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Profile a dataset for completeness, duplicates, outliers, and schema issues before anything uses it.
Catches the business-critical nulls and duplicate keys before a report depends on them — concrete column-level issues, no hand-wavy causal claims.
Listed for review
No verified public repo for this skill yet, so this page does not give you an install command. Skills with a verified source install in one command — or fully manual: copy the skill folder into .claude/skills/ and your agent picks it up.
Boostor Quality Score
84/100 · B
Data Quality Profiler reads a dataset against your schema expectations and the business meaning of each column, then returns a profile: completeness, uniqueness, outliers, schema mismatches, and recommended fixes. It reports concrete column-level issues — the business-critical nulls and duplicate keys that break reports later — and avoids unsupported causal claims, so problems surface before a report or automation already depends on the data.
Audit product analytics for missing events, vague names, and payload gaps tied to real funnel questions.
Ties every event to a decision question and flags PII in payloads — so your analytics answers funnel questions instead of just piling up.
Transparent + deterministic: every point above is computed from this skill's real fields plus a prompt-injection safety scan. No black box, no pay-to-rank.
Trace, diff, and fix broken data transformations in any ETL pipeline.
Debugging an ETL means hunting which transform broke the data. This snapshots each step and walks the diff back to the first failing one for you.
Write an A/B test readout that separates significant results from directional signals from noise.
Separates a significant result from a directional signal from noise — and surfaces the sample-size and seasonality caveats before you ship a win.