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Use codebook to generate a Markdown variable codebook for [dataset path], save it under references/, and leave the source dataset unchanged.
Required inputs
[dataset path]
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SKILL.md
name: codebookdescription: 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
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
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
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