Agent Evaluation
Evaluates agent performance based on defined metrics, providing actionable feedback for improvement and optimization.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add davila7-agent-evaluation npx -- -y @trustedskills/davila7-agent-evaluation
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"davila7-agent-evaluation": {
"command": "npx",
"args": [
"-y",
"@trustedskills/davila7-agent-evaluation"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The davila7-agent-evaluation skill provides tools for assessing and benchmarking AI agents, including performance metrics, task completion analysis, and feedback generation. It enables users to evaluate how well an agent performs in specific scenarios or tasks.
When to use it
- To measure the effectiveness of an AI agent after deployment.
- When comparing multiple agents for a particular use case.
- During development to identify areas where an agent needs improvement.
- Before integrating an agent into a production environment to ensure reliability.
Key capabilities
- Performance benchmarking across different tasks
- Task completion analysis with detailed reports
- Feedback generation for iterative improvements
Example prompts
- "Evaluate the performance of this AI agent on customer support queries."
- "Generate a report comparing two agents based on their task accuracy."
- "Provide feedback to improve an agent's response time and quality."
Tips & gotchas
- Ensure that evaluation tasks are well-defined and representative of real-world scenarios for accurate results.
- The skill may require access to historical interaction data for comprehensive analysis.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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Passed automated security scans.