agent-evaluation

SKILL.md

Evaluation of agent systems requires different approaches than traditional software or even standard language model applications. Agents make dynamic decisions, are non-deterministic between runs, and often lack single correct answers. Effective evaluation must account for these characteristics while providing actionable feedback. A robust evaluation framework enables continuous improvement, catches regressions, a...

Agent evaluation requires outcome-focused approaches that account for non-determinism and multiple valid paths. Multi-dimensional rubrics capture various quality aspects: factual accuracy, completeness, citation accuracy, source quality, and tool efficiency. LLM-as-judge provides scalable evaluation while human evaluation catches edge cases.

The key insight is that agents may find alternative paths to goals—the evaluation should judge whether they achieve right outcomes while following reasonable processes.

Claude Code 명령, 기술 및 에이전트를 평가하고 개선합니다. 즉각적인 효과를 테스트하거나, 컨텍스트 엔지니어링 선택을 검증하거나, 개선 품질을 측정할 때 사용합니다. 출처: zpankz/mcp-skillset.

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