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Nomanriaz786Nomanriaz786/claude-consulting-team/skills/data-quality-audit/SKILL.md

data-quality-audit

Skill1 starsChanged 23 days agoMITView on GitHub

Audits data quality when reporting, operations or decisions are affected by unreliable or inconsistent records.

8 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 4 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use before analytics, migration, automation or control redesign where poor data could distort results.

    line 9
  2. Distinctive:

    Use Pareto analysis to isolate defect types causing most business exposure.

    line 21

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Data Quality Audit

When to use it

Use before analytics, migration, automation or control redesign where poor data could distort results.

What Claude needs

  • client-context.md for business background
  • Priority datasets, field definitions and extracts
  • Business rules and critical reporting uses
  • Known defects, corrections and accountable data owners

Method

  1. Prioritise critical data elements by decision impact and operational dependency.
  2. Define tests for completeness, validity, uniqueness, consistency, accuracy and timeliness.
  3. Profile fields and segment defects by source, period, workflow and responsible owner.
  4. Use Pareto analysis to isolate defect types causing most business exposure.
  5. Trace root causes through capture, transformation, transfer and maintenance steps.
  6. Set remediation, prevention, monitoring and acceptance thresholds for each issue.

Output format

Provide a data quality scorecard and defect register with element, rule, result, impact, root cause, owner and remedy. Add monitoring specifications.

Quality checks

  • Tests reflect business rules, not merely technical formats.
  • Samples and limitations are stated clearly.
  • Remedies address root causes as well as existing defects.

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