OpenParable
Skip to content
← Back to skills

Codebook

Documents
codebook

Codebook is an Agent Skill for documents published in the brycewang-stanford/Auto-Empirical-Research-Skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Auto-generates a Markdown codebook from a dataset (CSV, DTA, Excel, Parquet) with types and summary statistics. Use when documenting variables.

View on GitHub ↗
QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/29-quarcs-lab-project20XXy/dot-claude/skills/codebook/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/codebook, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

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.