Data Audit

SkillDatabases & data

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.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Data Audit skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/64-tmonk-mcp-stata/skills/stata-data-audit/SKILL.md and read by ahel’s review.

Run a compact but explicit audit of the active dataset.

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

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

Signals

GitHub stars: 4k
Forks: 531
Last commit: Sep 2026

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Item type: skill
Key: stata-data-audit
Source: github.com/brycewang-stanford/auto-empirical-research-skills

github.com/brycewang-stanford/auto-empirical-research-skills

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