robustness-table
Generates robustness check code and formats results as a combined table. Use for sensitivity analysis.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill robustness-table --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Generate Robustness Checks and Table Given a baseline regression, generate code for standard robustness checks and format the results as a publication-ready table. ## Arguments - `$ARGUMENTS` — notebook reference and baseline specification description (e.g., "notebook-02 baseline OLS with GDP on life expectancy") ## Steps 1. Read the specified notebook and locate the baseline regression: - Look for estimation commands (Python: `statsmodels`, `linearmodels`; R: `lm`, `fixest`, `felm`; Stata: `reg`, `reghdfe`, `ivregress`) - Identify the dependent variable, independent variables, fixed effects, and clustering 2. Ask the user which robustness checks to include: - Alternative control variable sets (drop/add controls) - Alternative fixed effects specifications - Different standard error clustering levels - Subsample analysis (e.g., by region, time period, income group) - Winsorized or trimmed dependent variable - Alternative dependent variable (e.g., log vs level) - Placebo tests (randomized treatment, pre-period outcome) - Alternative estimation methods (e.g., OLS vs Poisson, logit vs probit) 3. Generate code cells in the notebook for each robustness specification: - Each cell should
- Arguments
- Steps
- Error handling
uv run jupytext --sync notebooks/<name>.md
What does the robustness-table skill do?
Generates robustness check code and formats results as a combined table. Use for sensitivity analysis.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill robustness-table --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.