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Regression Table

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regression-table

Regression Table 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.

Formats estimation output as a publication-quality regression table with stars, SEs, and fit statistics. Use when creating a results table.

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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/regression-table/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/regression-table, 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.

Format Regression Table

Create a publication-quality regression table from estimation output in a notebook.

Arguments

  • $ARGUMENTS — a notebook reference and/or description of the table (e.g., "notebook-02 OLS results" or "main regression table with 3 specifications")

Steps

  1. Identify the source notebook and the estimation output:

    • If a notebook name is provided, read that notebook
    • If no notebook is specified, ask the user which notebook contains the regression results
    • Look for cells with estimation commands (Python: statsmodels, linearmodels; R: lm, fixest, felm; Stata: reg, reghdfe, ivregress)

  2. Ask the user for table specifications:

    • Which models/columns to include
    • Dependent variable name(s)
    • Which coefficients to display (or "all")
    • Fixed effects to report as Yes/No rows
    • Clustering level for standard errors
    • Any custom notes for the table footer

  3. Construct the table following academic conventions:

    • Header row: Dependent variable name spanning all columns, column numbers (1), (2), (3)...
    • Coefficient rows: Point estimate on top, standard error in parentheses below
    • Significance stars: * p<0.10, ** p<0.05, *** p<0.01
    • Fixed effects rows: Yes/No indicators
    • Summary rows: Observations (N), R-squared, Adjusted R-squared, or other fit statistics
    • Footer: Significance legend and notes about standard errors

  4. Create or update a cell in the specified notebook with:

    • Cell directive: #| label: tbl-<descriptive-name> (or *| for Stata)
    • Cell directive: #| tbl-cap: "<caption>" (or *| for Stata)
    • The code to generate the formatted Markdown table
    • Stata caveat: Do NOT use tbl- prefix for Stata text output — use a plain label instead (e.g., stata-regression)

  5. Sync the Jupytext pair:

    uv run jupytext --sync notebooks/<name>.md

  6. Show the user the embed shortcode to paste into index.qmd:

    {{< embed notebooks/<name>.ipynb#tbl-<label> >}}

Error handling

  • If the notebook has no estimation output, report this and ask the user to run the regressions first.
  • If the estimation output format is not recognized, ask the user to provide the raw coefficients and standard errors.