name: codebook description: Auto-generates a Markdown codebook from a dataset (CSV, DTA, Excel, Parquet) with types and summary statistics. Use when documenting variables. argument-hint: <path to dataset> allowed-tools: Bash, Read, Write, Glob, Grep

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
   • .parquet — read with pandas read_parquet
   • Other formats: ask the user how to load it

2. Load the dataset using uv run python and extract metadata for each variable:

   • Variable name
   • Data type (numeric, string, categorical, datetime)
   • Non-missing count and missing count
   • Number of unique values
   • For numeric variables: min, max, mean, median, standard deviation
   • For categorical/string variables: top 5 most frequent values with counts
   • For datetime variables: min and max date

3. Generate a Markdown codebook with:

   • Header: Dataset name, file path, number of observations, number of variables, date generated
   • Summary table: Variable name | Type | Non-missing | Unique | Description (placeholder)
   • Detailed sections per variable: Full statistics and a [FILL: description] placeholder for the user to add a human-readable description

4. Derive the output filename from the dataset name:

   • data/rawData/sample_data.csv → references/sample-data-codebook.md

5. Save to references/<dataset-name>-codebook.md

6. Report the file path and the number of variables documented.

Error handling

• If the file does not exist, report the error and suggest checking the path.
• If the file cannot be read (corrupt, unsupported format), report the error and ask for guidance.
• Never modify the source data file. This command is read-only with respect to data.