AI Agent Workflow Collection

Create, update, delete, and manage Fabric Dataflows Gen2 artifacts with Power Query M mashup definitions via CLI (az rest / curl). Uses az rest and curl against the Fabric REST API to author definitions containing base64-encoded mashup.pq, queryMetadata.json, and .platform parts. Supports creating dataflows with inline definitions, modifying mashup queries, binding connections, triggering Execute refresh jobs with typed parameter overrides, and exporting definitions for CI/CD. Use when the user wants to: (1) create a new Dataflow Gen2 with Power Query M queries, (2) update a dataflow mashup definition, (3) trigger a dataflow refresh job, (4) bind or manage dataflow connections, (5) set up CI/CD via definition export and import, (6) delete a dataflow, (7) configure staging destinations. Triggers: "create dataflow", "author dataflow", "Power Query M", "mashup document", "update dataflow definition", "refresh dataflow", "dataflow connection", "ETL dataflow", "dataflow CI/CD".

Create, manage, and deploy Power BI semantic models inside Microsoft Fabric workspaces via az rest CLI against Fabric and Power BI REST APIs. Use when the user wants to: (1) create a semantic model from TMDL definition files, (2) retrieve or download semantic model definitions, (3) update a semantic model definition with modified TMDL, (4) trigger or manage dataset refresh operations, (5) configure data sources, parameters, or permissions, (6) deploy semantic models between pipeline stages. Covers Fabric Items API (CRUD) and Power BI Datasets API (refresh, data sources, permissions). For read-only DAX queries, use powerbi-consumption-cli. For fine-grained modeling changes, route to powerbi-modeling-mcp. Triggers: "create semantic model", "upload TMDL", "download semantic model TMDL", "refresh dataset", "semantic model deployment pipeline", "dataset permissions", "list dataset users", "semantic model authoring".

Diagnose failed Spark jobs, unhealthy Livy sessions, and performance bottlenecks in Microsoft Fabric via read-only CLI triage. Use when the user wants to: (1) diagnose why a Spark job, notebook run, or Lakehouse job failed, (2) triage stuck or dead Livy sessions, (3) identify OOM, shuffle spill, or data skew, (4) retrieve driver and executor logs or Spark Advisor findings, (5) copy event logs and start a local Spark History Server, (6) diagnose all Spark activities within a failed pipeline run. Triggers: "diagnose my failed notebook", "why did my spark job fail", "triage spark failure", "diagnose pipeline run failure", "why did my pipeline fail", "livy session stuck in starting", "spark executor OOM", "check spark advisor findings", "shuffle spill diagnosis", "why did my lakehouse job fail", "diagnose lakehouse table load", "data skew diagnosis", "open spark history server locally", "analyze spark failure logs", "spark job triage".