# Aidp Data Quality

> Run data-quality rule checks on AIDP tables — not-null, uniqueness, allowed ranges/sets, referential integrity, and freshness. Use when the user wants to validate data, check for nulls/duplicates/orphans, assert a column's domain, or gate a pipeline on quality. Expresses each rule as bounded Spark SQL and reports pass/fail with offending counts.

## Facts
- Page: https://tashan.sh/capability/skill-oracle-samples-aidp-data-quality
- tashan id: skill:oracle-samples/aidp-data-quality
- Source: https://github.com/oracle-samples/oracle-aidp-samples
- Type: skill
- Category: data
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 95.0
- Freshness: 90.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: UPL-1.0
- Official: no

## Install

```sh
cp -r aidp-data-quality ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-20 by tashan (https://tashan.sh) from public evidence. Scorer s5.