[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-microsoft-spark-consumption-cli":3,"mdc-k2y0g7-key":39,"related-repo-microsoft-spark-consumption-cli":3143,"related-org-microsoft-spark-consumption-cli":3250},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":28,"repoUrl":29,"updatedAt":30,"license":31,"forks":32,"topics":33,"repo":34,"sourceUrl":37,"mdContent":38},"spark-consumption-cli","analyze lakehouse data with PySpark and Spark SQL","Interactive ad-hoc Spark analysis through Fabric Lakehouse Livy API sessions ONLY. This skill NEVER authors or runs a notebook: any notebook cell (%%sql\u002F%%configure) or a notebook-run-by-name (and reporting its run status) is `spark-authoring-cli`, not this skill. Scope: create a Livy session, execute PySpark, inspect output, and compute quick calculations over lakehouse data (DataFrames, cross-lakehouse joins, Delta time-travel, unstructured\u002FJSON). For Spark failure triage use `spark-operations-cli`; for plain T-SQL Lakehouse\u002FWarehouse queries use `sqldw-consumption-cli`. Triggers: \"create Livy session\", \"run Livy calculation\", \"Livy session calculation\", \"interactive Livy session\", \"PySpark\", \"analyze with PySpark\", \"Spark DataFrame\", \"lakehouse with Python\", \"PySpark data quality\", \"Delta time-travel with Spark\", \"join tables across Lakehouses\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"microsoft","Microsoft","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fmicrosoft.png",[12,16,19,22,25],{"name":13,"slug":14,"type":15},"PySpark","pyspark","tag",{"name":17,"slug":18,"type":15},"Data Engineering","data-engineering",{"name":20,"slug":21,"type":15},"Microsoft Fabric","microsoft-fabric",{"name":23,"slug":24,"type":15},"Spark","spark",{"name":26,"slug":27,"type":15},"Analytics","analytics",858,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills-for-fabric","2026-07-18T05:14:52.104846",null,237,[],{"repoUrl":29,"stars":28,"forks":32,"topics":35,"description":36},[],"A collection of skills and MCP systems to enable users of CLI, VSCode, Claude to operate over Microsoft Fabric","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills-for-fabric\u002Ftree\u002FHEAD\u002Fskills\u002Fspark-consumption-cli","---\nname: spark-consumption-cli\ndescription: >\n  Interactive ad-hoc Spark analysis through Fabric Lakehouse Livy API sessions ONLY.\n  This skill NEVER authors or runs a notebook: any notebook cell (%%sql\u002F%%configure) or a\n  notebook-run-by-name (and reporting its run status) is `spark-authoring-cli`, not this skill.\n  Scope: create a Livy session, execute PySpark, inspect output, and compute quick\n  calculations over lakehouse data (DataFrames, cross-lakehouse joins, Delta time-travel, unstructured\u002FJSON).\n  For Spark failure triage use `spark-operations-cli`; for plain T-SQL Lakehouse\u002FWarehouse queries use `sqldw-consumption-cli`.\n  Triggers: \"create Livy session\", \"run Livy calculation\", \"Livy session calculation\", \"interactive Livy session\",\n  \"PySpark\", \"analyze with PySpark\", \"Spark DataFrame\",\n  \"lakehouse with Python\", \"PySpark data quality\", \"Delta time-travel with Spark\",\n  \"join tables across Lakehouses\".\n---\n\n> **Update Check — ONCE PER SESSION (mandatory)**\n> The first time this skill is used in a session, run the **check-updates** skill before proceeding.\n> - **GitHub Copilot CLI \u002F VS Code**: invoke the `check-updates` skill.\n> - **Claude Code \u002F Cowork \u002F Cursor \u002F Windsurf \u002F Codex**: compare local vs remote package.json version.\n> - Skip if the check was already performed earlier in this session.\n\n> **CRITICAL NOTES**\n> 1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering\n> 2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering\n\n# Data Engineering Consumption — CLI Skill\n\n## Table of Contents\n\n| Task | Reference | Notes |\n|---|---|---|\n| Fabric Topology & Key Concepts | [COMMON-CORE.md § Fabric Topology & Key Concepts](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#fabric-topology--key-concepts) ||\n| Environment URLs | [COMMON-CORE.md § Environment URLs](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#environment-urls) ||\n| Authentication & Token Acquisition | [COMMON-CORE.md § Authentication & Token Acquisition](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#authentication--token-acquisition) | Wrong audience = 401; read before any auth issue |\n| Core Control-Plane REST APIs | [COMMON-CORE.md § Core Control-Plane REST APIs](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#core-control-plane-rest-apis) ||\n| Pagination | [COMMON-CORE.md § Pagination](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#pagination) ||\n| Long-Running Operations (LRO) | [COMMON-CORE.md § Long-Running Operations (LRO)](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#long-running-operations-lro) ||\n| Rate Limiting & Throttling | [COMMON-CORE.md § Rate Limiting & Throttling](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#rate-limiting--throttling) ||\n| OneLake Data Access | [COMMON-CORE.md § OneLake Data Access](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#onelake-data-access) | Requires `storage.azure.com` token, not Fabric token |\n| Job Execution | [COMMON-CORE.md § Job Execution](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#job-execution) ||\n| Capacity Management | [COMMON-CORE.md § Capacity Management](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#capacity-management) ||\n| Gotchas & Troubleshooting | [COMMON-CORE.md § Gotchas & Troubleshooting](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#gotchas--troubleshooting) ||\n| Best Practices | [COMMON-CORE.md § Best Practices](..\u002F..\u002Fcommon\u002FCOMMON-CORE.md#best-practices) ||\n| Tool Selection Rationale | [COMMON-CLI.md § Tool Selection Rationale](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#tool-selection-rationale) ||\n| Finding Workspaces and Items in Fabric | [COMMON-CLI.md § Finding Workspaces and Items in Fabric](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#finding-workspaces-and-items-in-fabric) | **Mandatory** — *READ link first* [needed for finding workspace id by its name or item id by its name, item type, and workspace id] |\n| Authentication Recipes | [COMMON-CLI.md § Authentication Recipes](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#authentication-recipes) | `az login` flows and token acquisition |\n| Fabric Control-Plane API via `az rest` | [COMMON-CLI.md § Fabric Control-Plane API via az rest](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#fabric-control-plane-api-via-az-rest) | **Always pass `--resource https:\u002F\u002Fapi.fabric.microsoft.com`** or `az rest` fails |\n| Pagination Pattern | [COMMON-CLI.md § Pagination Pattern](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#pagination-pattern) ||\n| Long-Running Operations (LRO) Pattern | [COMMON-CLI.md § Long-Running Operations (LRO) Pattern](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#long-running-operations-lro-pattern) ||\n| OneLake Data Access via `curl` | [COMMON-CLI.md § OneLake Data Access via curl](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#onelake-data-access-via-curl) | Use `curl` not `az rest` (different token audience) |\n| SQL \u002F TDS Data-Plane Access | [COMMON-CLI.md § SQL \u002F TDS Data-Plane Access](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#sql--tds-data-plane-access) | `sqlcmd` (Go) connect, query, CSV export |\n| Job Execution (CLI) | [COMMON-CLI.md § Job Execution](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#job-execution) ||\n| OneLake Shortcuts | [COMMON-CLI.md § OneLake Shortcuts](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#onelake-shortcuts) ||\n| Capacity Management (CLI) | [COMMON-CLI.md § Capacity Management](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#capacity-management) ||\n| Composite Recipes | [COMMON-CLI.md § Composite Recipes](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#composite-recipes) ||\n| Gotchas & Troubleshooting (CLI-Specific) | [COMMON-CLI.md § Gotchas & Troubleshooting (CLI-Specific)](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#gotchas--troubleshooting-cli-specific) | `az rest` audience, shell escaping, token expiry |\n| Quick Reference: `az rest` Template | [COMMON-CLI.md § Quick Reference: az rest Template](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#quick-reference-az-rest-template) ||\n| Quick Reference: Token Audience \u002F CLI Tool Matrix | [COMMON-CLI.md § Quick Reference: Token Audience ↔ CLI Tool Matrix](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md#quick-reference-token-audience--cli-tool-matrix) | Which `--resource` + tool for each service |\n| Relationship to SPARK-AUTHORING-CORE.md | [SPARK-CONSUMPTION-CORE.md § Relationship to SPARK-AUTHORING-CORE.md](..\u002F..\u002Fcommon\u002FSPARK-CONSUMPTION-CORE.md#relationship-to-spark-authoring-coremd) ||\n| Data Engineering Consumption Capability Matrix | [SPARK-CONSUMPTION-CORE.md § Data Engineering Consumption Capability Matrix](..\u002F..\u002Fcommon\u002FSPARK-CONSUMPTION-CORE.md#data-engineering-consumption-capability-matrix) ||\n| OneLake Table APIs (Schema-enabled Lakehouses) | [SPARK-CONSUMPTION-CORE.md § OneLake Table APIs (Schema-enabled Lakehouses)](..\u002F..\u002Fcommon\u002FSPARK-CONSUMPTION-CORE.md#onelake-table-apis-schema-enabled-lakehouses) | Unity Catalog-compatible metadata; requires `storage.azure.com` token |\n| Lakehouse Livy Session Management | [SPARK-CONSUMPTION-CORE.md § Livy Session Management](..\u002F..\u002Fcommon\u002FSPARK-CONSUMPTION-CORE.md#livy-session-management) | Lakehouse Livy API: session creation, states, lifecycle, termination |\n| Interactive Data Exploration | [SPARK-CONSUMPTION-CORE.md § Interactive Data Exploration](..\u002F..\u002Fcommon\u002FSPARK-CONSUMPTION-CORE.md#interactive-data-exploration) | Statement execution, output retrieval, data discovery |\n| PySpark Analytics Patterns | [SPARK-CONSUMPTION-CORE.md § PySpark Analytics Patterns](..\u002F..\u002Fcommon\u002FSPARK-CONSUMPTION-CORE.md#pyspark-analytics-patterns) | Cross-lakehouse 3-part naming, performance optimization |\n| Must\u002FPrefer\u002FAvoid | [SKILL.md § Must\u002FPrefer\u002FAvoid](#mustpreferavoid) | **MUST DO \u002F AVOID \u002F PREFER** checklists |\n| Quick Start | [SKILL.md § Quick Start](#quick-start) | CLI-specific Lakehouse Livy session setup and data exploration |\n| Key Fabric Patterns | [SKILL.md § Key Fabric Patterns](#key-fabric-patterns) | Spark pattern quick-reference table |\n| Session Cleanup | [SKILL.md § Session Cleanup](#session-cleanup) | Clean up idle Lakehouse Livy sessions via CLI |\n\n---\n\n## Must\u002FPrefer\u002FAvoid\n\n### MUST DO\n\n- Check for existing idle sessions before creating new ones\n- Use dynamic workspace\u002Flakehouse discovery\n- Follow API patterns from [COMMON-CLI.md](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md)\n\n### PREFER\n\n- **sqldw-consumption-cli for simple lakehouse queries** — row counts, SELECT, schema exploration, filtering, and aggregation on lakehouse Delta tables should use the SQL Endpoint via `sqlcmd`, not Spark. Only use this skill when the user explicitly requests PySpark, DataFrames, or Spark-specific features.\n- SQL Endpoint for Delta tables\n- Livy for unstructured\u002FJSON data or complex Python analytics\n- Session reuse over creation\n\n### AVOID\n\n- Hardcoded workspace IDs\n- Creating unnecessary sessions\n- Large result sets without LIMIT\n- **Confusing Lakehouse Livy sessions with Notebook Spark sessions** — This skill covers **Lakehouse Livy sessions** (the public Livy API at `\u002Flakehouses\u002F{lhId}\u002Flivyapi\u002F...\u002Fsessions`). Notebook Spark sessions are created internally when running a notebook via the Jobs API (`RunNotebook`) and are NOT managed through the Livy API. To run a notebook as a job, see SPARK-AUTHORING-CORE.md § Notebook Execution & Job Management\n- **Writing or generating notebook cells** — prompts that ask for `%%sql`, `%%configure`, PySpark notebook cell code, notebook deployment, or notebook execution belong to `spark-authoring-cli`, even when the cell queries data.\n\n---\n\n## Quick Start\n\n### Environment Setup\n\nApply environment detection from COMMON-CORE.md Environment Detection Pattern to set:\n- `$FABRIC_API_BASE` and `$FABRIC_RESOURCE_SCOPE`\n- `$FABRIC_API_URL` and `$LIVY_API_PATH` for Livy operations\n\n**Authentication**: Use token acquisition from [COMMON-CLI.md](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md) Environment Detection and API Configuration\n\n### Workspace & Item Discovery\n\n**Preferred**: Use [COMMON-CLI.md](..\u002F..\u002Fcommon\u002FCOMMON-CLI.md) item discovery patterns (Finding things in Fabric) to find workspaces and items by name.\n\n**Fallback** (when workspace is already known):\n```bash\n# List workspaces\naz rest --method get --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\" --query \"value[].{name:displayName, id:id}\" --output table\nread -p \"Workspace ID: \" workspaceId\n\n# List lakehouses in workspace\naz rest --method get --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\u002F$workspaceId\u002Fitems?type=Lakehouse\" --query \"value[].{name:displayName, id:id}\" --output table  \nread -p \"Lakehouse ID: \" lakehouseId\n```\n\n### Lakehouse Livy Session Management\n\n> **Two types of Spark sessions in Fabric** — This skill manages **Lakehouse Livy sessions**, created via the public Livy API endpoint (`\u002Flakehouses\u002F{lhId}\u002Flivyapi\u002F...\u002Fsessions`). These are ad-hoc interactive sessions for remote clients. **Notebook Spark sessions** are a separate mechanism — they are created internally when a Fabric Notebook is executed (via portal or Jobs API `RunNotebook`), and are managed through the notebook lifecycle, not the Livy API.\n\n```bash\n# Check for existing idle Lakehouse Livy session (avoid resource waste)\nsessionId=$(az rest --method get --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\u002F$workspaceId\u002Flakehouses\u002F$lakehouseId\u002F$LIVY_API_PATH\u002Fsessions\" --query \"sessions[?state=='idle'][0].id\" --output tsv)\n\n# Create if none available - FORCE STARTER POOL USAGE\nif [[ -z \"$sessionId\" ]]; then\n    cat > \u002Ftmp\u002Fbody.json \u003C\u003C 'EOF'\n{\n    \"name\":\"analysis\",\n    \"driverMemory\":\"56g\",\n    \"driverCores\":8,\n    \"executorMemory\":\"56g\",\n    \"executorCores\":8,\n    \"conf\": {\n        \"spark.dynamicAllocation.enabled\": \"true\",\n        \"spark.fabric.pool.name\": \"Starter Pool\"\n    }\n}\nEOF\n    sessionId=$(az rest --method post --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\u002F$workspaceId\u002Flakehouses\u002F$lakehouseId\u002F$LIVY_API_PATH\u002Fsessions\" --body @\u002Ftmp\u002Fbody.json --query \"id\" --output tsv)\n    \n    echo \"⏳ Waiting for starter pool session to be ready...\" \n    # With starter pools, this should be 3-5 seconds\n    timeout=30  # Reduced from 90s since starter pools are fast\n    while [ $timeout -gt 0 ]; do\n        state=$(az rest --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\u002F$workspaceId\u002Flakehouses\u002F$lakehouseId\u002F$LIVY_API_PATH\u002Fsessions\u002F$sessionId\" --query \"state\" --output tsv)\n        if [[ \"$state\" == \"idle\" ]]; then\n            echo \"✅ Session ready in starter pool!\"\n            break\n        fi\n        echo \"   Session state: $state (${timeout}s remaining)\"\n        sleep 3\n        timeout=$((timeout - 3))\n    done\nfi\n```\n\n### Data Exploration (Fabric-Specific Patterns)\n```bash\n# Execute statement (LLM knows Python\u002FSpark syntax)\ncat > \u002Ftmp\u002Fbody.json \u003C\u003C 'EOF'\n{\n  \"code\": \"spark.sql(\\\"SHOW TABLES\\\").show(); df = spark.table(\\\"your_table\\\"); df.describe().show()\",\n  \"kind\": \"pyspark\"\n}\nEOF\naz rest --method post --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\u002F$workspaceId\u002Flakehouses\u002F$lakehouseId\u002F$LIVY_API_PATH\u002Fsessions\u002F$sessionId\u002Fstatements\" --body @\u002Ftmp\u002Fbody.json\n```\n\n## Key Fabric Patterns\n\n| Pattern | Code | Use Case |\n|---|---|---|\n| **Table Discovery** | `spark.sql(\"SHOW TABLES\")` | List available tables |\n| **Cross-Lakehouse** | `spark.sql(\"SELECT * FROM other_workspace.table\")` | Query across workspaces |\n| **Delta Features** | `df.history()`, `df.readVersion(1)` | Time travel, versioning |\n| **Schema Evolution** | `df.printSchema()` | Understand structure |\n\n## Lakehouse Livy Session Cleanup\n```bash\n# Clean up idle Lakehouse Livy sessions (optional)\naz rest --method get --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\u002F$workspaceId\u002Flakehouses\u002F$lakehouseId\u002F$LIVY_API_PATH\u002Fsessions\" --query \"sessions[?state=='idle'].id\" --output tsv | xargs -I {} az rest --method delete --resource \"$FABRIC_RESOURCE_SCOPE\" --url \"$FABRIC_API_URL\u002Fworkspaces\u002F$workspaceId\u002Flakehouses\u002F$lakehouseId\u002F$LIVY_API_PATH\u002Fsessions\u002F{}\"\n```\n\n---\n\n**Focus**: This skill provides Fabric-specific REST API patterns. LLM already knows Python\u002FSpark syntax — we focus on Fabric integration, session management, and API endpoints.\n",{"data":40,"body":41},{"name":4,"description":6},{"type":42,"children":43},"root",[44,107,132,139,146,1069,1073,1078,1085,1109,1115,1150,1156,1241,1244,1249,1255,1260,1298,1314,1320,1336,1346,1644,1649,1689,2582,2588,2761,2766,2900,2906,3124,3127,3137],{"type":45,"tag":46,"props":47,"children":48},"element","blockquote",{},[49,69],{"type":45,"tag":50,"props":51,"children":52},"p",{},[53,60,62,67],{"type":45,"tag":54,"props":55,"children":56},"strong",{},[57],{"type":58,"value":59},"text","Update Check — ONCE PER SESSION (mandatory)",{"type":58,"value":61},"\nThe first time this skill is used in a session, run the ",{"type":45,"tag":54,"props":63,"children":64},{},[65],{"type":58,"value":66},"check-updates",{"type":58,"value":68}," skill before 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These are ad-hoc interactive sessions for remote clients. 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|",{"type":45,"tag":488,"props":3031,"children":3032},{"style":1374},[3033],{"type":58,"value":3034}," xargs",{"type":45,"tag":488,"props":3036,"children":3037},{"style":1380},[3038],{"type":58,"value":3039}," -I",{"type":45,"tag":488,"props":3041,"children":3042},{"style":1380},[3043],{"type":58,"value":3044}," {}",{"type":45,"tag":488,"props":3046,"children":3047},{"style":1380},[3048],{"type":58,"value":3049}," az",{"type":45,"tag":488,"props":3051,"children":3052},{"style":1380},[3053],{"type":58,"value":1383},{"type":45,"tag":488,"props":3055,"children":3056},{"style":1380},[3057],{"type":58,"value":1388},{"type":45,"tag":488,"props":3059,"children":3060},{"style":1380},[3061],{"type":58,"value":3062}," delete",{"type":45,"tag":488,"props":3064,"children":3065},{"style":1380},[3066],{"type":58,"value":1398},{"type":45,"tag":488,"props":3068,"children":3069},{"style":1401},[3070],{"type":58,"value":1404},{"type":45,"tag":488,"props":3072,"children":3073},{"style":1407},[3074],{"type":58,"value":1279},{"type":45,"tag":488,"props":3076,"children":3077},{"style":1401},[3078],{"type":58,"value":1414},{"type":45,"tag":488,"props":3080,"children":3081},{"style":1380},[3082],{"type":58,"value":1419},{"type":45,"tag":488,"props":3084,"children":3085},{"style":1401},[3086],{"type":58,"value":1404},{"type":45,"tag":488,"props":3088,"children":3089},{"style":1407},[3090],{"type":58,"value":1288},{"type":45,"tag":488,"props":3092,"children":3093},{"style":1380},[3094],{"type":58,"value":1569},{"type":45,"tag":488,"props":3096,"children":3097},{"style":1407},[3098],{"type":58,"value":1574},{"type":45,"tag":488,"props":3100,"children":3101},{"style":1380},[3102],{"type":58,"value":1773},{"type":45,"tag":488,"props":3104,"children":3105},{"style":1407},[3106],{"type":58,"value":1778},{"type":45,"tag":488,"props":3108,"children":3109},{"style":1380},[3110],{"type":58,"value":1783},{"type":45,"tag":488,"props":3112,"children":3113},{"style":1407},[3114],{"type":58,"value":1295},{"type":45,"tag":488,"props":3116,"children":3117},{"style":1380},[3118],{"type":58,"value":3119},"\u002Fsessions\u002F{}",{"type":45,"tag":488,"props":3121,"children":3122},{"style":1401},[3123],{"type":58,"value":2447},{"type":45,"tag":1070,"props":3125,"children":3126},{},[],{"type":45,"tag":50,"props":3128,"children":3129},{},[3130,3135],{"type":45,"tag":54,"props":3131,"children":3132},{},[3133],{"type":58,"value":3134},"Focus",{"type":58,"value":3136},": This skill provides Fabric-specific REST API patterns. LLM already knows Python\u002FSpark syntax — we focus on Fabric integration, session management, and API endpoints.",{"type":45,"tag":3138,"props":3139,"children":3140},"style",{},[3141],{"type":58,"value":3142},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":3144,"total":2022},[3145,3156,3175,3193,3207,3223,3237],{"slug":66,"name":66,"fn":3146,"description":3147,"org":3148,"tags":3149,"stars":28,"repoUrl":29,"updatedAt":3155},"check for Fabric skill marketplace updates","Check an installed skills-for-fabric plugin bundle or git clone for updates, show the matching changelog, and provide host-appropriate update guidance. Use when the user wants to: (1) check for skill updates, (2) see what changed, (3) verify the installed version. Triggers: \"check for updates\", \"am I up to date\", \"what version\", \"update skills\", \"show changelog\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3150,3151,3152],{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},{"name":3153,"slug":3154,"type":15},"Operations","operations","2026-07-24T05:41:25.892114",{"slug":3157,"name":3157,"fn":3158,"description":3159,"org":3160,"tags":3161,"stars":28,"repoUrl":29,"updatedAt":3174},"databricks-migration","migrate Databricks notebooks to Microsoft Fabric","Port Databricks notebooks and jobs to Microsoft Fabric. Provides an exhaustive dbutils to notebookutils substitution table: fs operations (runtime mounts or OneLake Shortcuts), secret scope to Key Vault URL conversion, notebook run and exit, widget replacement with parameter-tagged cells, and library install replacement with Fabric Environments. Covers Unity Catalog three-level namespace reduction to Lakehouse two-level schemas, DBFS path conversion to OneLake, Databricks Jobs to Spark Job Definitions, MLflow tracking URI removal, and Photon to Native Execution Engine substitution. Use when the user wants to: (1) replace dbutils with notebookutils, (2) collapse Unity Catalog namespaces to Lakehouse schemas, (3) convert Databricks Jobs or Delta Live Tables. Triggers: \"migrate from databricks\", \"databricks to fabric\", \"dbutils to notebookutils\", \"dbutils fabric\", \"unity catalog migration\", \"dbfs to onelake\", \"databricks notebook migration\", \"delta live tables fabric\", \"photon native execution\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3162,3163,3166,3167,3168,3171],{"name":17,"slug":18,"type":15},{"name":3164,"slug":3165,"type":15},"Databricks","databricks",{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},{"name":3169,"slug":3170,"type":15},"Migration","migration",{"name":3172,"slug":3173,"type":15},"Notebooks","notebooks","2026-07-24T05:41:22.914341",{"slug":3176,"name":3176,"fn":3177,"description":3178,"org":3179,"tags":3180,"stars":28,"repoUrl":29,"updatedAt":3192},"dataflows-authoring-cli","manage Microsoft Fabric Dataflows via CLI","Create, update, delete, and refresh Fabric Dataflows Gen2 with write-side CLI via Fabric APIs. Build mashup.pq and queryMetadata.json, preview candidate M with executeQuery\u002FcustomMashupDocument, bind connections, and configure output destinations. For saved query execution or refresh-status reads, use `dataflows-consumption-cli`. If a request explicitly insists on the Dataflows consumption or read-only path for a mutation, do not route here; let consumption refuse before any separately confirmed authoring handoff. Triggers: \"create dataflow\", \"update dataflow\", \"delete dataflow\", \"trigger dataflow refresh\", \"preview Power Query M\", \"preview before save\", \"customMashupDocument\", \"create Fabric data source connection\", \"create SQL Server source REST\", \"POST \u002Fv1\u002Fconnections\", \"supportedConnectionTypes\", \"passwordReference\", \"bind connection\", \"dataflow output destination\", \"dataflow write to lakehouse\", \"dataflow write to warehouse\", \"dataflow write to ADX\", \"DataDestinations annotation\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3181,3184,3185,3188,3189],{"name":3182,"slug":3183,"type":15},"CLI","cli",{"name":17,"slug":18,"type":15},{"name":3186,"slug":3187,"type":15},"Data Pipeline","data-pipeline",{"name":20,"slug":21,"type":15},{"name":3190,"slug":3191,"type":15},"REST API","rest-api","2026-07-31T05:54:59.059517",{"slug":3194,"name":3194,"fn":3195,"description":3196,"org":3197,"tags":3198,"stars":28,"repoUrl":29,"updatedAt":3206},"e2e-medallion-architecture","implement Medallion Architecture in Microsoft Fabric","Plan and implement end-to-end Microsoft Fabric data platforms and Medallion Architecture (Bronze\u002FSilver\u002FGold) lakehouse patterns using PySpark, Delta Lake, Lakehouse\u002FWarehouse items, Fabric Pipelines, and semantic-model handoff. Use when the user wants to: (1) design a Bronze\u002FSilver\u002FGold data lakehouse, (2) plan an ingestion-to-semantic-model Fabric data platform, (3) set up multi-layer workspaces with lakehouses for each tier, (4) build ingestion-to-analytics pipelines with data quality enforcement, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. For natural-language business questions over existing Power BI report data, use `fabriciq`. Triggers: \"medallion architecture\", \"bronze silver gold\", \"lakehouse layers\", \"e2e data pipeline\", \"end-to-end lakehouse\", \"data lakehouse pattern\", \"multi-layer lakehouse\", \"build medallion\", \"setup medallion\", \"end-to-end Fabric data platform\", \"bronze silver gold platform\", \"ingestion semantic model platform\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3199,3202,3203,3204,3205],{"name":3200,"slug":3201,"type":15},"Architecture","architecture",{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-31T05:54:57.038396",{"slug":3208,"name":3208,"fn":3209,"description":3210,"org":3211,"tags":3212,"stars":28,"repoUrl":29,"updatedAt":3222},"eventhouse-authoring-cli","manage Fabric Eventhouse and KQL databases","Execute KQL management commands (table management, ingestion, policies, functions, materialized views) against Fabric Eventhouse and KQL Databases via CLI. Use when the user wants to:\n  1. Create or alter KQL tables, columns, or functions\n  2. Ingest data into an Eventhouse (inline, from storage, streaming)\n  3. Configure retention, caching, or partitioning policies\n  4. Create or manage materialized views and update policies\n  5. Manage data mappings for ingestion pipelines\n  6. Deploy KQL schema via scripts\nTriggers: \"create kql table\", \"kql ingestion\", \"ingest into eventhouse\", \"kql function\", \"materialized view\", \"kql retention policy\", \"eventhouse schema\", \"kql authoring\", \"create eventhouse table\", \"kql mapping\"\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3213,3216,3219,3220,3221],{"name":3214,"slug":3215,"type":15},"Database","database",{"name":3217,"slug":3218,"type":15},"KQL","kql",{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},{"name":3153,"slug":3154,"type":15},"2026-04-06T18:36:51.340409",{"slug":3224,"name":3224,"fn":3225,"description":3226,"org":3227,"tags":3228,"stars":28,"repoUrl":29,"updatedAt":3236},"eventhouse-consumption-cli","query Fabric Eventhouse with KQL","Run KQL queries against Fabric Eventhouse for real-time intelligence and time-series analytics using `az rest` against the Kusto REST API. Covers KQL operators (where, summarize, join, render), Eventhouse schema discovery (.show tables), time-series patterns with bin(), and ingestion monitoring. Use when the user wants to:\n  1. Run read-only KQL queries against an Eventhouse or KQL Database\n  2. Discover Eventhouse table schema and metadata\n  3. Analyse real-time or time-series data with KQL operators\n  4. Monitor ingestion health and active KQL queries\n  5. Export KQL results to JSON\nTriggers: \"kql query\", \"kusto query\", \"eventhouse query\", \"kql database\", \"real-time intelligence\", \"time-series kql\", \"query eventhouse\", \"explore eventhouse\", \"show tables kql\"\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3229,3230,3231,3232,3233],{"name":26,"slug":27,"type":15},{"name":3217,"slug":3218,"type":15},{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},{"name":3234,"slug":3235,"type":15},"Real-Time Intelligence","real-time-intelligence","2026-04-06T18:36:45.018807",{"slug":3238,"name":3238,"fn":3239,"description":3240,"org":3241,"tags":3242,"stars":28,"repoUrl":29,"updatedAt":3249},"eventstream-consumption-cli","monitor Fabric Eventstream ingestion pipelines","List, inspect, and monitor Fabric Eventstream real-time ingestion pipelines via the Items REST API. Discover Eventstreams across workspaces, decode base64 graph topologies tracing event flow from source through operators to destination nodes. Validate connection IDs, wiring, retention policies (1-90 days), and throughput levels. Retrieve Custom Endpoint Kafka credentials via Topology API. **Invoke this skill** to: (1) list Eventstreams, (2) inspect Eventstream topology showing sources and destinations, (3) validate Eventstream configurations, (4) check Eventstream retention policy and throughput level, (5) get connection strings. Triggers: \"list eventstreams\", \"inspect eventstream\", \"describe eventstream topology\", \"eventstream operator nodes\", \"eventstream sources and destinations\", \"eventstream health\", \"eventstream status\", \"eventstream configuration\", \"eventstream retention\", \"eventstream throughput level\", \"eventstream connection string\", \"custom endpoint credentials\", \"check eventstream\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3243,3244,3245,3246],{"name":3182,"slug":3183,"type":15},{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":3247,"slug":3248,"type":15},"Monitoring","monitoring","2026-07-31T05:55:03.045486",{"items":3251,"total":3440},[3252,3274,3295,3312,3327,3344,3355,3368,3383,3398,3415,3428],{"slug":3253,"name":3253,"fn":3254,"description":3255,"org":3256,"tags":3257,"stars":3271,"repoUrl":3272,"updatedAt":3273},"rushstack-best-practices","manage Rush monorepos with best practices","Provides best practices and guidance for working with Rush monorepos. Use when the user is working in a Rush-based repository, asks about Rush commands (install, update, build, rebuild), needs help with project selection, dependency management, build caching, subspace configuration, or troubleshooting Rush-specific issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3258,3261,3264,3265,3268],{"name":3259,"slug":3260,"type":15},"Engineering","engineering",{"name":3262,"slug":3263,"type":15},"Local Development","local-development",{"name":9,"slug":8,"type":15},{"name":3266,"slug":3267,"type":15},"Project Management","project-management",{"name":3269,"slug":3270,"type":15},"Rush","rush",6484,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frushstack","2026-04-06T18:34:44.965032",{"slug":3275,"name":3275,"fn":3276,"description":3277,"org":3278,"tags":3279,"stars":3292,"repoUrl":3293,"updatedAt":3294},"azure-ai-agents-persistent-dotnet","build AI agents with Azure .NET SDK","Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: \"PersistentAgentsClient\", \"persistent agents\", \"agent threads\", \"agent runs\", \"streaming agents\", \"function calling agents .NET\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3280,3283,3286,3289],{"name":3281,"slug":3282,"type":15},".NET","net",{"name":3284,"slug":3285,"type":15},"Agents","agents",{"name":3287,"slug":3288,"type":15},"Azure","azure",{"name":3290,"slug":3291,"type":15},"LLM","llm",2804,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills","2026-07-03T16:32:10.297433",{"slug":3296,"name":3296,"fn":3297,"description":3298,"org":3299,"tags":3300,"stars":3292,"repoUrl":3293,"updatedAt":3311},"azure-ai-anomalydetector-java","build anomaly detection applications with Java","Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate\u002Fmultivariate anomaly detection, time-series analysis, or AI-powered monitoring.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3301,3302,3303,3306,3309,3310],{"name":26,"slug":27,"type":15},{"name":3287,"slug":3288,"type":15},{"name":3304,"slug":3305,"type":15},"Data Analysis","data-analysis",{"name":3307,"slug":3308,"type":15},"Java","java",{"name":9,"slug":8,"type":15},{"name":3247,"slug":3248,"type":15},"2026-05-13T06:14:16.261754",{"slug":3313,"name":3313,"fn":3314,"description":3315,"org":3316,"tags":3317,"stars":3292,"repoUrl":3293,"updatedAt":3326},"azure-ai-contentsafety-java","build content moderation applications with Azure AI","Build content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text\u002Fimage analysis, blocklist management, or harm detection for hate, violence, sexual content, and self-harm.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3318,3321,3322,3323],{"name":3319,"slug":3320,"type":15},"AI Infrastructure","ai-infrastructure",{"name":3287,"slug":3288,"type":15},{"name":3307,"slug":3308,"type":15},{"name":3324,"slug":3325,"type":15},"Security","security","2026-07-07T06:53:31.293235",{"slug":3328,"name":3328,"fn":3329,"description":3330,"org":3331,"tags":3332,"stars":3292,"repoUrl":3293,"updatedAt":3343},"azure-ai-contentsafety-py","detect harmful content with Azure AI Content Safety","Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.\nTriggers: \"azure-ai-contentsafety\", \"ContentSafetyClient\", \"content moderation\", \"harmful content\", \"text analysis\", \"image analysis\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3333,3334,3337,3338,3339,3342],{"name":3287,"slug":3288,"type":15},{"name":3335,"slug":3336,"type":15},"Compliance","compliance",{"name":3290,"slug":3291,"type":15},{"name":9,"slug":8,"type":15},{"name":3340,"slug":3341,"type":15},"Python","python",{"name":3324,"slug":3325,"type":15},"2026-07-18T05:14:23.017504",{"slug":3345,"name":3345,"fn":3346,"description":3347,"org":3348,"tags":3349,"stars":3292,"repoUrl":3293,"updatedAt":3354},"azure-ai-language-conversations-py","implement conversational language understanding with Python","Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3350,3351,3352,3353],{"name":26,"slug":27,"type":15},{"name":3287,"slug":3288,"type":15},{"name":3290,"slug":3291,"type":15},{"name":3340,"slug":3341,"type":15},"2026-07-31T05:54:29.068751",{"slug":3356,"name":3356,"fn":3357,"description":3358,"org":3359,"tags":3360,"stars":3292,"repoUrl":3293,"updatedAt":3367},"azure-ai-translation-text-py","translate text using Azure AI services","Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.\nTriggers: \"text translation\", \"translator\", \"translate text\", \"transliterate\", \"TextTranslationClient\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3361,3364,3365,3366],{"name":3362,"slug":3363,"type":15},"API Development","api-development",{"name":3287,"slug":3288,"type":15},{"name":9,"slug":8,"type":15},{"name":3340,"slug":3341,"type":15},"2026-07-18T05:14:16.988376",{"slug":3369,"name":3369,"fn":3370,"description":3371,"org":3372,"tags":3373,"stars":3292,"repoUrl":3293,"updatedAt":3382},"azure-ai-vision-imageanalysis-py","analyze images with Azure AI Vision","Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.\nTriggers: \"image analysis\", \"computer vision\", \"OCR\", \"object detection\", \"ImageAnalysisClient\", \"image caption\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3374,3375,3378,3381],{"name":3287,"slug":3288,"type":15},{"name":3376,"slug":3377,"type":15},"Computer Vision","computer-vision",{"name":3379,"slug":3380,"type":15},"Images","images",{"name":3340,"slug":3341,"type":15},"2026-07-18T05:14:18.007737",{"slug":3384,"name":3384,"fn":3385,"description":3386,"org":3387,"tags":3388,"stars":3292,"repoUrl":3293,"updatedAt":3397},"azure-appconfiguration-java","manage configuration with Azure App Configuration","Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots.\nTriggers: \"ConfigurationClient java\", \"app configuration java\", \"feature flag java\", \"configuration setting java\", \"azure config java\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3389,3390,3393,3396],{"name":3287,"slug":3288,"type":15},{"name":3391,"slug":3392,"type":15},"Configuration","configuration",{"name":3394,"slug":3395,"type":15},"Feature Flags","feature-flags",{"name":3307,"slug":3308,"type":15},"2026-07-03T16:32:01.278468",{"slug":3399,"name":3399,"fn":3400,"description":3401,"org":3402,"tags":3403,"stars":3292,"repoUrl":3293,"updatedAt":3414},"azure-cosmos-rust","build applications with Azure Cosmos DB","Azure Cosmos DB library for Rust (NoSQL API). Document CRUD, containers, and globally distributed data.\nTriggers: \"cosmos db rust\", \"CosmosClient rust\", \"document crud rust\", \"NoSQL rust\", \"partition key rust\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3404,3407,3408,3411],{"name":3405,"slug":3406,"type":15},"Cosmos DB","cosmos-db",{"name":3214,"slug":3215,"type":15},{"name":3409,"slug":3410,"type":15},"NoSQL","nosql",{"name":3412,"slug":3413,"type":15},"Rust","rust","2026-07-31T05:54:27.021432",{"slug":3416,"name":3416,"fn":3400,"description":3417,"org":3418,"tags":3419,"stars":3292,"repoUrl":3293,"updatedAt":3427},"azure-cosmos-ts","Azure Cosmos DB JavaScript\u002FTypeScript SDK (@azure\u002Fcosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management. Triggers: \"Cosmos DB\", \"@azure\u002Fcosmos\", \"CosmosClient\", \"document CRUD\", \"NoSQL queries\", \"bulk operations\", \"partition key\", \"container.items\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3420,3421,3422,3423,3424],{"name":3405,"slug":3406,"type":15},{"name":3214,"slug":3215,"type":15},{"name":9,"slug":8,"type":15},{"name":3409,"slug":3410,"type":15},{"name":3425,"slug":3426,"type":15},"TypeScript","typescript","2026-07-03T16:31:19.368382",{"slug":3429,"name":3429,"fn":3430,"description":3431,"org":3432,"tags":3433,"stars":3292,"repoUrl":3293,"updatedAt":3439},"azure-data-tables-java","build table storage applications with Java","Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3434,3435,3436,3437,3438],{"name":3287,"slug":3288,"type":15},{"name":3405,"slug":3406,"type":15},{"name":3214,"slug":3215,"type":15},{"name":3307,"slug":3308,"type":15},{"name":3409,"slug":3410,"type":15},"2026-05-13T06:14:17.582229",267]