Update to Data Engineering Copilot

{
  "description": "Review SQL data-model grain, key uniqueness, fact/dimension joins, slowly changing dimensions and metric definitions.",
  "included_files": [],
  "name": "review-data-model",
  "skill_md_contents": "---\nname: review-data-model\ndescription: Review SQL data-model grain, key uniqueness, fact/dimension joins, slowly changing dimensions and metric definitions.\n---\n\n\n# Model grain, semantics and correctness\n**Trigger:** Double-counted metrics, ambiguous keys, wrong joins, SCD, star schema, business metric design or data contract review.\n\n1. Identify intended business entity and grain of **each** input/output table. Mark primary/foreign keys, nullable keys and one-to-many relationships.\n2. Establish metric denominator, event time, valid-time/system-time and semantic definitions; use actual domain definitions, not guesses.\n3. Test join cardinality and fan-out before aggregate measures. Reduce child tables to agreed grain before joining; don't use DISTINCT to conceal root causes.\n4. For SCD type 2, define effective ranges, overlapping current rows, late corrections, surrogate keys and point-in-time joins.\n5. Check NULL semantics, time zones, currency, cancellation/refund treatment and missing/duplicate rows.\n6. Return model specification, sample reconciliation SQL and a regression checklist for BI consumers.\n\nRead `../../references/schema-contracts.md`, `../../references/warehouse-dbt.md`, `../../templates/model-contract.md`.\n"
}