Generate Robustness Checks and Table
SkillDev toolsGenerates robustness check code and formats results as a combined table. Use for sensitivity analysis.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Generate Robustness Checks and Table skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/29-quarcs-lab-project20XXy/dot-claude/skills/robustness-table/SKILL.md and read by ahel’s review.
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
-
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
- Look for estimation commands (Python:
-
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)
-
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
-
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)
- Cell directive:
-
Optionally export the table to
tables/as a standalone file (LaTeX or CSV) -
Sync the Jupytext pair:
uv run jupytext --sync notebooks/<name>.md -
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.
Signals
- GitHub stars
- 4k
- Forks
- 531
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
robustness-table- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
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