data-quality-audit

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