agent-evaluation
๐ฏSkillfrom supercent-io/skills-template
|Evaluates AI agent performance, capabilities, and effectiveness through systematic assessment and scoring methodologies.
Overview
agent-evaluation is an agent development skill from the Supercent Agent Skills collection (71 skills) that provides systematic frameworks for evaluating AI agent performance, capabilities, and effectiveness. It covers assessment methodologies, scoring systems, and benchmarking approaches for multi-agent environments.
Key Features
- Systematic Assessment Frameworks: Structured methodologies for evaluating agent performance across accuracy, efficiency, and task completion metrics
- Scoring & Benchmarking: Quantitative scoring systems for comparing agent capabilities and tracking performance over time
- Multi-Agent Evaluation: Patterns for evaluating agents in orchestrated workflows (omc teams, ralph loops, jeo pipelines)
- Cross-Platform Support: Works across all AI agent platforms (Claude Code, Gemini CLI, Codex CLI, Cursor, Windsurf, OpenCode)
- TOON Format Integration: Compressed skill context auto-injected into prompts for evaluation guidance during agent development
Who is this for?
- AI agent developers who need systematic approaches to measure and improve agent performance across tasks
- Teams building multi-agent systems who need benchmarking frameworks to compare different agent configurations and models
- Engineering leads evaluating AI agent effectiveness for adoption decisions and need structured assessment criteria
Same repository
supercent-io/skills-template(102 items)
Installation
npx vibeindex add supercent-io/skills-template --skill agent-evaluationnpx skills add supercent-io/skills-template --skill agent-evaluation~/.claude/skills/agent-evaluation/SKILL.mdSKILL.md
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