Data Quality Audit
Scores a dataset against your own business rules and freshness expectations, and names exactly which checks failed instead of a vague pass or fail.
Overview
Data Quality Audit runs a comprehensive quality assessment right after a pipeline loads new data, when a stakeholder flags a concern about wrong totals or unexpected nulls, or when a data governance process needs a formal quality scorecard.
What it covers
- Runs a null and completeness audit, then a duplicate check at the full-row and key level.
- Validates referential integrity between parent and child tables and reports the orphan rate.
- Checks values against your own business rules and flags anything out of range.
- Runs a freshness check against the pipeline's expected update lag.
- Scores every finding by severity (critical, high, medium, low) and fills a shareable quality report plus a one-page scorecard.
Part of Data Analytics Skills
This is one of 31 skills in the Data Analytics Skills library, in the Data Quality & Validation group.
Installation
- Clone the repo once:
git clone https://github.com/nimrodfisher/data-analytics-skills.git - Copy just this skill:
cp -r data-analytics-skills/01-data-quality-validation/data-quality-audit ~/.claude/skills/(or~/.agents/skills/for Codex) - Ask your agent something like "audit this table's quality before we build reports on it" and it activates on its own.
See the Data Analytics Skills entry for the full library.
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