Data Quality Observability
Use when designing or reviewing data quality checks, freshness alerts, anomaly detection, null/duplicate/orphan checks, quarantine, repair, or data incident handoff.
Install
$ agentstack add skill-jukrap-ai-agent-playbook-data-quality-observability ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
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Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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Data Quality Observability
Use this as the primary data skill for quality checks, freshness signals, alerts, and data incident handoff.
Workflow
- Identify data source, transformation boundary, dataset grain, quality dimensions, owner, consumers, and freshness or SLA expectations.
- Choose bounded checks for nulls, duplicates, orphans, ranges, enums, referential integrity, volume, drift, completeness, and freshness.
- Define alert threshold, run cadence, sample window, quarantine/repair path, and owner handoff.
- Verify with source counts, sampled rows, reconciliation queries, historical baselines, and alert evidence when possible.
Reference
Read references/quality-check-design.md for source, transform, consumer, and repair check design.
Read references/freshness-anomaly-and-alerts.md for freshness, anomaly, threshold, alert, and incident handoff checks.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: jukrap
- Source: jukrap/ai-agent-playbook
- License: MIT
- Homepage: https://www.npmjs.com/package/ai-agent-playbook
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.