Data Quality Observability Review

Use this skill to convert a data quality, freshness, completeness, schema drift, lineage, alerting, and metric trust question into a concrete artifact with owners, gates, metrics, and recovery paths.

Workflow

1. Identify critical datasets, decisions they drive, owners, data producers, consumers, SLAs, freshness needs, and blast radius of wrong data.
2. Read references/data-quality-observability-patterns.md.
3. Classify quality checks: schema, freshness, volume, uniqueness, referential integrity, distribution, reconciliation, privacy, and semantic metric checks.
4. Define monitors, alert thresholds, owner routing, backfill policy, dashboard trust states, incident comms, and data contract changes.
5. Produce quality map, state machine, decision table, event schema, alert policy, and backfill checklist.

When not to use

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

Guardrails