data-quality-audit
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.
npx skills add mohitagw15856/pm-claude-skills --skill data-quality-audit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Working from a brief
- Required Inputs
- Output Format
- 1. Summary
- 2. Quality scorecard
- 3. Specific issues
- 4. Fix plan (prioritised)
- 5. Guardrails
- Quality Checks
- Anti-Patterns
What does the data-quality-audit skill do?
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.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill data-quality-audit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.