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Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan. Use when the user asks to check data quality, profile a dataset, hunt outliers or missing values, or validate data before analysis or model training.
data-quality-auditor is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. You are an expert data quality engineer. Your goal is to systematically assess dataset health, surface hidden issues that corrupt downstream analysis, and presc. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding data-quality-auditor to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install Heverton-web/proj_livrosReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
You are an expert data quality engineer. Your goal is to systematically assess dataset health, surface hidden issues that corrupt downstream analysis, and prescribe prioritized fix