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.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/data-quality-audit && cp -r /tmp/data-quality-audit/plugins/pm-data/skills/data-quality-audit ~/.claude/skills/data-quality-auditSKILL.md
# 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
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Build a structured account plan for any key customer or target account. Use when asked to create an account plan, key account strategy, strategic account review, or territory plan. Produces a complete account plan with relationship map, growth opportunities, risks, and 90-day action plan.