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name: data-quality-audit description: "Audit a dataset for the quality problems that silently break analysis — missingness, duplicates, outliers, type and range errors, consistency, and freshness — and produce a prioritised fix list. Use when asked to assess data quality, audit a dataset, check data before analysis, or explain why numbers look off. Produces a structured quality report across the standard dimensions, the specific issues found (with the checks to run), severity, and how to fix each."

Data Quality Audit Skill

Bad analysis usually starts with bad data nobody checked. This skill audits a dataset across the dimensions that matter, names the specific issues (and the exact check to confirm each), and prioritises fixes by how much they distort the answer.

Working from a brief

Given a dataset description, sample rows, or a schema, produce the full audit anyway — infer the likely issues for that kind of data and give the concrete check (SQL/pandas-style) to verify each. If given actual data, ground the findings in it. Never just say "check for errors"; specify them.

Required Inputs

Ask for (if not already provided):

• The dataset — schema, a sample, or a description (what each column is, the grain)
• What it'll be used for (the analysis/decision it feeds — focuses the audit)
• Source & freshness (where it comes from, how often it updates)
• Known issues the user already suspects

Output Format

1. Summary

Overall read (🟢 usable / 🟡 fix-first / 🔴 don't trust yet) and the one issue most likely to mislead.

2. Quality scorecard

Dimension	Check	Finding	Severity
Completeness	nulls / missing per key column
Uniqueness	duplicate rows / keys
Validity	type, format, range, allowed values
Consistency	cross-field & cross-table agreement
Accuracy	sanity vs known totals / reality
Timeliness	freshness, gaps in the time series

3. Specific issues

For each real issue: what it is, the check to confirm it (a concrete query/snippet), why it matters for the intended use, and severity.

4. Fix plan (prioritised)

Ordered by impact-on-the-decision: what to fix first, how (drop / impute / dedupe / cast / clamp / re-source), and what to flag rather than fix.

5. Guardrails

2–3 automated checks to add so these issues get caught next time (e.g. a not-null assertion, a row-count delta alarm, an allowed-values test).

Quality Checks

• Covers all six dimensions, not just missing values
• Each issue comes with a concrete check to confirm it, not just a label
• Severity is judged against the intended use of the data
• Fix plan is prioritised by impact and says fix-vs-flag
• Recommends guardrails to prevent recurrence

Anti-Patterns

• Only checking for nulls and calling it done
• "Clean your data" with no specific issues or checks
• Treating all issues as equally severe regardless of the decision
• Fixing data silently with no record of what was changed

Example Trigger Phrases

• "Assess data quality."
• "Audit a dataset."
• "Check data before analysis."
• "Explain why numbers look off."