claude

data-quality-observability

Data-quality & observability team — agents (data-quality-architect, data-quality-engineer) for the layer answering 'is this data CORRECT, FRESH, COMPLETE, and can we TRUST it?': producer-boundary contracts, validation tests (not-null/unique/accepted-values/referential/distribution), freshness/volume/schema-drift/distribution monitors, threshold vs statistical anomaly detection, data-quality SLAs/SLIs, and data-incident response (triage, quarantine/circuit-breaker, root-cause, backfill). Fluent in dbt tests + dbt-expectations, Great Expectations, Soda, Elementary, managed observability (Monte Carlo, Bigeye, Metaplane), and warehouse-native checks. skills, a knowledge bank (tooling decision tree + 2026 patterns reference), and templates. Distinct from data-platform (ELT/warehouse/BI), analytics-engineering (dbt transforms), data-orchestration (schedules the runs), and data-governance-privacy (policy/PII/lineage) — this is the trust/quality layer over all of them. Requires ravenclaude-core@>=0.7.0.

9 on GitHubby Matt Corbettv0.1.2MITupdated today
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Plugin data-quality-observability from marketplace ravenclaude in mcorbett51090/RavenClaude. Commands read from the repo's manifest — today.

What it installs

Skills (3)

  • choose-data-quality-approach

    Pick the right data-quality approach and tooling for a described stack by traversing the data-quality tooling decision tree (already-on-dbt? → check shape, known-rule vs unknown-over-time → build vs buy → where checks run → tool), then return the recommended contracts/tests/monitors mix, the tool (d

  • design-data-contracts-and-tests

    From a dataset and its consumers, derive the producer-boundary data contract (schema, semantics, freshness and volume expectations, ownership) and the concrete validation test suite (not-null, unique, accepted-values, referential integrity, distribution/value-range), each test carrying a severity. R

  • set-up-data-observability-monitors

    Stand up the observability monitors that watch for the unknown over time — freshness, volume, schema-drift, and distribution/anomaly monitors — each anchored to a baseline and a tolerance (not a hard-coded magic number), then wire owner-routed alerting and link a data-incident runbook. Reach for thi

Subagents (2)

  • data-quality-architect

    Use to choose the data-quality APPROACH + tooling — 'dbt tests vs Great Expectations vs Soda vs Monte Carlo?', 'contracts vs tests vs monitors?', 'where should checks run?', 'what DQ SLAs?'. Decision-tree-driven. NOT for PII/access/retention policy (data-governance-privacy).

  • data-quality-engineer

    Use to BUILD & RUN data quality — author contracts/tests/monitors, wire into CI + orchestration + alerting, and run data-incident response (triage, root-cause to the change, quarantine/circuit-breaker, backfill). dbt-tests/GE/Soda/Elementary-fluent. NOT for tool selection (data-quality-architect).

View source on GitHub mcorbett51090.github.io

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