fabric-authoring

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.

Create alerts, notifications, and automated actions on Fabric data and events via Fabric REST API and `az rest` CLI. Use when the user wants to: (1) create, update, or delete an alert or notification flow, (2) send a Teams message, send an email, or run a Fabric item when something happens, (3) connect alert logic to Eventhouse, Eventstream, Real-time Hub, or Digital Twin Builder / Ontology data, (4) adjust thresholds, filters, event triggers, or actions, (5) troubleshoot or change an existing Activator/Reflex definition. Triggers: "create an alert", "notify me when", "let me know when", "take action when", "send me an email when", "send a teams message when", "run a pipeline when", "update an alert", "delete an alert", "activator rule"

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".

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".

Assess, plan, and execute dataflow Gen1 → Gen2.1 CI/CD save-as operations via CLI (az rest / curl) against both Power BI REST and Fabric REST APIs. Scan workspaces or entire tenants for Gen1 dataflows, evaluate save-as readiness with seven risk signals (incremental refresh, BYOSA storage, Power Automate triggers, pipeline dependencies, linked entities, DirectQuery, caller-not-owner), produce a Save-As Readiness Snapshot (markdown + JSON), and invoke the SaveAsNativeArtifact API to create upgraded Gen2.1 copies of Gen1 dataflows. Use when the user wants to: (1) discover Gen1 dataflows in a workspace or tenant, (2) assess save-as readiness and risk signals, (3) upgrade or migrate Gen1 into a Gen2.1 copy, (4) validate post-save-as data integrity, (5) detect residual Gen1 references. Triggers: "save Gen1 dataflow", "convert dataflow Gen1", "upgrade dataflow", "migrate dataflow", "dataflow readiness", "Gen1 to Gen2", "dataflow save-as assessment", "saveAsNativeArtifact", "dataflow save-as scan".

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".