Agent Evaluation

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by supercent-io · vlatest · Repository

Supercent-io's agent-evaluation assesses agent performance against defined metrics, providing actionable insights for improvement.

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add agent-evaluation npx -- -y @trustedskills/agent-evaluation
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "agent-evaluation": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/agent-evaluation"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

What it does

The agent-evaluation skill enables users to assess and compare AI agents based on predefined criteria such as performance, accuracy, efficiency, and user feedback. It provides structured evaluation frameworks that help identify strengths and weaknesses of different agents in specific use cases.

When to use it

  • Evaluating multiple AI agents for a project to determine the best fit
  • Comparing agent outputs against ground truth data or benchmarks
  • Gathering user feedback on agent performance in real-world scenarios

Key capabilities

  • Structured evaluation frameworks
  • Performance benchmarking
  • User feedback analysis
  • Comparative agent scoring

Example prompts

  • "Compare Agent A and Agent B based on response accuracy and speed."
  • "Evaluate this AI agent's performance using the latest dataset."
  • "Generate a report comparing three agents in terms of user satisfaction and task completion rates."

Tips & gotchas

  • Ensure all agents being evaluated are trained on similar datasets for fair comparison.
  • Use consistent metrics across evaluations to maintain reliability and avoid bias.

Tags

🛡️

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Details

Version
vlatest
License
Author
supercent-io
Installs
6.4k

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Passed automated security scans.