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data-quality-audit

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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.

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# 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)
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