llm-evaluation-guide
Evaluate and benchmark large language models for research applications
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill llm-evaluation-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.
# LLM Evaluation Guide A skill for evaluating and benchmarking large language models (LLMs) in research settings. Covers automatic metrics, human evaluation protocols, benchmark suites, evaluation pitfalls, and best practices for reporting LLM performance. ## Evaluation Taxonomy ### Types of Evaluation ``` 1. Intrinsic evaluation: Measures model quality on its own terms - Perplexity, likelihood, c
What does the llm-evaluation-guide skill do?
Evaluate and benchmark large language models for research applications
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill llm-evaluation-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.