{
  "id": "fabric-analytics-engineering",
  "name": "Fabric Analytics Engineering",
  "type": "skill",
  "provider": "microsoft",
  "harnesses": [
    "codex",
    "claude-code",
    "cursor",
    "gemini",
    "kiro",
    "other"
  ],
  "summary": "Review Microsoft Fabric analytics engineering artifacts: Fabric Data Warehouse T-SQL design and anti-patterns, dimensional modeling (star schema, fact and dimension tables, relationships), semantic model design (Direct Lake vs Import vs DirectQuery selection, table layout, relationship cardinality), DAX measure correctness and optimization (iterators, filter context, variables, CALCULATE), data preparation and transformation quality, and reusable certified semantic models feeding Power BI reports. Use to fix bad star schemas, slow DAX, untrustworthy measures, and warehouse anti-patterns. Distinct from governance: this skill covers build quality and modeling correctness, not RLS or workspace trust. Static review only; production warehouse schema changes and semantic-model deployment are live-guard gated.",
  "source_type": "original",
  "official_docs": [
    "https://learn.microsoft.com/fabric/data-warehouse/dimensional-modeling-overview",
    "https://learn.microsoft.com/fabric/fundamentals/direct-lake-overview",
    "https://learn.microsoft.com/fabric/data-warehouse/data-warehousing",
    "https://learn.microsoft.com/dax/dax-overview",
    "https://learn.microsoft.com/credentials/certifications/resources/study-guides/dp-600"
  ],
  "security_notes": "Static review only. Never execute DDL or DML against production Fabric Data Warehouse schemas, deploy or overwrite production semantic models, publish Power BI reports to production workspaces, or modify deployment-pipeline stages without explicit human approval, blast-radius assessment, and a rollback path; these are live-guard gated and must be escalated to a Fabric or analytics administrator. Do not ask for credentials, tenant IDs, workspace URLs, or customer data. Treat missing surrogate keys in fact tables, fan-out or chasm-trap join patterns, incorrect DAX filter context, measures using calculated columns instead of explicit DAX, DirectQuery fallback on Direct Lake SQL views, and unverified measure outputs as correctness risks until reviewed. Label every finding as documented artifact, user-provided sanitized evidence, documentation-based, or inference.",
  "last_verified": "2026-06-17",
  "path": "skills/microsoft/fabric-analytics-engineering",
  "author": "github: VincentChuWaiChow",
  "version": "0.1.0",
  "category": "data",
  "companion_agents": ["fabric-analytics-engineering-agent"]
}