llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
npx skills add majiayu000/claude-skill-registry --skill llm-evaluation-jantoniofc-skillsbank --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 Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing. ## Do not use this skill when - The task is unrelated to llm evaluation - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Use this skill when - Measuring LLM application performance systematically - Comparing different models or prompts - Detecting performance regressions before deployment - Validating improvements from prompt changes - Building confidence in production systems - Establishing baselines and tracking progress over time - Debugging unexpected model behavior ## Core Evaluation Types ### 1. Automated Metrics Fast, repeatable, scalable evaluation using computed scores. **Text Generation:** - **BLEU**: N-gram overlap (translation) - **ROUGE**: Recall-oriented (summarization) - **METEOR**: Semantic similarity - **BERTScore**: Embedding-based similarity - **Perplexity**: Language
- Do not use this skill when
- Instructions
- Use this skill when
- Core Evaluation Types
- 1. Automated Metrics
- 2. Human Evaluation
- 3. LLM-as-Judge
- Quick Start
- Automated Metrics Implementation
- BLEU Score
- ROUGE Score
- BERTScore
- Custom Metrics
- LLM-as-Judge Patterns
What does the llm-evaluation skill do?
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-evaluation-jantoniofc-skillsbank --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 majiayu000/claude-skill-registry, a repository with 534 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.