data-quality-audit-9bd349 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited data-quality-audit-9bd349 (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
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.
Ask for (if not already provided):
Overall read (🟢 usable / 🟡 fix-first / 🔴 don't trust yet) and the one issue most likely to mislead.
| 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 |
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.
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.
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).
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.