panel-data-guide
Panel data analysis with fixed and random effects models
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill panel-data-guide --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.
# Panel Data Analysis Guide Estimate and interpret fixed effects, random effects, and dynamic panel models using Stata, R, and Python for longitudinal/panel datasets. ## What Is Panel Data? Panel data (also called longitudinal or cross-sectional time-series data) tracks the same units (individuals, firms, countries) across multiple time periods. This structure enables: - Controlling for unobserved
What does the panel-data-guide skill do?
Panel data analysis with fixed and random effects models
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill panel-data-guide --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.