latte-review-guide
Automate systematic literature reviews with LatteReview AI agents
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill latte-review-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.
# LatteReview Guide ## Overview LatteReview is a low-code Python package that uses AI agents to automate systematic literature reviews. It handles title/abstract screening, full-text assessment, data extraction, and PRISMA-compliant reporting — tasks that typically consume hundreds of researcher-hours. Supports multiple LLM backends (Anthropic, OpenAI, local models). ## Installation ```bash pip in
What does the latte-review-guide skill do?
Automate systematic literature reviews with LatteReview AI agents
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill latte-review-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.