codebook
Auto-generates a Markdown codebook from a dataset (CSV, DTA, Excel, Parquet) with types and summary statistics. Use when documenting variables.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill codebook --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.
# Generate Variable Codebook Auto-generate a Markdown codebook documenting all variables in a dataset. ## Arguments - `$ARGUMENTS` — path to a dataset file (e.g., `data/rawData/sample_data.csv`, `data/panel.dta`) ## Steps 1. Determine the file format from the extension: - `.csv` — read with pandas `read_csv` - `.dta` — read with pandas `read_stata` - `.xlsx` / `.xls` — read with pandas `read_excel
What does the codebook skill do?
Auto-generates a Markdown codebook from a dataset (CSV, DTA, Excel, Parquet) with types and summary statistics. Use when documenting variables.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill codebook --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.