Evaluation

This is about building evaluation frameworks for agent systems where the normal rules don't apply. The core insight here is that agents are non-deterministic and can take completely different valid paths to the same goal, so you need outcome-focused rubrics instead of checking specific steps. There's a notable finding from BrowseComp research: token usage explains 80% of performance variance, which means your evaluation needs realistic token budgets, not unlimited resources. The framework covers LLM-as-judge for scale, human evaluation for edge cases, and multi-dimensional scoring across accuracy, completeness, and tool efficiency. Use this when you need systematic testing before shipping changes or want to catch regressions in production agent systems.

skills.sh installs: 189
GitHub stars: 39.4K
last updated: Jun 3, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill evaluation --agent claude-code

Installs into .claude/skills of the current project.

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When to Use This Skill

Build evaluation frameworks for agent systems

Use this skill when working with build evaluation frameworks for agent systems.

Evaluation Methods for Agent Systems

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, and validates that context engineering choices achieve intended effects.