pyfixest-reference
Dense, machine-readable API reference for PyFixest — high-dimensional fixed-effects OLS/WLS/IV and Poisson (feols, fepois, feglm), clustered/robust standard errors, R-style formula syntax, and post-estimation. Use when writing or debugging Python fixed-effects regressions with the pyfixest package.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 40-py-econometrics-pyfixest --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.
# PyFixest LLM Skill Reference > Dense, machine-readable reference for LLMs. No prose padding. > Version: matches latest PyFixest release. ## Package Import ```python import pyfixest as pf ``` ## Core Estimation Functions ### pf.feols() — OLS / WLS / IV with Fixed Effects ```python pf.feols( fml: str, # Formula: "Y ~ X1 + X2 | fe1 + fe2" or IV: "Y ~ exog | fe | endog ~ inst" data: pd.DataFrame, vc
What does the pyfixest-reference skill do?
Dense, machine-readable API reference for PyFixest — high-dimensional fixed-effects OLS/WLS/IV and Poisson (feols, fepois, feglm), clustered/robust standard errors, R-style formula syntax, and post-estimation. Use when writing or debugging Python fixed-effects regressions with the pyfixest package.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 40-py-econometrics-pyfixest --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.