Data Warehouse Metrics Layer Review

Use this skill to convert warehouse metrics layer, semantic model, KPI definition, lineage, freshness, and dashboard trust questions into a concrete artifact with owners, gates, metrics, and recovery paths.

Workflow

1. Identify decision questions, source systems, metric consumers, current dashboard conflicts, event tables, warehouse models, BI tools, owners, freshness needs, and compliance constraints.
2. Read references/data-warehouse-metrics-layer-patterns.md.
3. Classify metric as financial, product usage, growth funnel, support, reliability, marketplace, experiment, AI cost, or operational health.
4. Define canonical metric contract, grain, dimensions, filters, owner, lineage, freshness SLA, quality tests, access rules, dashboard publishing, and change log.
5. Produce metrics-layer design, state machine, decision table, event schema, quality checklist, and migration/deprecation plan.

When not to use

• Do not use for generic advice the base model already handles without this skill's specific artifact contract.

Guardrails