# Data Quality Checker

> Validate dataset completeness and basic correctness before downstream analysis. Use for nulls, duplicates, schema drift, range checks, and column-level sanity reviews; not for anomaly detection or ML evaluation.

- Skill: `majiayu000/data-quality-checker` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/data-quality-checker`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/data-quality-checker/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/data-quality-checker

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# Data Quality Checker

## Purpose

Use this skill when the user needs to know whether the dataset is trustworthy enough to continue.

## When to Use

Use this skill when:
- Checking null rates, duplicates, invalid categories, or out-of-range values
- Verifying schemas after ETL or data ingestion
- Producing a pre-modeling data sanity checklist

## Not For / Boundaries

- Rare-event or fraud detection: use `anomaly-detector`
- Model metrics and benchmark comparisons: use `evaluating-machine-learning-models`
- Report authoring and presentation polish: use `scientific-reporting`

## Typical Outputs

- Data quality scorecards
- Column-level issue summaries
- Recommended cleaning priorities before modeling or reporting

## Related Skills

- `anomaly-detector` after the basic data checks pass
- `structured-content-storage` if the dataset and outputs need stricter organization