# stata-data-audit

> Audit datasets for structure, missingness, labeling, suspicious values, duplicate identifiers, and documentation readiness. Use when a researcher asks for data QA, codebook review, sanity checks, or pre-analysis cleanup guidance.

- Skill: `brycewang-stanford/stata-data-audit` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add brycewang-stanford/stata-data-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/brycewang-stanford/stata-data-audit/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: brycewang-stanford (https://skillmd.com/u/brycewang-stanford)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/brycewang-stanford/stata-data-audit

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# Data Audit

Run a compact but explicit audit of the active dataset.

1. Start with `stata_inspect_data(action="describe")` and `stata_inspect_data(action="summary")`.
2. Use targeted `codebook`, `search`, and `stata_run` checks for key variables or suspicious patterns.
3. Report concrete issues, not generic reassurance.

Read `references/checklist.md` for the full audit checklist and recommended output format.