{"id":77112,"plugin_id":"plugins_6ab424ad5b448191b8fede2ebf569333","kind":"skill","collection_source":"plugin_package","comparison_source":null,"observed_at":"2026-11-03T00:12:27.068Z","digest":"2fb80d2430a9b37a9a0d4c56cf1dd6e3970a0e2e409121eae4d593f59b743329","against":null,"payload":{"description":"Design source/target data contracts, reconciliation, completeness, freshness and anomaly tests with actions.","included_files":[],"name":"design-data-quality","skill_md_contents":"---\nname: design-data-quality\ndescription: Design source/target data contracts, reconciliation, completeness, freshness and anomaly tests with actions.\n---\n\n\n# Data quality and data contracts\n**Trigger:** Missing data, unexpected duplicates, schema drift, poor reliability, quality checks, acceptance criteria or reconciliation.\n\n1. Define expected grain, contract fields and rules: nullability, uniqueness, domains, foreign keys, source-to-target conservation and freshness SLA.\n2. Distinguish check categories: schema, row-level validity, aggregate reconciliation, distribution, volume/freshness and semantic correctness.\n3. For each test specify source/target, threshold, severity, sample failures, owner and action: block, quarantine, alert or allow with observation.\n4. Check that key/row counts and financial totals use compatible scopes and currencies; differences may be legitimate, so define tolerances and exclusions.\n5. Make tests runnable on minimal fixture data when possible. Report the tested dataset and versions, not universal claims.\n6. Output an executable test matrix and sample SQL/PySpark/dbt test patterns with unverified sections identified.\n\nUse `../../references/data-quality-reconciliation.md` and `../../templates/quality-test-matrix.csv`.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}