Implement comprehensive evaluation strategies for LLM applications using automated metrics, LLM-as-judge, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, comparing prompts/models, or establishing evaluation frameworks. Covers RAGAS for RAG pipelines, evals-as-code CI/CD integration, and modern 2025/2026 practices including structured output evaluation and agentic task success measurement.
How to install llm-evaluation?
ckorhonen/claude-skills/llm-evaluation
$npx -y skills add ckorhonen/claude-skills --skill llm-evaluationInstalls into the current project.
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Run `npx skills use "https://github.com/ckorhonen/claude-skills" --skill "ckorhonen/claude-skills/llm-evaluation"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/ckorhonen/claude-skills" that are relevant to the current task. Run `npx skills add "https://github.com/ckorhonen/claude-skills"` and select the relevant skills, then follow their instructions.
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