Exact public version
data-quality-auditor v1
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.
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Download package724edd9e9a491596a7f0c2dac30093b99e9dfe4d8caae4a8b5666098d5fa5ce3Package contents
Manifest
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.
Manifest SHA-256
81cfc069d6b7571e2b301879ea52be83d953bd11db1473c0eb2a7f10e0f7e604- SKILL.md9,130 bytes
- references/data-quality-concepts.md5,607 bytes
- scripts/data_profiler.py9,418 bytes
- scripts/missing_value_analyzer.py8,989 bytes
- scripts/outlier_detector.py9,419 bytes