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.
Profile →npx skills add wshobson/agents --skill llm-evaluation --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. ## When to Use This Skill - 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 model confidence **Classification:** - **Accuracy**: Percentage correct - **Precision/Recall/F1**: Class-specific performance - **Confusion Matrix**: Error patterns - **AUC-ROC**: Ranking quality **Retrieval (RAG):** - **MRR**: Mean Reciprocal Rank - **NDCG**: Normalized Discounted Cumulative Gain - **Precision@K**: Relevant in top K - **Recall@K**: Coverage in top K ### 2
- When to Use This Skill
- Core Evaluation Types
- 1. Automated Metrics
- 2. Human Evaluation
- 3. LLM-as-Judge
- Quick Start
- Detailed patterns and worked examples
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 wshobson/agents --skill llm-evaluation --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 wshobson/agents, a repository with 38,479 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.