Agent Evaluation
neolabhq/context-engineering-kitEvaluating AI agents is different from testing regular software. Agents make their own decisions and can take many paths to a goal. This skill explains how to judge their performance using outcome-focused methods.
Key ideas include multi-dimensional rubrics that score accuracy, completeness, and tool use. The 95% finding shows token usage drives most performance, followed by tool calls and model choice.
You will learn to handle non-determinism and build evaluation that catches regressions. Use LLM-as-judge and human evaluation for best results.
Add Agent Evaluation skill to your workflow
Global
mkdir -p ~/.claude/skills/agent-evaluationProject
mkdir -p .claude/skills/agent-evaluationSource Repository
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