode-dev/posecode-mcp", "kind": "mcp", "official": null, "desc": "Author, ROM-validate, and render 3D human movement (.posecode) with a shareable playground link.", "repo": "https://github.com/posecode-dev/posecode", "install": ["npm:posecode-mcp"], "stars": 131, "pushed": "2026-10-26T06:18:57Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:infranodus/skills/claude-directory/infranodus", "name": "infranodus", "kind": "skill", "official": false, "desc": "Text network analysis and knowledge graphs via the InfraNodus MCP server: text structure, content gaps, research questions and ideas, text comparison, SEO/GEO and Google search analysis, GraphRAG retrieval, structured memory. Accepts text, URLs (incl. YouTube), and saved graphs. Analyzes and optimizes an LLM's memories (Claude or Codex memory, mem0, claude-mem): optimizes their structure with optimize_knowledge_base and finds gaps to bridge them and enhance the memories. Also graphs a project fo", "repo": "https://github.com/infranodus/skills/tree/HEAD/claude-directory/infranodus", "install": ["npx skills add infranodus/skills --skill infranodus"], "stars": 127, "pushed": "2026-11-12T21:39:13Z", "license": "NOASSERTION", "plat": "infranodus", "live": "Not checked (runs locally)"}, {"id": "skill:infranodus/skills/skill-ontology-creator", "name": "ontology-generator", "kind": "skill", "official": false, "desc": "Generate comprehensive ontological knowledge graphs in [[wikilinks]] syntax for InfraNodus visualization. Use when the user requests to create an ontology, extract entities and relationships from text, or generate knowledge graph structures. Handles both topic-based ontology generation and entity extraction from existing text. Output is formatted for direct paste into InfraNodus.com for network visualization and AI-powered gap analysis.", "repo": "https://github.com/infranodus/skills/tree/HEAD/skill-ontology-creator", "install": ["npx skills add infranodus/skills --skill ontology-generator"], "stars": 127, "pushed": "2026-11-12T21:39:13Z", "license": "NOASSERTION", "plat": "infranodus", "live": "Not checked (runs locally)"}, {"id": "skill:neo4j-contrib/neo4j-skills/neo4j-aura-graph-analytics-skill", "name": "neo4j-aura-graph-analytics-skill", "kind": "skill", "official": false, "desc": "Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions, AuraGraphDataScience, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection with gds.graph.project.cypher and gds.graph.project.remote, gds.graph.project.native, gds.graph.construct, graphdatascience client 2.0 session endpoints, async compute and gds.jobs, AuraDB Cypher API memory/sessionId projection, algorithms, write-back, and session lifecycle. Use for AuraDB-connected, self-mana", "repo": "https://github.com/neo4j-contrib/neo4j-skills/tree/HEAD/neo4j-aura-graph-analytics-skill", "install": ["npx skills add neo4j-contrib/neo4j-skills --skill neo4j-aura-graph-analytics-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-cypher-skill", "name": "neo4j-cypher-skill", "kind": "skill", "official": false, "desc": "Generates, optimizes, and validates Cypher 25 queries for Neo4j 2025.x and 2026.x. Use when writing new Cypher queries, optimizing slow queries, graph pattern matching, vector or fulltext search, subqueries, or batch writes. Covers MATCH, MERGE, CREATE, WITH, RETURN, CALL, UNWIND, FOREACH, LOAD CSV, SEARCH, expressions, functions, indexes, and subqueries. Does NOT handle driver migration or API changes — use neo4j-migration-skill. Does NOT cover DB administration or server ops — use neo4j-cli-to", "repo": "https://github.com/neo4j-contrib/neo4j-skills/tree/HEAD/neo4j-cypher-skill", "install": ["npx skills add neo4j-contrib/neo4j-skills --skill neo4j-cypher-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-vector-index-skill", "name": "neo4j-vector-index-skill", "kind": "skill", "official": false, "desc": "Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipel", "repo": "https://github.com/neo4j-contrib/neo4j-skills/tree/HEAD/neo4j-vector-index-skill", "install": ["npx skills add neo4j-contrib/neo4j-skills --skill neo4j-vector-index-skill"], "stars": 122, "pushed": "2026-11-09T08:06:18Z", "license": "MIT", "plat": "neo4j", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Ljunqiang/rustchain-mcp", "name": "RustChain MCP", "kind": "mcp", "official": null, "desc": "MCP server for RustChain blockchain, BoTTube video, and Beacon agent messaging", "repo": "https://github.com/Scottcjn/rustchain-mcp", "install": ["mcpb:https://github.com/Ljunqiang/rustchain-mcp/releases/download/v0.4.0-mcpb/rustchain-mcp.mcpb"], "stars": 122, "pushed": "2026-10-04T21:44:20Z", "license": "MIT", "plat": "rustchain", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:memgraph/ai-toolkit", "name": "ai-toolkit", "kind": "mcp", "official": null, "desc": "Open-source tools for AI on Memgraph: persistent agent memory, knowledge graphs, GraphRAG, and integrations for LangChain, LightRAG, and MCP.", "repo": "https://github.com/memgraph/ai-toolkit", "install": [], "stars": 119, "pushed": null, "license": null, "plat": "memgraph", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:lindeloev/mcp", "name": "mcp", "kind": "mcp", "official": null, "desc": "Regression with Multiple Change Points", "repo": "https://github.com/lindeloev/mcp", "install": [], "stars": 119, "pushed": null, "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.VictoriaMetrics-Community/mcp-victorialogs", "name": "io.github.VictoriaMetrics-Community/mcp-victorialogs", "kind": "mcp", "official": null, "desc": "MCP Server for VictoriaLogs. Provides integration with VictoriaLogs API and documentation", "repo": "https://github.com/VictoriaMetrics-Community/mcp-victorialogs", "install": ["oci:ghcr.io/victoriametrics-community/mcp-victorialogs:1.8.0"], "stars": 111, "pushed": "2026-11-12T11:34:39Z", "license": "Apache-2.0", "plat": "victorialogs", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.VictoriaMetrics/mcp-victorialogs", "name": "io.github.VictoriaMetrics/mcp-victorialogs", "kind": "mcp", "official": null, "desc": "MCP Server for VictoriaLogs. Provides integration with VictoriaLogs API and documentation", "repo": "https://github.com/VictoriaMetrics/mcp-victorialogs", "install": ["oci:ghcr.io/victoriametrics/mcp-victorialogs:1.9.0"], "stars": 110, "pushed": "2026-11-12T11:34:39Z", "license": "Apache-2.0", "plat": "victorialogs", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:Sangwxx/ArcGIS-Pro-Bridge-MCP-Server", "name": "ArcGIS-Pro-Bridge-MCP-Server", "kind": "mcp", "official": null, "desc": "ArcGIS Pro Bridge MCP Server 是一个让 AI 客户端连接本机 ArcGIS Pro 的 MCP Server。", "repo": "https://github.com/Sangwxx/ArcGIS-Pro-Bridge-MCP-Server", "install": [], "stars": 106, "pushed": null, "license": null, "plat": "arcgis pro", "live": "Not checked (runs locally)"}, {"id": "mcp:ai.smithery/infranodus-mcp-server-infranodus", "name": "ai.smithery/infranodus-mcp-server-infranodus", "kind": "mcp", "official": null, "desc": "Map text into knowledge graphs to create a structured representation of conceptual relations and t…", "repo": "https://github.com/infranodus/mcp-server-infranodus", "install": ["https://server.smithery.ai/@infranodus/mcp-server-infranodus/mcp"], "stars": 105, "pushed": "2026-11-11T13:22:53Z", "license": "MIT", "plat": "knowledge graphs", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.neo4j-contrib/gds-agent", "name": "Neo4j GDS Agent", "kind": "mcp", "official": null, "desc": "Neo4j Graph Data Science MCP server", "repo": "https://github.com/neo4j-contrib/gds-agent", "install": ["pypi:gds-agent"], "stars": 99, "pushed": "2026-11-11T10:39:09Z", "license": "MIT", "plat": "neo4j", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:pinecone", "name": "pinecone", "kind": "claude-plugin", "official": null, "desc": "Pinecone vector database integration. Streamline your Pinecone development with powerful tools for managing vector indexes, querying data, and rapid prototyping. Use slash commands like /quickstart to generate AGENTS.md files and initialize Python projects and /query to quickly explore indexes. Access the Pinecone MCP server for creating, describing, upserting and querying indexes with Claude. Perfect for developers building semantic search, RAG applications, recommendation systems, and other vector-based applications with Pinecone.", "repo": "https://github.com/pinecone-io/pinecone-claude-code-plugin.git", "install": ["claude plugin install pinecone@claude-plugins-official"], "stars": 68, "pushed": "2026-11-12T22:33:56Z", "license": "MIT", "plat": "pinecone", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.msdanyg/smart-connections-mcp", "name": "io.github.msdanyg/smart-connections-mcp", "kind": "mcp", "official": null, "desc": "Local semantic search over Obsidian vaults via Smart Connections embeddings. Multi-vault, private.", "repo": "https://github.com/msdanyg/smart-connections-mcp", "install": ["npm:smart-connections-mcp"], "stars": 59, "pushed": "2026-08-22T03:15:36Z", "license": "MIT", "plat": "obsidian", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:fiftyone", "name": "fiftyone", "kind": "claude-plugin", "official": null, "desc": "Build high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.", "repo": "https://github.com/voxel51/fiftyone-skills.git", "install": ["claude plugin install fiftyone@claude-plugins-official"], "stars": 48, "pushed": "2026-10-29T23:06:45Z", "license": "Apache-2.0", "plat": "fiftyone", "live": "Not checked (runs locally)"}, {"id": "mcp:ai.smithery/blockscout-mcp-server", "name": "ai.smithery/blockscout-mcp-server", "kind": "mcp", "official": null, "desc": "Provide AI agents and automation tools with contextual access to blockchain data including balance…", "repo": "https://github.com/blockscout/mcp-server", "install": ["https://server.smithery.ai/@blockscout/mcp-server/mcp"], "stars": 46, "pushed": "2026-09-08T03:19:30Z", "license": "NOASSERTION", "plat": "blockchain", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.nonatofabio/local-faiss-mcp", "name": "Local FAISS MCP Server", "kind": "mcp", "official": null, "desc": "Local FAISS vector database for RAG with document ingestion, semantic search, and MCP prompts.", "repo": "https://github.com/nonatofabio/local_faiss_mcp", "install": ["pypi:local-faiss-mcp"], "stars": 36, "pushed": "2026-04-24T21:21:39Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:com.mcparmory/alchemy-nft", "name": "com.mcparmory/alchemy-nft", "kind": "mcp", "official": null, "desc": "Retrieve NFT metadata, ownership, and contract details across blockchains", "repo": "https://github.com/mcparmory/registry", "install": ["pypi:mcparmory-alchemy-nft", "oci:ghcr.io/mcparmory/alchemy-nft:1.0.3"], "stars": 32, "pushed": "2026-05-12T12:55:57Z", "license": "NOASSERTION", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:com.mcparmory/pinecone", "name": "com.mcparmory/pinecone", "kind": "mcp", "official": null, "desc": "Create and manage vector database indexes, backups, and collections", "repo": 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