Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
Quickly install evaluate-rag AI skill to your development environment via command line
Source: hamelsmu/evals-skills.
Complete error analysis on RAG pipeline traces before selecting metrics. Inspect what was retrieved vs. what the model needed. Determine whether the problem is retrieval, generation, or both. Fix retrieval first.
Measure each component independently. Use the appropriate metric for each retrieval stage:
Manual curation (highest quality): Write realistic questions and map each to the exact chunk(s) containing the answer.
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies. Source: hamelsmu/evals-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/hamelsmu/evals-skills --skill evaluate-ragGuides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies. Source: hamelsmu/evals-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/hamelsmu/evals-skills --skill evaluate-rag Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/hamelsmu/evals-skills