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.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stata-data-audit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ou
What does the stata-data-audit skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stata-data-audit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.