workflows:review
Run multi-agent econometric review on estimation code, identification arguments, and research artifacts
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-review --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.
# Review Command **Pipeline mode:** This command operates fully autonomously. All decisions are made automatically. Perform exhaustive econometric and methodological review using multi-agent parallel analysis. Domain-specific reviewers check estimation quality, identification strategy, numerical stability, and mathematical rigor. ## Input <review_target> #$ARGUMENTS </review_target> ## Execution W
What does the workflows:review skill do?
Run multi-agent econometric review on estimation code, identification arguments, and research artifacts
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-review --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.