Llm_Evaluation
Assess LLM outputs based on provided criteria like accuracy, relevance, and safety, generating detailed feedback reports.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add llm_evaluation npx -- -y @trustedskills/llm_evaluation
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"llm_evaluation": {
"command": "npx",
"args": [
"-y",
"@trustedskills/llm_evaluation"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill allows an AI agent to evaluate the output of other language models (LLMs). It can assess responses based on criteria like helpfulness, accuracy, and relevance. The evaluation process provides a structured feedback mechanism for improving LLM performance in various applications.
When to use it
- Automated Feedback Loops: Integrate into workflows where continuous improvement of an LLM is needed, such as chatbot training or content generation pipelines.
- A/B Testing: Compare the quality of responses from different LLMs or prompt variations.
- Quality Assurance: Regularly check the output of an LLM to ensure it meets predefined standards and identify potential issues.
Key capabilities
- LLM Output Evaluation
- Helpfulness Assessment
- Accuracy Verification
- Relevance Scoring
Example prompts
- "Evaluate this response: [insert LLM response here] based on helpfulness, accuracy, and relevance."
- "Score the following text for its adherence to a professional tone: [insert LLM generated text]."
- "Compare these two responses and tell me which is better and why: [response 1], [response 2]"
Tips & gotchas
The effectiveness of this skill depends on clear evaluation criteria. Providing specific guidelines or examples will improve the quality of the assessment.
Tags
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Security Audits
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