Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install brycewang-stanford-awesome-agent-skills-for-empirical-research-skills-29-quarcs-lab-project20xxy-dot-claude-skills-robustness-tgit clone https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research.gitcp Awesome-Agent-Skills-for-Empirical-Research/SKILL.MD ~/.claude/skills/brycewang-stanford-awesome-agent-skills-for-empirical-research-skills-29-quarcs-lab-project20xxy-dot-claude-skills-robustness-t/SKILL.md---
name: robustness-table
description: Generates robustness check code and formats results as a combined table. Use for sensitivity analysis.
argument-hint: <notebook> <baseline spec>
allowed-tools: Bash, Read, Write, Edit, Glob, Grep
---
# 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 be self-contained (loads data, runs regression, stores results)
- Use consistent variable naming for results collection
4. Create a summary cell that collects all results into a single table:
- Cell directive: `#| label: tbl-robustness` (or `*|` for Stata)
- Cell directive: `#| tbl-cap: "Robustness checks"` (or `*|` for Stata)
- Format: baseline in column (1), each robustness check in subsequent columns
- Follow academic conventions: coefficient (SE), significance stars, N, R², FE indicators
- **Stata caveat:** Do NOT use `tbl-` prefix for Stata text output — use a plain label (e.g., `stata-robustness`)
5. Optionally export the table to `tables/` as a standalone file (LaTeX or CSV)
6. Sync the Jupytext pair:
```bash
uv run jupytext --sync notebooks/<name>.md
```
7. Show the embed shortcode for `index.qmd`:
```
{{< embed notebooks/<name>.ipynb#tbl-robustness >}}
```
## Error handling
- If the baseline regression is not found, ask the user to point to the specific cell.
- If the notebook uses a language not recognized, ask for guidance on the estimation syntax.