Agentic Eval
Agentic Eval assesses an agent’s performance across multiple runs, identifying strengths and weaknesses for improved efficiency and reliability.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add agentic-eval npx -- -y @trustedskills/agentic-eval
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"agentic-eval": {
"command": "npx",
"args": [
"-y",
"@trustedskills/agentic-eval"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The agentic-eval skill enables AI agents to evaluate their own performance or that of other agents through structured, goal-oriented assessments. It supports defining evaluation criteria, executing test scenarios, and providing feedback based on predefined metrics.
When to use it
- To assess the accuracy and reliability of an agent's responses in a controlled environment.
- When developing and refining AI agents for complex tasks like coding or content generation.
- For benchmarking multiple agents against each other using standardized tests.
- During quality assurance phases to ensure agents meet performance expectations.
Key capabilities
- Customizable evaluation frameworks tailored to specific use cases.
- Integration with test scenarios that simulate real-world agent interactions.
- Automated feedback generation based on predefined success metrics.
Example prompts
- "Evaluate the accuracy of this AI agent's code suggestions against a set of known solutions."
- "Run a performance assessment for the agent using the provided benchmark dataset."
- "Compare the response quality of two agents using the evaluation framework defined in the prompt."
Tips & gotchas
- Ensure that evaluation criteria are clearly defined to avoid ambiguous results.
- This skill may require additional configuration or integration with testing tools for advanced use cases.
Tags
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