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.
Scanned 9/3/2026
Install to Claude Code
npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stata-data-audit --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: stata-data-audit
description: 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.
---
# 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.
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