Regression Table
- 1 installs
- 3.2k repo stars
- Updated August 4, 2026
- brycewang-stanford/awesome-agent-skills-for-empirical-research
regression-table is a Claude skill that formats regression estimation output from a notebook into a publication-quality table with stars, standard errors, and fit statistics.
About
A skill that formats estimation output from a notebook into a publication-quality regression table. It adds coefficient rows with standard errors, significance stars, fixed-effects Yes/No rows, and summary statistics like N and R-squared, following academic conventions. A researcher uses it when turning regression results into a results table for a paper. It recognizes statsmodels, linearmodels, R fixest, and Stata reghdfe output and emits a Quarto embed shortcode.
- Formats estimation output as a publication-quality regression table
- Adds significance stars, standard errors, fixed-effects rows, and fit statistics
- Recognizes statsmodels, linearmodels, R fixest/felm, and Stata reghdfe output
Regression Table by the numbers
- 1 all-time installs (skills.sh)
- Ranked #1,803 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
regression-table capabilities & compatibility
- Capabilities
- regression table · results formatting
- Use cases
- data analysis · documentation
What regression-table says it does
Formats estimation output as a publication-quality regression table with stars, SEs, and fit statistics. Use when creating a results table.
**Significance stars:** `*` p<0.10, `**` p<0.05, `***` p<0.01
**Summary rows:** Observations (N), R-squared, Adjusted R-squared, or other fit statistics
npx skills add https://github.com/brycewang-stanford/awesome-agent-skills-for-empirical-research --skill regression-tableAdd your badge
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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
Format regression estimation output into a publication-quality results table for a paper.
Who is it for?
Turning regression results into a formatted, journal-style results table.
Skip if: Running the regressions themselves, which requires estimation output to already exist.
When should I use this skill?
You have estimation output in a notebook and need a formatted results table for a paper.
What you get
A publication-ready regression table with coefficients, SEs, stars, FE rows, and fit statistics plus a Quarto embed shortcode.
- Publication-quality regression table
- Quarto embed shortcode
By the numbers
- Three significance star levels (p<0.10, p<0.05, p<0.01)
Files
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>.md6. 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.
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What does the regression-table skill do?
It formats estimation output from a notebook into a publication-quality regression table with coefficients, standard errors, significance stars, FE rows, and fit statistics.
What estimation output does it recognize?
Python statsmodels and linearmodels, R lm/fixest/felm, and Stata reg/reghdfe/ivregress output.