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.
Install
mkdir -p .claude/skills/llm-evaluation-h4d3zs && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13370" && unzip -o skill.zip -d .claude/skills/llm-evaluation-h4d3zs && rm skill.zipInstalls to .claude/skills/llm-evaluation-h4d3zs
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.232 chars✓ has a “when” trigger
About this skill
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