new-analysis
Scaffolds a method-specific analysis notebook (DiD, IV, RDD, LASSO, Panel FE) with boilerplate. Use when starting a new econometric analysis.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill new-analysis --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.
# Scaffold Analysis Notebook Create a new notebook pre-populated with method-specific boilerplate for a common econometric technique. ## Arguments - `$ARGUMENTS` — the method name and optional title (e.g., "DiD Event Study", "IV Analysis of Colonial Origins", "RDD Minimum Wage", "LASSO Variable Selection", "Panel FE Growth Regressions") ## Steps 1. Parse the method from the arguments. Recognized methods: - **DiD** (difference-in-differences) - **IV** (instrumental variables) - **RDD** (regression discontinuity design) - **LASSO** (regularized regression / variable selection) - **Panel FE** (panel fixed effects) - If the method is not recognized, ask the user to clarify. 2. Follow the same notebook creation conventions as `/project:new-notebook`: - Check `notebooks/` for existing files to determine the next sequential number - Ask the user for the kernel: Python, R, or Stata - Create the `.ipynb` with the appropriate kernel and setup cell: - **Python:** `import sys; sys.path.insert(0, ".."); from config import set_seeds, DATA_DIR; set_seeds()` - **R:** `source("../config.R"); set_seeds()` - **Stata:** `clear all` followed by `set seed 42` 3. Add method-specific sections as markdown
- Arguments
- Steps
uv run jupytext --set-formats ipynb,md:myst notebooks/<name>.ipynb
What does the new-analysis skill do?
Scaffolds a method-specific analysis notebook (DiD, IV, RDD, LASSO, Panel FE) with boilerplate. Use when starting a new econometric analysis.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill new-analysis --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.