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data-quality-audit

data-quality-audit

github.com/nimrodfisherData & databases504263 installsUpdated 5w ago
Repository

About data-quality-audit

data-quality-audit is a skill for checking datasets and producing quality findings for data teams and governance work. It guides checks for nulls, duplicates, referential integrity, value ranges, and freshness, then organizes findings by severity.

Key features

  1. 01Profiles nulls and completeness
  2. 02Detects row and key duplicates
  3. 03Checks referential integrity
  4. 04Validates business value ranges
  5. 05Verifies data freshness
  6. 06Produces reports and scorecards

Use cases

  1. 01Validating data before downstream reporting
  2. 02Investigating concerns about incorrect or stale data
  3. 03Assessing a new source before building on it

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill data-quality-audit

After installing, type/data-quality-auditin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
audit_report_template.html
quality_rubric.md
references/
business_rule_patterns.md
quality_dimensions.md
scripts/
duplicate_finder.py
freshness_check.py
null_counter.py
referential_integrity.py
value_range_validator.py

Details

From the project README

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

Comments

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