Data quality observability
Skill jukrap/ai-agent-playbook/skills/data/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.From its SKILL.md
npx -y skills add jukrap/ai-agent-playbook --skill data-quality-observabilityAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
1.1 KB, 187 tokens by cl100k_base, as published. Nobody here has run it
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.
What ships with it: 2 files
2.4 KB alongside SKILL.md