[{"id": "skill:nexu-io/open-design/plugins/_official/examples/hps-y2k-chrome", "name": "hps-y2k-chrome", "kind": "skill", "official": false, "desc": "OpenDesign's KPI decision brief: activation and retention drivers, what's working, and the one metric to move next. Built as a decision-grade data & finance deck for CRO, RevOps, leadership.", "repo": "https://github.com/nexu-io/open-design/tree/HEAD/plugins/_official/examples/hps-y2k-chrome", "install": ["npx skills add nexu-io/open-design --skill hps-y2k-chrome"], "stars": 107550, "pushed": "2026-11-14T01:09:21Z", "license": "Apache-2.0", "plat": "opendesign", "live": "Not checked (runs locally)"}, {"id": "skill:nexu-io/open-design/design-templates/html-ppt-weekly-report", "name": "html-ppt-weekly-report", "kind": "skill", "official": false, "desc": "OpenDesign's weekly growth review: stars, installs, activation — the number, the driver, and the recommended move. Built as a decision-grade data & finance deck for growth & product team.", "repo": "https://github.com/nexu-io/open-design/tree/HEAD/design-templates/html-ppt-weekly-report", "install": ["npx skills add nexu-io/open-design --skill html-ppt-weekly-report"], "stars": 107550, "pushed": "2026-11-14T01:09:21Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:ComposioHQ/awesome-claude-skills/composio-skills/big-data-cloud-automation", "name": "big-data-cloud-automation", "kind": "skill", "official": false, "desc": "Automate Big Data Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.", "repo": "https://github.com/ComposioHQ/awesome-claude-skills/tree/HEAD/composio-skills/big-data-cloud-automation", "install": ["npx skills add ComposioHQ/awesome-claude-skills --skill big-data-cloud-automation"], "stars": 79451, "pushed": "2026-09-18T07:16:11Z", "license": null, "plat": "big data cloud", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/dask", "name": "dask", "kind": "skill", "official": false, "desc": "Scales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/dask", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill dask"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/depmap", "name": "depmap", "kind": "skill", "official": false, "desc": "Retrieves and analyzes Cancer Dependency Map (DepMap) release data, including CRISPR Chronos gene effects, cancer model annotations, omics biomarkers, and PRISM drug sensitivity. Supports cancer-selective dependency, co-essentiality, and candidate synthetic-lethality analyses with release-aware identifiers and statistical checks.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/depmap", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill depmap"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "depmap", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/gget", "name": "gget", "kind": "skill", "official": false, "desc": "Queries 20+ bioinformatics resources through CLI/Python. Supports quick lookups of gene info, BLAST/BLAT, viral sequence downloads, PDB/mmCIF structures, G2P residue annotations, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/gget", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill gget"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "gget", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/imaging-data-commons", "name": "imaging-data-commons", "kind": "skill", "official": false, "desc": "Queries and downloads public cancer imaging data from NCI Imaging Data Commons. Supports IDC collection discovery, DICOM access, radiology (CT, MR, PET) and pathology AI datasets, metadata SQL, visualization, licensing, and citations. Uses public metadata and download routes without authentication; optional BigQuery and Google Healthcare routes require Google credentials.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/imaging-data-commons", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill imaging-data-commons"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "imaging data commons", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.metabase/mcp", "name": "Metabase", "kind": "mcp", "official": null, "desc": "Lets AI clients search, explore, query, and visualize data in a Metabase instance.", "repo": "https://github.com/metabase/metabase", "install": ["https://{metabase_host}/api/mcp"], "stars": 50311, "pushed": "2026-11-13T14:01:13Z", "license": "NOASSERTION", "plat": "metabase", "live": "Down · URLError: <urlopen error [Errno 11001] getaddrinfo failed> (checked 2026-11-13)"}, {"id": "skill:sickn33/agentic-awesome-skills/plugins/aasb-aas-data-analytics/skills/analytics-tracking", "name": "analytics-tracking", "kind": "skill", "official": false, "desc": "Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-analytics/skills/analytics-tracking", "install": ["npx skills add sickn33/agentic-awesome-skills --skill analytics-tracking"], "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-analytics/skills/business-analyst", "name": "business-analyst", "kind": "skill", "official": false, "desc": "Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-analytics/skills/business-analyst", "install": ["npx skills add sickn33/agentic-awesome-skills --skill business-analyst"], "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/data-engineer", "name": "data-engineer", "kind": "skill", "official": false, "desc": "Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-engineering-platform/skills/data-engineer", "install": ["npx skills add sickn33/agentic-awesome-skills --skill data-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-data-analytics/skills/data-storytelling", "name": "data-storytelling", "kind": "skill", "official": false, "desc": "Transform raw data into compelling narratives that drive decisions and inspire action.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-analytics/skills/data-storytelling", "install": ["npx skills add sickn33/agentic-awesome-skills --skill data-storytelling"], "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-analytics/skills/sql-pro", "name": "sql-pro", "kind": "skill", "official": false, "desc": "Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-analytics/skills/sql-pro", "install": ["npx skills add sickn33/agentic-awesome-skills --skill sql-pro"], "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-analytics/skills/startup-financial-modeling", "name": "startup-financial-modeling", "kind": "skill", "official": false, "desc": "Build comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-analytics/skills/startup-financial-modeling", "install": ["npx skills add sickn33/agentic-awesome-skills --skill startup-financial-modeling"], "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-analytics/skills/startup-metrics-framework", "name": "startup-metrics-framework", "kind": "skill", "official": false, "desc": "Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-analytics/skills/startup-metrics-framework", "install": ["npx skills add sickn33/agentic-awesome-skills --skill startup-metrics-framework"], "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/warehouse", "name": "warehouse", "kind": "skill", "official": false, "desc": "Plan and review read-only data warehouse analysis with explicit scope, privacy, provenance, and validation checks.", "repo": "https://github.com/sickn33/agentic-awesome-skills/tree/HEAD/plugins/aasb-aas-data-engineering-platform/skills/warehouse", "install": ["npx skills add sickn33/agentic-awesome-skills --skill warehouse"], "stars": 49672, "pushed": "2026-11-13T22:02:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/business-analytics/skills/kpi-dashboard-design", "name": "kpi-dashboard-design", "kind": "skill", "official": false, "desc": "Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/business-analytics/skills/kpi-dashboard-design", "install": ["npx skills add wshobson/agents --skill kpi-dashboard-design"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "kpi dashboard", "live": "Not checked (runs locally)"}, {"id": "skill:wshobson/agents/plugins/data-engineering/skills/spark-optimization", "name": "spark-optimization", "kind": "skill", "official": false, "desc": "Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.", "repo": "https://github.com/wshobson/agents/tree/HEAD/plugins/data-engineering/skills/spark-optimization", "install": ["npx skills add wshobson/agents --skill spark-optimization"], "stars": 42495, "pushed": "2026-11-09T01:08:33Z", "license": "MIT", "plat": "apache spark", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/bigquery-pipeline-audit", "name": "bigquery-pipeline-audit", "kind": "skill", "official": false, "desc": "Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/bigquery-pipeline-audit", "install": ["npx skills add github/awesome-copilot --skill bigquery-pipeline-audit"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "bigquery", "live": "Not checked (runs locally)"}, {"id": "skill:github/awesome-copilot/skills/fabric-lakehouse", "name": "fabric-lakehouse", "kind": "skill", "official": false, "desc": "Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.", "repo": "https://github.com/github/awesome-copilot/tree/HEAD/skills/fabric-lakehouse", "install": ["npx skills add github/awesome-copilot --skill fabric-lakehouse"], "stars": 41140, "pushed": "2026-11-13T21:37:52Z", "license": "MIT", "plat": "microsoft fabric", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.PostHog/mcp", "name": "PostHog MCP Server", "kind": "mcp", "official": null, "desc": "Official PostHog MCP Server for product analytics, feature flags, experiments, and more.", "repo": "https://github.com/PostHog/posthog", "install": ["https://mcp.posthog.com/mcp", "https://mcp.posthog.com/sse"], "stars": 40768, "pushed": "2026-11-13T08:00:28Z", "license": "NOASSERTION", "plat": "posthog", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "skill:davila7/claude-code-templates/cli-tool/components/skills/database/bigquery-basics", "name": "bigquery-basics", "kind": "skill", "official": false, "desc": "Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queries, resource management, data ingestion, or AI applications on BigQuery.", "repo": "https://github.com/davila7/claude-code-templates/tree/HEAD/cli-tool/components/skills/database/bigquery-basics", "install": ["npx skills add davila7/claude-code-templates --skill bigquery-basics"], "stars": 33495, "pushed": "2026-11-14T03:16:32Z", "license": "MIT", "plat": "bigquery", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/c-level-advisor/chief-data-officer-advisor/skills/chief-data-officer-advisor", "name": "chief-data-officer-advisor", "kind": "skill", "official": false, "desc": "Use for data strategy decisions: training models on customer data, warehouse vs lakehouse vs mesh, valuing data for a raise or M&A, or data hiring order. Runs ai_training_data_audit.py, data_product_strategy_picker.py and data_asset_valuator.py.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/c-level-advisor/chief-data-officer-advisor/skills/chief-data-officer-advisor", "install": ["npx skills add alirezarezvani/claude-skills --skill chief-data-officer-advisor"], "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-data-engineer", "name": "senior-data-engineer", "kind": "skill", "official": false, "desc": "Data pipelines: Airflow DAGs, Spark, dbt, Kafka, ETL/ELT, data quality checks. Load whenever a DAG or batch job times out or fails, data arrives wrong, or the user designs an ingestion pipeline.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering-team/skills/senior-data-engineer", "install": ["npx skills add alirezarezvani/claude-skills --skill senior-data-engineer"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "airflow, spark, dbt, kafka", "live": "Not checked (runs locally)"}, {"id": "skill:alirezarezvani/claude-skills/engineering-team/snowflake-development/skills/snowflake-development", "name": "snowflake-development", "kind": "skill", "official": false, "desc": "Use for Snowflake work: SQL, Dynamic Tables, Streams and Tasks, Cortex AI functions and agents, Snowpark Python, dbt on Snowflake, or Snowflake errors. Helps with queries via snowflake_query_helper.py.", "repo": "https://github.com/alirezarezvani/claude-skills/tree/HEAD/engineering-team/snowflake-development/skills/snowflake-development", "install": ["npx skills add alirezarezvani/claude-skills --skill snowflake-development"], "stars": 29773, "pushed": "2026-11-12T10:35:06Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:google/skills/skills/cloud/bigquery-basics", "name": "bigquery-basics", "kind": "skill", "official": false, "desc": "Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/bigquery-basics", "install": ["npx skills add google/skills --skill bigquery-basics"], "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/bigquery-bigframes", "name": "bigquery-bigframes", "kind": "skill", "official": false, "desc": "Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/bigquery-bigframes", "install": ["npx skills add google/skills --skill bigquery-bigframes"], "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/bigquery-migration-data-loader", "name": "bigquery-migration-data-loader", "kind": "skill", "official": false, "desc": "Provisions BigQuery datasets, ingests staged Parquet files directly into native BigQuery tables by using bq load, and deploys already-translated views from local or Cloud Storage BigQuery Migration Service (BQMS) translation outputs. Use when provisioning target BigQuery datasets for warehouse migrations, batch loading staged Parquet files into native BigQuery tables by using bq load, auditing loaded table row counts, or deploying already-translated BigQuery views into a target dataset. Don't us", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/bigquery-migration-data-loader", "install": ["npx skills add google/skills --skill bigquery-migration-data-loader"], "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/bigquery-migration-metadata-extractor", "name": "bigquery-migration-metadata-extractor", "kind": "skill", "official": false, "desc": "Extracts database catalog metadata and query logs from data warehouses by running the DWH migration extractor locally or on a temporary Google Cloud VM through the BigQuery Migration Cloud Extractor (credentials held in Secret Manager), and inspects extracted `.zip` archives to report object and volume statistics. Use when extracting database metadata or query logs from Snowflake, Redshift, Teradata, Oracle, PostgreSQL, SQL Server, Hive, etc., deciding between local and cloud (Cloud Extractor) e", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/bigquery-migration-metadata-extractor", "install": ["npx skills add google/skills --skill bigquery-migration-metadata-extractor"], "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/bigquery-optimization", "name": "bigquery-optimization", "kind": "skill", "official": false, "desc": "Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/bigquery-optimization", "install": ["npx skills add google/skills --skill bigquery-optimization"], "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/datalineage-bigquery-asset-impact-analysis", "name": "datalineage-bigquery-asset-impact-analysis", "kind": "skill", "official": false, "desc": "Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don'", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/datalineage-bigquery-asset-impact-analysis", "install": ["npx skills add google/skills --skill datalineage-bigquery-asset-impact-analysis"], "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/datalineage-summary", "name": "datalineage-summary", "kind": "skill", "official": false, "desc": "Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud Storage. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/datalineage-summary", "install": ["npx skills add google/skills --skill datalineage-summary"], "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-agentic-ai-borderless-data-lakehouse", "name": "google-cloud-solution-agentic-ai-borderless-data-lakehouse", "kind": "skill", "official": false, "desc": "Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-d", "repo": "https://github.com/google/skills/tree/HEAD/skills/cloud/google-cloud-solution-agentic-ai-borderless-data-lakehouse", "install": ["npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse"], "stars": 24146, "pushed": "2026-11-13T17:51:35Z", "license": "Apache-2.0", "plat": "google cloud", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.open-metadata/openmetadata-mcp", "name": "OpenMetadata", "kind": "mcp", "official": null, "desc": "Official OpenMetadata MCP: 21 read and write tools for search, lineage, data quality, governance.", "repo": "https://github.com/open-metadata/OpenMetadata", "install": ["https://{openmetadata_host}/mcp"], "stars": 15762, "pushed": "2026-11-13T08:58:34Z", "license": "Apache-2.0", "plat": "openmetadata", "live": "Down · URLError: <urlopen error [Errno 11001] getaddrinfo failed> (checked 2026-11-13)"}, {"id": "skill:Jeffallan/claude-skills/skills/ml-pipeline", "name": "ml-pipeline", "kind": "skill", "official": false, "desc": "Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data vers", "repo": "https://github.com/Jeffallan/claude-skills/tree/HEAD/skills/ml-pipeline", "install": ["npx skills add Jeffallan/claude-skills --skill ml-pipeline"], "stars": 12355, "pushed": "2026-11-11T08:34:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:Jeffallan/claude-skills/skills/spark-engineer", "name": "spark-engineer", "kind": "skill", "official": false, "desc": "Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.", "repo": "https://github.com/Jeffallan/claude-skills/tree/HEAD/skills/spark-engineer", "install": ["npx skills add Jeffallan/claude-skills --skill spark-engineer"], "stars": 12355, "pushed": "2026-11-11T08:34:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.BuilderIO/agent-native-analytics", "name": "Agent-Native Analytics", "kind": "mcp", "official": null, "desc": "Agent-Native Amplitude/Mixpanel - connect data sources, prompt for charts", "repo": "https://github.com/BuilderIO/agent-native", "install": ["https://analytics.agent-native.com/mcp"], "stars": 7691, "pushed": "2026-11-13T03:22:22Z", "license": "MIT", "plat": "amplitude/mixpanel", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:com.jitsu/mcp", "name": "Jitsu", "kind": "mcp", "official": null, "desc": "Manage Jitsu data pipelines: destinations, streams, connections, functions, live events.", "repo": "https://github.com/jitsucom/jitsu", "install": ["https://use.jitsu.com/mcp"], "stars": 5135, "pushed": "2026-11-12T12:22:14Z", "license": "MIT", "plat": "jitsu", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "skill:charlie947/social-media-skills/skills/analytics-dashboard", "name": "analytics-dashboard", "kind": "skill", "official": false, "desc": "Turn a LinkedIn Analytics export into an interactive dark-themed React dashboard plus a written strategic analysis with 5 data-backed content recommendations. Reads every sheet in the export, builds charts for engagement trend, follower growth, post performance scatter, day-of-week heatmap, and audience breakdown. Use this skill whenever the user says \"analyse my linkedin\", \"linkedin analytics\", \"build my dashboard\", \"review my performance\", or uploads a LinkedIn Analytics export file. Requires ", "repo": "https://github.com/charlie947/social-media-skills/tree/HEAD/skills/analytics-dashboard", "install": ["npx skills add charlie947/social-media-skills --skill analytics-dashboard"], "stars": 4698, "pushed": "2026-10-18T20:20:39Z", "license": "MIT", "plat": "linkedin analytics", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-genomics-workflow-acceleration", "name": "genomics-workflow-acceleration", "kind": "skill", "official": false, "desc": "Use when accelerating existing genomics workflows with NVIDIA Parabricks, improving runtime or price/performance, converting pipeline steps to GPUs, or comparing CPU and GPU workflow outputs. Adds optional GPU steps in-place with runtime toggles (default off). Do NOT use for individual pbrun command routing — use parabricks.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-genomics-workflow-acceleration", "install": ["npx skills add NVIDIA/skills --skill genomics-workflow-acceleration"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia parabricks", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/dask", "name": "dask", "kind": "skill", "official": false, "desc": "Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/dask", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill dask"], "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/zarr-python", "name": "zarr-python", "kind": "skill", "official": false, "desc": "Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/zarr-python", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill zarr-python"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "s3/gcs", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:aws-data-analytics", "name": "aws-data-analytics", "kind": "claude-plugin", "official": null, "desc": "Data lake, analytics, and ETL workflows with S3 Tables, AWS Glue, and Athena.", "repo": "https://github.com/aws/agent-toolkit-for-aws.git", "install": ["claude plugin install aws-data-analytics@claude-plugins-official"], "stars": 3480, "pushed": "2026-11-13T08:43:24Z", "license": "Apache-2.0", "plat": "aws", "live": "Not checked (runs locally)"}, {"id": "skill:ciembor/agent-rules-books/designing-data-intensive-applications", "name": "designing-data-intensive-applications", "kind": "skill", "official": false, "desc": "Apply Martin Kleppmann-inspired data-system rules when designing reliability, scalability, consistency, replication, transactions, streams, or schema evolution.", "repo": "https://github.com/ciembor/agent-rules-books/tree/HEAD/designing-data-intensive-applications", "install": ["npx skills add ciembor/agent-rules-books --skill designing-data-intensive-applications"], "stars": 3265, "pushed": "2026-09-10T11:21:47Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/flux-pipeline", "name": "flux-pipeline", "kind": "skill", "official": false, "desc": "Build a data pipeline — ETL/ELT with extraction, transformation, loading, error handling, and scheduling. Use when asked to \"build ETL\", \"data pipeline\", \"move data from X to Y\", or \"sync data\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/flux-pipeline", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill flux-pipeline"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "data pipeline", "live": "Not checked (runs locally)"}, {"id": "skill:jeremylongshore/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/lens-audit", "name": "lens-audit", "kind": "skill", "official": false, "desc": "Review existing analytics — find all dashboards and reports, check who uses them, whether metrics are defined, and whether they drive decisions. Recommend what to keep, kill, or add. Use when asked \"are our dashboards useful\", \"analytics review\", or \"metrics audit\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/lens-audit", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill lens-audit"], "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/lens-chart", "name": "lens-chart", "kind": "skill", "official": false, "desc": "Use when asked to select chart types for analytics dashboards, choose BI visualizations, or design data displays. Examples: \"best chart for sales data\", \"dashboard visualization for metrics\", \"analytics chart selection\"", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/lens-chart", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill lens-chart"], "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/lens-dashboard", "name": "lens-dashboard", "kind": "skill", "official": false, "desc": "Design and spec an analytical dashboard — define the question each chart answers, write the SQL queries, spec the layout and refresh cadence. Produces a complete dashboard spec ready to implement. Use when asked to \"build a dashboard\", \"analytics dashboard\", \"BI dashboard\", \"weekly product health\", or \"visualize this data\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/lens-dashboard", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill lens-dashboard"], "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/lens-metrics", "name": "lens-metrics", "kind": "skill", "official": false, "desc": "Produce a complete metrics definition doc — metric name, formula, data source, segmentation, SQL or event tracking spec, and what good/bad looks like. Given a product area, outputs the full metrics spec. Use when asked to \"define KPIs\", \"metrics framework\", \"what should we measure\", \"north star metric\", or \"instrument this feature\".", "repo": "https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/HEAD/plugins/ai-agency/tonone/skills/lens-metrics", "install": ["npx skills add jeremylongshore/tons-of-skills-marketplace --skill lens-metrics"], "stars": 2919, "pushed": "2026-11-14T05:32:50Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:rohitg00/awesome-claude-code-toolkit/skills/data-engineering", "name": "data-engineering", "kind": "skill", "official": false, "desc": "Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation", "repo": "https://github.com/rohitg00/awesome-claude-code-toolkit/tree/HEAD/skills/data-engineering", "install": ["npx skills add rohitg00/awesome-claude-code-toolkit --skill data-engineering"], "stars": 2838, "pushed": "2026-05-12T07:18:34Z", "license": "Apache-2.0", "plat": "apache spark", "live": "Not checked (runs locally)"}, {"id": "skill:rmyndharis/antigravity-skills/skills/data-engineer", "name": "data-engineer", "kind": "skill", "official": false, "desc": "Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use for data pipeline design, analytics infrastructure, or modern data stack implementation.", "repo": "https://github.com/rmyndharis/antigravity-skills/tree/HEAD/skills/data-engineer", "install": ["npx skills add rmyndharis/antigravity-skills --skill data-engineer"], "stars": 2009, "pushed": "2026-11-07T15:24:20Z", "license": "MIT", "plat": "apache spark", "live": "Not checked (runs locally)"}, {"id": "skill:rmyndharis/antigravity-skills/skills/kpi-dashboard-design", "name": "kpi-dashboard-design", "kind": "skill", "official": false, "desc": "Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.", "repo": "https://github.com/rmyndharis/antigravity-skills/tree/HEAD/skills/kpi-dashboard-design", "install": ["npx skills add rmyndharis/antigravity-skills --skill kpi-dashboard-design"], "stars": 2009, "pushed": "2026-11-07T15:24:20Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:rmyndharis/antigravity-skills/skills/machine-learning-ops-ml-pipeline", "name": "machine-learning-ops-ml-pipeline", "kind": "skill", "official": false, "desc": "Design ML pipelines covering data preparation, model training, deployment, monitoring, and continuous improvement. Use when coordinating an end-to-end MLOps workflow.", "repo": "https://github.com/rmyndharis/antigravity-skills/tree/HEAD/skills/machine-learning-ops-ml-pipeline", "install": ["npx skills add rmyndharis/antigravity-skills --skill machine-learning-ops-ml-pipeline"], "stars": 2009, "pushed": "2026-11-07T15:24:20Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:slothflowlabs/duckle", "name": "duckle", "kind": "mcp", "official": null, "desc": "Open-source ETL/ELT you deploy on your own servers or cloud. Built on DuckDB: no-code/low-code visual pipelines or SQL, 426 components, dbt, CDC, data quality, reverse ETL, lineage, MCP for AI agents. No vendor cloud, no per-row billing.", "repo": "https://github.com/slothflowlabs/duckle", "install": [], "stars": 1571, "pushed": null, "license": null, "plat": "duckdb", "live": "Not checked (runs locally)"}, {"id": "skill:emotixco/claude-skills-founder/skills/metrics-dashboard", "name": "metrics-dashboard", "kind": "skill", "official": false, "desc": "Pick the 5 metrics that matter at a startup's current stage, with definitions, targets, actions when below target, a minimal tracking setup, and a weekly review template. Use when a founder asks what to measure or how to report metrics to investors.", "repo": "https://github.com/emotixco/claude-skills-founder/tree/HEAD/skills/metrics-dashboard", "install": ["npx skills add emotixco/claude-skills-founder --skill metrics-dashboard"], "stars": 840, "pushed": "2026-11-03T07:46:57Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:warpdotdev/oz-skills/.agents/skills/analysis-artifacts", "name": "analysis-artifacts", "kind": "skill", "official": false, "desc": "Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis. Use when asked to conduct a deep dive, exploratory analysis, or investigation that goes beyond a simple data lookup.", "repo": "https://github.com/warpdotdev/oz-skills/tree/HEAD/.agents/skills/analysis-artifacts", "install": ["npx skills add warpdotdev/oz-skills --skill analysis-artifacts"], "stars": 825, "pushed": "2026-08-15T23:08:46Z", "license": "MIT", "plat": "bigquery", "live": "Not checked (runs locally)"}, {"id": "skill:warpdotdev/oz-skills/.agents/skills/dbt-model-index", "name": "dbt-model-index", "kind": "skill", "official": false, "desc": "Provide a lookup index of dbt models (BigQuery tables) to guide query writing against a data warehouse. Use when you need to query, analyze, or look up data in a dbt-powered data warehouse, or when resolving a vague data question into the right BigQuery tables to query.", "repo": "https://github.com/warpdotdev/oz-skills/tree/HEAD/.agents/skills/dbt-model-index", "install": ["npx skills add warpdotdev/oz-skills --skill dbt-model-index"], "stars": 825, "pushed": "2026-08-15T23:08:46Z", "license": "MIT", "plat": "bigquery", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.marmotdata/marmot", "name": "Marmot Data Catalog", "kind": "mcp", "official": null, "desc": "Open-source data catalog. Search assets, explore lineage, and find ownership.", "repo": "https://github.com/marmotdata/marmot", "install": ["https://{marmot_host}/api/v1/mcp"], "stars": 636, "pushed": "2026-11-13T09:54:56Z", "license": "MIT", "plat": "marmot", "live": "Down · URLError: <urlopen error [Errno 11001] getaddrinfo failed> (checked 2026-11-13)"}, {"id": "mcp:gh:dbt-labs/dbt-mcp", "name": "dbt-mcp", "kind": "mcp", "official": null, "desc": "A MCP (Model Context Protocol) server for interacting with dbt.", "repo": "https://github.com/dbt-labs/dbt-mcp", "install": [], "stars": 622, "pushed": null, "license": null, "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:astronomer-data-agents", "name": "astronomer-data-agents", "kind": "claude-plugin", "official": null, "desc": "Data engineering for Apache Airflow and Astronomer. Author DAGs with best practices, debug pipeline failures, trace data lineage, profile tables, migrate Airflow 2 to 3, and manage local and cloud deployments.", "repo": "https://github.com/astronomer/agents.git", "install": ["claude plugin install astronomer-data-agents@claude-plugins-official"], "stars": 461, "pushed": "2026-11-02T18:15:30Z", "license": "Apache-2.0", "plat": "astronomer", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:data", "name": "data", "kind": "claude-plugin", "official": null, "desc": "Data engineering for Apache Airflow and Astronomer. Author DAGs with best practices, debug pipeline failures, trace data lineage, profile tables, migrate Airflow 2 to 3, and manage local and cloud deployments.", "repo": "https://github.com/astronomer/agents.git", "install": ["claude plugin install data@claude-plugins-official"], "stars": 461, "pushed": "2026-11-02T18:15:30Z", "license": "Apache-2.0", "plat": "apache airflow", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:data-engineering", "name": "data-engineering", "kind": "claude-plugin", "official": null, "desc": "Data engineering plugin - warehouse exploration, pipeline authoring, Airflow integration", "repo": "https://github.com/astronomer/agents.git", "install": ["claude plugin install data-engineering@claude-plugins-official"], "stars": 461, "pushed": "2026-11-02T18:15:30Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:databricks", "name": "databricks", "kind": "claude-plugin", "official": null, "desc": "Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless migration.", "repo": "https://github.com/databricks/databricks-agent-skills.git", "install": ["claude plugin install databricks@claude-plugins-official"], "stars": 397, "pushed": "2026-11-13T06:06:24Z", "license": "NOASSERTION", "plat": "databricks", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:tableau/tableau-mcp", "name": "tableau-mcp", "kind": "mcp", "official": null, "desc": "Tableau's official MCP Server. Helping agents see and understand data.", "repo": "https://github.com/tableau/tableau-mcp", "install": [], "stars": 373, "pushed": null, "license": null, "plat": "tableau", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:apache/doris-mcp-server", "name": "doris-mcp-server", "kind": "mcp", "official": null, "desc": "Apache Doris MCP Server", "repo": "https://github.com/apache/doris-mcp-server", "install": [], "stars": 358, "pushed": null, "license": null, "plat": "apache doris", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.airbytehq/airbyte", "name": "Airbyte", "kind": "mcp", "official": null, "desc": "Build and manage data pipelines, add custom connectors, and chat with your data.", "repo": "https://github.com/airbytehq/PyAirbyte", "install": ["https://mcp.airbyte.com/mcp"], "stars": 343, "pushed": "2026-11-13T00:28:31Z", "license": "NOASSERTION", "plat": "", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:gh:fidetolabs/qanat", "name": "qanat", "kind": "mcp", "official": null, "desc": "A data platform for investment research. Ingests financial data sources and computes point-in-time financial metrics for valuation and screening. Delivered as an MCP server.", "repo": "https://github.com/fidetolabs/qanat", "install": [], "stars": 332, "pushed": null, "license": null, "plat": "qanat", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Snowflake-Labs/mcp", "name": "OSS Snowflake MCP Server", "kind": "mcp", "official": null, "desc": "MCP Server for Snowflake from Snowflake Labs", "repo": "https://github.com/Snowflake-Labs/mcp", "install": ["pypi:snowflake-labs-mcp"], "stars": 302, "pushed": "2026-05-15T16:30:51Z", "license": "Apache-2.0", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.maxanatsko/mcp-engine", "name": "SemanticOps MCP (for Power BI)", "kind": "mcp", "official": null, "desc": "Power BI MCP server (Supports Windows and macOS)", "repo": "https://github.com/maxanatsko/mcp-engine-public", "install": ["npm:mcp-engine"], "stars": 255, "pushed": "2026-10-16T15:07:29Z", "license": "NOASSERTION", "plat": "power bi", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.surendranb/google-analytics-mcp", "name": "io.github.surendranb/google-analytics-mcp", "kind": "mcp", "official": null, "desc": "GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.", "repo": "https://github.com/surendranb/google-analytics-mcp", "install": ["pypi:google-analytics-mcp"], "stars": 244, "pushed": "2026-11-08T11:13:00Z", "license": "MIT", "plat": "google analytics", "live": "Not checked (runs locally)"}, {"id": "skill:mongodb/agent-skills/plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing", "name": "mongodb-atlas-stream-processing", "kind": "skill", "official": false, "desc": "Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.", "repo": "https://github.com/mongodb/agent-skills/tree/HEAD/plugins/mongodb-atlas/skills/mongodb-atlas-stream-processing", "install": ["npx skills add mongodb/agent-skills --skill mongodb-atlas-stream-processing"], "stars": 198, "pushed": "2026-11-12T22:08:40Z", "license": "Apache-2.0", "plat": "mongodb atlas", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:StarRocks/mcp-server-starrocks", "name": "mcp-server-starrocks", "kind": "mcp", "official": null, "desc": "StarRocks MCP (Model Context Protocol) Server", "repo": "https://github.com/StarRocks/mcp-server-starrocks", "install": [], "stars": 196, "pushed": null, "license": null, "plat": "starrocks", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:aptro/superset-mcp", "name": "superset-mcp", "kind": "mcp", "official": null, "desc": "connect to 50+ data stores via superset mcp server. Can use with open ai agent sdk, Claude app, cursor, windsurf", "repo": "https://github.com/aptro/superset-mcp", "install": [], "stars": 195, "pushed": null, "license": null, "plat": "superset", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:ergut/mcp-bigquery-server", "name": "mcp-bigquery-server", "kind": "mcp", "official": null, "desc": "A Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. Enables Large Language Models (LLMs) to safely query and analyze data through a standardized interface.", "repo": "https://github.com/ergut/mcp-bigquery-server", "install": [], "stars": 148, "pushed": null, "license": null, "plat": "bigquery", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/altimate-code", "name": "altimate-code", "kind": "skill", "official": false, "desc": "Delegates dbt and warehouse work to altimate-code, a specialized CLI agent with 100+ purpose-built data tools. USE THIS SKILL FIRST whenever the task mentions or implies: warehouse access (Snowflake, BigQuery, Redshift, Databricks, Postgres, MySQL, DuckDB), column-level lineage, downstream-impact analysis, dbt builds against a real warehouse, cross-database migration or validation, query cost attribution, schema diff, data parity checking, PII detection from sampled rows, incremental/SCD2/backfi", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/altimate-code", "install": ["npx skills add AltimateAI/data-engineering-skills --skill altimate-code"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/creating-dbt-models", "name": "creating-dbt-models", "kind": "skill", "official": false, "desc": "Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions \"create\", \"build\", \"add\", \"write\", \"new\", or \"implement\" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/creating-dbt-models", "install": ["npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/dbt-model-enhancement", "name": "dbt-model-enhancement", "kind": "skill", "official": false, "desc": "Scoped enhancement of ONE existing dbt model from a ticket, with a validated PR as the deliverable. Use when: (1) User says \"update <model> per JIRA-123\", \"add column X to this model\", \"enhance <model> based on this ticket\" (2) Task is a scoped change to a specific existing model — one model, one ticket, one PR (3) User expects dbt build + self-review + PR open at the end (4) The change can be described as \"add/change column, filter, grain, or test\" — not a cross-model refactor **Prefer `creatin", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/dbt-model-enhancement", "install": ["npx skills add AltimateAI/data-engineering-skills --skill dbt-model-enhancement"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/debugging-dbt-errors", "name": "debugging-dbt-errors", "kind": "skill", "official": false, "desc": "Debugs and fixes dbt errors systematically. Use when working with dbt errors for: (1) Task mentions \"fix\", \"error\", \"broken\", \"failing\", \"debug\", \"wrong\", or \"not working\" (2) Compilation Error, Database Error, or test failures occur (3) Model produces incorrect output or unexpected results (4) Need to troubleshoot why a dbt command failed Reads full error, checks upstream first, runs dbt build (not just compile) to verify fix.", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/debugging-dbt-errors", "install": ["npx skills add AltimateAI/data-engineering-skills --skill debugging-dbt-errors"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/developing-incremental-models", "name": "developing-incremental-models", "kind": "skill", "official": false, "desc": "Develops and troubleshoots dbt incremental models. Use when working with incremental materialization for: (1) Creating new incremental models (choosing strategy, unique_key, partition) (2) Task mentions \"incremental\", \"append\", \"merge\", \"upsert\", or \"late arriving data\" (3) Troubleshooting incremental failures (merge errors, partition pruning, schema drift) (4) Optimizing incremental performance or deciding table vs incremental Guides through strategy selection, handles common incremental gotcha", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/developing-incremental-models", "install": ["npx skills add AltimateAI/data-engineering-skills --skill developing-incremental-models"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/documenting-dbt-models", "name": "documenting-dbt-models", "kind": "skill", "official": false, "desc": "Documents dbt models and columns in schema.yml. Use when working with dbt documentation for: (1) Adding model descriptions or column definitions to schema.yml (2) Task mentions \"document\", \"describe\", \"description\", \"dbt docs\", or \"schema.yml\" (3) Explaining business context, grain, meaning of data, or business rules (4) Preparing dbt docs generate or improving model discoverability Matches existing project documentation style and conventions before writing.", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/documenting-dbt-models", "install": ["npx skills add AltimateAI/data-engineering-skills --skill documenting-dbt-models"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/snowflake/end-to-end-snowflake-pipeline", "name": "end-to-end-snowflake-pipeline", "kind": "skill", "official": false, "desc": "Build an end-to-end Snowflake pipeline — ticket → dbt models + orchestration DAG → PR → self-review → ticket comment. Use when: (1) User says \"build a pipeline for X **end-to-end**\", \"create an end-to-end flow\", \"set up the <X> pipeline per JIRA-123\" (2) Scope spans dbt models AND orchestration AND a PR — not just one of them (3) Target warehouse is Snowflake and the dbt project already exists (4) An orchestrator (Airflow / Prefect / Dagster) is already in the repo, OR the user is OK operating w", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/snowflake/end-to-end-snowflake-pipeline", "install": ["npx skills add AltimateAI/data-engineering-skills --skill end-to-end-snowflake-pipeline"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/snowflake/finding-expensive-queries", "name": "finding-expensive-queries", "kind": "skill", "official": false, "desc": "Finds and ranks expensive Snowflake queries by cost, time, or data scanned. Use when: (1) User asks to find slow, expensive, or problematic queries (2) Task mentions \"query history\", \"top queries\", \"most expensive\", or \"slowest queries\" (3) Analyzing warehouse costs or identifying optimization candidates (4) Finding queries that scan the most data or have the most spillage Returns ranked list of queries with metrics and optimization recommendations.", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/snowflake/finding-expensive-queries", "install": ["npx skills add AltimateAI/data-engineering-skills --skill finding-expensive-queries"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/migrating-sql-to-dbt", "name": "migrating-sql-to-dbt", "kind": "skill", "official": false, "desc": "Converts legacy SQL to modular dbt models. Use when migrating SQL to dbt for: (1) Converting stored procedures, views, or raw SQL files to dbt models (2) Task mentions \"migrate\", \"convert\", \"legacy SQL\", \"transform to dbt\", or \"modernize\" (3) Breaking monolithic queries into modular layers (discovers project conventions first) (4) Porting existing data pipelines or ETL to dbt patterns Checks for existing models/sources, builds and validates layer by layer.", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/migrating-sql-to-dbt", "install": ["npx skills add AltimateAI/data-engineering-skills --skill migrating-sql-to-dbt"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/mssql-to-snowflake-dbt-migration", "name": "mssql-to-snowflake-dbt-migration", "kind": "skill", "official": false, "desc": "Convert MS SQL Server source code (stored procs, views, T-SQL scripts) into dbt models targeting Snowflake, with build-first-then-parity, isolated-schema deployment, and a migration dashboard. Use when: (1) User says \"migrate this T-SQL to dbt on Snowflake\", \"convert these stored procs end-to-end\", \"move <schema> from SQL Server to Snowflake\" (2) Source lives in a repo as `.sql` dumps OR behind a reachable MS SQL connection, AND source rows are available in Snowflake for parity (replicated, stag", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/mssql-to-snowflake-dbt-migration", "install": ["npx skills add AltimateAI/data-engineering-skills --skill mssql-to-snowflake-dbt-migration"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/databricks/optimizing-databricks-sql", "name": "optimizing-databricks-sql", "kind": "skill", "official": false, "desc": "Analyze DBSQL queries, including SQL embedded in notebooks (`spark.sql(...)`, `%sql` cells), for anti-patterns, lint issues, and performance problems, using Databricks-specific dialect and platform knowledge (Delta, Photon, Unity Catalog) layered on top of altimate-code's generic SQL engine. Use when a user asks to optimize, review, or lint DBSQL queries on Databricks, whether standalone or embedded in a notebook.", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/databricks/optimizing-databricks-sql", "install": ["npx skills add AltimateAI/data-engineering-skills --skill optimizing-databricks-sql"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "databricks", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/snowflake/optimizing-query-by-id", "name": "optimizing-query-by-id", "kind": "skill", "official": false, "desc": "Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions \"slow query\", \"optimize\", \"query history\", or \"query profile\" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized S", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/snowflake/optimizing-query-by-id", "install": ["npx skills add AltimateAI/data-engineering-skills --skill optimizing-query-by-id"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/snowflake/optimizing-query-text", "name": "optimizing-query-text", "kind": "skill", "official": false, "desc": "Optimizes Snowflake SQL query performance from provided query text. Use when optimizing Snowflake SQL for: (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions \"slow query\", \"make faster\", \"improve performance\", \"optimize SQL\", or \"query tuning\" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/snowflake/optimizing-query-text", "install": ["npx skills add AltimateAI/data-engineering-skills --skill optimizing-query-text"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/refactoring-dbt-models", "name": "refactoring-dbt-models", "kind": "skill", "official": false, "desc": "Safely refactors dbt models with downstream impact analysis. Use when restructuring dbt models for: (1) Task mentions \"refactor\", \"restructure\", \"extract\", \"split\", \"break into\", or \"reorganize\" (2) Extracting CTEs to intermediate models or creating macros (3) Modifying model logic that has downstream consumers (4) Renaming columns, changing types, or reorganizing model dependencies Analyzes all downstream dependencies BEFORE making changes.", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/refactoring-dbt-models", "install": ["npx skills add AltimateAI/data-engineering-skills --skill refactoring-dbt-models"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/snowflake/snowflake-dbt-model-refactoring", "name": "snowflake-dbt-model-refactoring", "kind": "skill", "official": false, "desc": "Refactor a bloated dbt model on Snowflake safely and produce a quantified before/after report that explains **which qualities improved and why** — not just raw deltas. Extends the standard refactor flow with (1) downstream impact analysis via `dbt-analyze`, (2) row-count + column-hash parity validation via `data-parity`, (3) a report that lands a Bottleneck Summary and an \"Outcome axes improved\" table (readability, maintainability, performance, cost, governance, best-practices), and (4) an optio", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/snowflake/snowflake-dbt-model-refactoring", "install": ["npx skills add AltimateAI/data-engineering-skills --skill snowflake-dbt-model-refactoring"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/snowflake/snowflake-query-optimization", "name": "snowflake-query-optimization", "kind": "skill", "official": false, "desc": "End-to-end Snowflake query optimization — fetch, rewrite, prove parity, benchmark, report. Use when: (1) User wants an **end-to-end flow** (fetch + rewrite + parity + benchmark + report), not just a rewrite (2) Input is a `.sql` file path in the repo (optionally `:L10-L80` line range), OR a Snowflake `query_id` (UUID) (3) User expects a correctness proof (row-count + per-column signature + multiplicity) before accepting the rewrite (4) Task says \"optimize this query end-to-end\", \"tune this file ", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/snowflake/snowflake-query-optimization", "install": ["npx skills add AltimateAI/data-engineering-skills --skill snowflake-query-optimization"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:AltimateAI/data-engineering-skills/skills/dbt/testing-dbt-models", "name": "testing-dbt-models", "kind": "skill", "official": false, "desc": "Adds schema tests and data quality validation to dbt models. Use when working with dbt tests for: (1) Adding or modifying tests in schema.yml files (2) Task mentions \"test\", \"validate\", \"data quality\", \"unique\", \"not_null\", or \"accepted_values\" (3) Ensuring data integrity - primary keys, foreign keys, relationships (4) Debugging test failures or understanding why dbt test failed Matches existing project test patterns and YAML style before adding new tests.", "repo": "https://github.com/AltimateAI/data-engineering-skills/tree/HEAD/skills/dbt/testing-dbt-models", "install": ["npx skills add AltimateAI/data-engineering-skills --skill testing-dbt-models"], "stars": 136, "pushed": "2026-10-27T06:58:19Z", "license": "MIT", "plat": "dbt", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:malloydata/publisher", "name": "publisher", "kind": "mcp", "official": null, "desc": "Publisher is the open-source analytics engine for Malloy. It lets you define data models once — and use them everywhere.", "repo": "https://github.com/malloydata/publisher", "install": [], "stars": 132, "pushed": null, "license": null, "plat": "malloy", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.stefanoamorelli/fred-mcp-server", "name": "io.github.stefanoamorelli/fred-mcp-server", "kind": "mcp", "official": null, "desc": "Federal Reserve Economic Data (FRED) MCP Server - Access all 800,000+ economic time series", "repo": "https://github.com/stefanoamorelli/fred-mcp-server", "install": ["npm:fred-mcp-server"], "stars": 131, "pushed": "2026-08-22T18:34:49Z", "license": "AGPL-3.0", "plat": "fred", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:LucasHild/mcp-server-bigquery", "name": "mcp-server-bigquery", "kind": "mcp", "official": null, "desc": "A Model Context Protocol server that provides access to BigQuery", "repo": "https://github.com/LucasHild/mcp-server-bigquery", "install": [], "stars": 131, "pushed": null, "license": null, "plat": "bigquery", "live": "Not checked (runs locally)"}, {"id": "skill:neo4j-contrib/neo4j-skills/neo4j-snowflake-graph-analytics-skill", "name": "neo4j-snowflake-graph-analytics-skill", "kind": "skill", "official": false, "desc": "Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App (\"GDS Snowflake\", \"graph algorithms in Snowflake\", \"Neo4j Graph Analytics\"). Covers the explore → prepare projection views → project-compute-write flow, the strict view/column type rules the graph engine requires, exact SQL CALL syntax, and privilege se", "repo": "https://github.com/neo4j-contrib/neo4j-skills/tree/HEAD/neo4j-snowflake-graph-analytics-skill", "install": ["npx skills add neo4j-contrib/neo4j-skills --skill neo4j-snowflake-graph-analytics-skill"], "stars": 122, "pushed": "2026-11-09T08:06:18Z", "license": "MIT", "plat": "snowflake", "live": "Not checked (runs locally)"}, {"id": "skill:exmergo/dex/claude/skills/explore", "name": "explore", "kind": "skill", "official": false, "desc": "Use this whenever you need to know what is actually in a database, warehouse, or DuckDB file before you trust it: inventory, column profiles, PII detection, grain and data-quality problems, verified joins, ER diagrams, ad-hoc SQL probes, segmentation, and the semantic layer a repo declares (dbt, Ossie, SLayer, or Cube). Trigger it on an unmet precondition, not on phrasing: if you are about to write or fix SQL against tables whose columns, types, grain, or join keys you have not verified in this ", "repo": "https://github.com/exmergo/dex/tree/HEAD/claude/skills/explore", "install": ["npx skills add exmergo/dex --skill explore"], "stars": 119, "pushed": "2026-11-14T04:27:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:zorost/alchemylake-databricks", "name": "alchemylake-databricks", "kind": "mcp", "official": null, "desc": "Governed, provenance-sealed creative rendering for Databricks — a Streamlit App + Unity Catalog ai_render() function that turns any governed source (Sample Lakehouse, UC, Genie, or BYO data) into infographics, reports, decks, video briefings, music, and podcasts.", "repo": "https://github.com/zorost/alchemylake-databricks", "install": [], "stars": 118, "pushed": null, "license": null, "plat": "databricks", "live": "Not checked (runs locally)"}, {"id": "skill:hyperfx-ai/marketing-skills/skills/analytics-insights", "name": "analytics-insights", "kind": "skill", "official": false, "desc": "Drive Google Analytics (GA4), Google Tag Manager, Google Search Console, and BigQuery from chat — tracking plans, GA4 reports, key-event (conversion) setup, custom dimensions and metrics, GTM audits, GSC performance, and GA4 BigQuery export queries. Use when the user wants an analytics audit, a GA4 report, a tracking plan, conversion setup, GTM cleanup, search-performance data, or asks \"how is the site performing?\" or \"are my conversions firing?\".", "repo": "https://github.com/hyperfx-ai/marketing-skills/tree/HEAD/skills/analytics-insights", "install": ["npx skills add hyperfx-ai/marketing-skills --skill analytics-insights"], "stars": 112, "pushed": "2026-11-11T05:11:27Z", "license": "MIT", "plat": "google analytics, bigquery", "live": "Not checked (runs locally)"}, {"id": "skill:SylphAI-Inc/skills/skills/posthog-analytics", "name": "posthog-analytics", "kind": "skill", "official": false, "desc": "Automate PostHog dashboard creation, sync, update, and export via API", "repo": "https://github.com/SylphAI-Inc/skills/tree/HEAD/skills/posthog-analytics", "install": ["npx skills add SylphAI-Inc/skills --skill posthog-analytics"], "stars": 112, "pushed": "2026-09-22T23:44:24Z", "license": null, "plat": "posthog", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:DavidOsherdiagnostica/data-gov-il-mcp", "name": "data-gov-il-mcp", "kind": "mcp", "official": null, "desc": "Advanced MCP server for seamless access to Israeli Government Open Data", "repo": "https://github.com/DavidOsherdiagnostica/data-gov-il-mcp", "install": [], "stars": 111, "pushed": null, "license": null, "plat": "israeli government open data", "live": "Not checked (runs locally)"}, {"id": "mcp:gh:santhoshravindran7/Fabric-Analytics-MCP", "name": "Fabric-Analytics-MCP", "kind": "mcp", "official": null, "desc": "A Model Context Protocol (MCP) server that enables AI assistants to securely access and analyze Microsoft Fabric Analytics data through authenticated API calls.", "repo": "https://github.com/santhoshravindran7/Fabric-Analytics-MCP", "install": [], "stars": 110, "pushed": null, "license": null, "plat": "microsoft fabric", "live": "Not checked (runs locally)"}, {"id": "skill:MAGA2010/data-master", "name": "data-master", "kind": "skill", "official": false, "desc": "Analyze datasets with preserved raw inputs, explicit cleaning decisions, reproducible figures and PDF reports. Full statistics for one tabular file; descriptive time-series analysis; integrity profiling for text, image, audio, geographic and mixed inputs. 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