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data-quality-audit

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Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

01-data-quality-validation/data-quality-audit/SKILL.md

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# When to use
- A data pipeline has just loaded new data and needs validation before downstream reports consume it
- A stakeholder has flagged data quality concerns (wrong totals, unexpected nulls, stale data)
- You need to produce a formal data quality scorecard for a data asset as part of a data governance process
- You are onboarding a new data source and need to understand its quality profile before building on it

# Process
1. **Null and completeness audit** — run `scripts/null_counter.py` for a column-by-column null profile. Flag columns above acceptable thresholds for the business context.
2. **Duplicate detection** — run `scripts/duplicate_finder.py` to identify full-row and key-level duplicates. Determine if duplicates are intentional (versioning) or errors (pipeline fan-out).
3. **Referential integrity check** — run `scripts/referential_integrity.py` to validate that foreign key values in child tables exist in parent tables. Report orphan rate per relationship.
4. **Value range validation** — run `scripts/value_range_validator.py` with business rules defined in `references/business_rule_patterns.md`. Flag values outside acceptable ranges.
5. **Freshness check** — run `scripts/freshness_check.py` to verify the dataset is up to date — compare the latest record timestamp against the expected lag for this pipeline.
6. **Score and classify findings** — map each finding to a quality dimension using `references/quality_dimensions.md`. Assign severity (CRITICAL / HIGH / MEDIUM / LOW).
7. **Produce deliverables** — fill `assets/audit_report_template.html` for a shareable report; fill `assets/quality_rubric.md` for a concise scorecard.
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