topic-modeling
Structural topic modeling: STM spec, topic count, coherence-exclusivity.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill topic-modeling --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.
# Topic Modeling for Survey and Experimental Text Data ## Instructions ### 1. Model Selection - Default to Structural Topic Models (STM) when analyzing text from surveys or experiments. STM incorporates document-level metadata — treatment conditions, respondent demographics, country — directly into estimation, allowing prevalence and content to vary with covariates (Roberts et al. 2014). - Use sta
What does the topic-modeling skill do?
Structural topic modeling: STM spec, topic count, coherence-exclusivity.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill topic-modeling --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.