text-classification
LLM-based text classification: codebook, validation, agreement statistics.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill text-classification --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.
# LLM-Based Text Classification for Social Science Research ## Instructions ### 1. Codebook Design - Before drafting the codebook, specify the population, sampling frame, and (for experimental data) the treatment condition each response is drawn from. These constrain which categories can plausibly exist and which demographic subgroups any bias assessment must cover. LLM classification extends, rat
What does the text-classification skill do?
LLM-based text classification: codebook, validation, agreement statistics.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill text-classification --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.