Codebook
- 1 installs
- 3.2k repo stars
- Updated August 4, 2026
- brycewang-stanford/awesome-agent-skills-for-empirical-research
codebook is a skill that auto-generates a Markdown codebook of variable types and summary statistics from a dataset.
About
codebook is a skill that auto-generates a Markdown codebook documenting the variables in a dataset. A researcher uses it to produce a variable reference with types and summary statistics from a CSV, Stata, Excel, or Parquet file. It reads the data read-only and outputs a summary table plus per-variable detail sections with description placeholders.
- Auto-generates a Markdown codebook from a dataset
- Supports CSV, Stata, Excel, and Parquet inputs
- Documents variable types and summary statistics, read-only on source data
Codebook by the numbers
- 1 all-time installs (skills.sh)
- Ranked #1,361 of 1,879 Documentation skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
codebook capabilities & compatibility
- Capabilities
- data documentation · codebook generation
- Use cases
- documentation · data analysis
- Pricing
- Free
What codebook says it does
Auto-generates a Markdown codebook from a dataset (CSV, DTA, Excel, Parquet) with types and summary statistics. Use when documenting variables.
Never modify the source data file. This command is read-only with respect to data.
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| Installs | 1 |
|---|---|
| repo stars | ★ 3.2k |
| Last updated | August 4, 2026 |
| Repository | brycewang-stanford/awesome-agent-skills-for-empirical-research ↗ |
What it does
Auto-generate a Markdown codebook of variable types and summary statistics from a CSV, Stata, Excel, or Parquet dataset.
Who is it for?
Documenting dataset variables with types and summary statistics
When should I use this skill?
Generating a Markdown codebook for a dataset file
What you get
A Markdown codebook with a summary table and per-variable statistics and description placeholders.
- Markdown codebook file
- variable summary table
By the numbers
- 4 supported dataset formats (CSV, DTA, Excel, Parquet)
- top 5 frequent values per categorical variable
Files
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 pandasread_csv.dta— read with pandasread_stata.xlsx/.xls— read with pandasread_excel.parquet— read with pandasread_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.
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