fabric-skills

Manage Microsoft Fabric operational excellence across capacity planning, governance, security, cost optimization, and observability. Use when the request involves workspace administration, capacity monitoring, access control, compliance policies, cross-workload operational concerns, or workspace documentation and inventory. Delegates endpoint-specific implementation to specialized skills where available.

Orchestrate end-to-end Microsoft Fabric data engineering workflows that span multiple workloads and personas. Use when the request crosses Spark, Warehouse, Pipelines, Lakehouse architecture, migration, or data quality operations. Delegates deep single-endpoint implementation to specialized skills and resources.

Build full-stack applications on top of Microsoft Fabric using Python, ODBC, XMLA, and REST APIs. Use when the request involves building applications connected to Fabric data. Delegates endpoint-specific implementation to specialized skills.

Check for skills-for-fabric marketplace updates at session start. Compares local version against GitHub releases and shows changelog if updates are available. Use when the user wants to: (1) check for skill updates, (2) see what's new in skills-for-fabric, (3) verify current version. Triggers: "check for updates", "am I up to date", "what version", "update skills", "show changelog".

Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. 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".

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: 1. Create or alter KQL tables, columns, or functions 2. Ingest data into an Eventhouse (inline, from storage, streaming) 3. Configure retention, caching, or partitioning policies 4. Create or manage materialized views and update policies 5. Manage data mappings for ingestion pipelines 6. Deploy KQL schema via scripts Triggers: "create kql table", "kql ingestion", "ingest into eventhouse", "kql function", "materialized view", "kql retention policy", "eventhouse schema", "kql authoring", "create eventhouse table", "kql mapping"

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: 1. Run read-only KQL queries against an Eventhouse or KQL Database 2. Discover Eventhouse table schema and metadata 3. Analyse real-time or time-series data with KQL operators 4. Monitor ingestion health and active KQL queries 5. Export KQL results to JSON Triggers: "kql query", "kusto query", "eventhouse query", "kql database", "real-time intelligence", "time-series kql", "query eventhouse", "explore eventhouse", "show tables kql"

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".