Agent Tuning
Refine agent behavior through iterative feedback loops, optimizing performance based on your specified goals and metrics.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add agent-tuning npx -- -y @trustedskills/agent-tuning
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"agent-tuning": {
"command": "npx",
"args": [
"-y",
"@trustedskills/agent-tuning"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill allows you to fine-tune the behavior of AI agents. It provides a mechanism for adjusting agent responses and actions based on specific instructions or examples, improving their performance in targeted scenarios. The goal is to make agents more reliable and aligned with desired outcomes through iterative adjustments.
When to use it
- Improving response quality: When an agent consistently gives inaccurate or unhelpful answers.
- Refining task execution: If an agent struggles to complete a specific task correctly, tuning can guide its actions.
- Adjusting tone and style: To modify the way an agent communicates (e.g., more formal, friendly, concise).
- Correcting biases or errors: When an agent exhibits undesirable behavior or makes systematic mistakes.
Key capabilities
- Response adjustment based on provided examples
- Action refinement through iterative feedback loops
- Tone and style modification
- Error correction and bias mitigation
Example prompts
- "Tune the agent to respond with more concise answers."
- "Provide an example of a good response for this query: [user query]"
- "Correct the agent's tendency to hallucinate information when asked about [topic]."
Tips & gotchas
The effectiveness of agent tuning depends on providing clear and specific feedback. Start with small adjustments and carefully monitor the results to avoid unintended consequences.
Tags
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