# Data Quality Audit

> 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.

- Skill: `nimrodfisher/data-quality-audit` (Agent Skill, multi-file: 10 files)
- Install (CLI): `npx skillmds@latest add nimrodfisher/data-quality-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nimrodfisher/data-quality-audit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: nimrodfisher (https://skillmd.com/u/nimrodfisher)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/nimrodfisher/data-quality-audit

---


# 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.

# Inputs the skill needs
- Required: dataset (CSV / Parquet / database table reference)
- Required: schema relationships — which columns are primary keys, which are foreign keys to which tables
- Required: business rules — acceptable value ranges, expected value sets, freshness SLA
- Optional: acceptable error rates — at what threshold does a failure become CRITICAL vs. HIGH
- Optional: pipeline schedule — to assess freshness relative to expected update frequency

# Output
- `assets/audit_report_template.html` (filled) — full quality report, shareable with stakeholders
- `assets/quality_rubric.md` (filled) — one-page quality scorecard with dimension scores
- Script console output — per-check pass/fail counts for each validation script