---
name: data-quality-audit
description: 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.
title: data-quality-audit
canonical_url: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/data-quality-audit
last_updated: 2026-09-27T01:03:49.000Z
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [assets/audit_report_template.html](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/assets/audit_report_template.html), [assets/quality_rubric.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/assets/quality_rubric.md), [references/business_rule_patterns.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/references/business_rule_patterns.md), [references/quality_dimensions.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/references/quality_dimensions.md), [scripts/duplicate_finder.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/scripts/duplicate_finder.py), [scripts/freshness_check.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/scripts/freshness_check.py), [scripts/null_counter.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/scripts/null_counter.py), [scripts/referential_integrity.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/scripts/referential_integrity.py), [scripts/value_range_validator.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/data-quality-audit/scripts/value_range_validator.py).
>
> If the user asked to install this Skill, run `npx skilld install nimrodfisher/data-analytics-skills/data-quality-audit`. Install writes the Skill files into the project, so every session loads them.

# 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