Update to Data Engineering Copilot

{
  "description": "Design or diagnose Microsoft Fabric Lakehouse, OneLake, data pipelines, shortcuts and deployment workflows.",
  "included_files": [],
  "name": "design-fabric-lakehouse",
  "skill_md_contents": "---\nname: design-fabric-lakehouse\ndescription: Design or diagnose Microsoft Fabric Lakehouse, OneLake, data pipelines, shortcuts and deployment workflows.\n---\n\n\n# Microsoft Fabric / OneLake\n**Trigger:** Fabric pipelines, Lakehouse, Spark notebooks, Warehouse, OneLake shortcuts, Dataflow Gen2 or workspace promotion.\n\n1. Identify Fabric capacity/workspace, source systems, artifacts, OneLake tables/files semantics and expected consumption path (Spark, SQL analytics endpoint, Power BI).\n2. Choose shortcuts vs physical copy intentionally: consider transformation control, source permissions, data residency, external shortcuts, cost and freshness.\n3. Design bronze/silver/gold with explicit table grain and idempotent ingestion; use metadata/control tables for variable source lists only when appropriate.\n4. Clarify Lakehouse versus Warehouse behavior, Delta compatibility, schema evolution and incremental change semantics from current Microsoft docs.\n5. For Git integration and deployment pipelines, recognize artifact **metadata** and data are not the same; deploying Lakehouse definitions does not copy table data by default.\n6. Validate workspace IDs, connections, credentials, shortcut targets and environment mapping. Plan smoke tests and rollbacks per environment.\n\nUse `../../references/fabric-onelake.md` and `../../references/schema-contracts.md`. Do not assert a Fabric feature exists across all regions/workspaces without verification.\n"
}