Agent Performance Optimizer
Ruvnet's agent-performance-optimizer dynamically adjusts agent parameters to maximize efficiency and task completion rates.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add agent-performance-optimizer npx -- -y @trustedskills/agent-performance-optimizer
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"agent-performance-optimizer": {
"command": "npx",
"args": [
"-y",
"@trustedskills/agent-performance-optimizer"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The Agent Performance Optimizer skill analyzes agent interactions and suggests improvements to enhance efficiency and output quality. It identifies areas where an agent might be struggling, such as repetitive tasks or inefficient prompt structures. The goal is to refine agent behavior through targeted adjustments based on observed performance data.
When to use it
- Debugging slow agents: If an agent takes too long to complete tasks, this skill can help pinpoint bottlenecks and suggest optimizations.
- Improving output quality: Identify patterns in low-quality outputs and receive recommendations for prompt refinement or task decomposition.
- Reducing repetitive actions: Discover redundant steps within an agent's workflow and streamline processes for increased efficiency.
- Optimizing complex workflows: When dealing with multi-step agents, this skill can help analyze each stage to ensure optimal performance.
Key capabilities
- Interaction analysis
- Performance bottleneck identification
- Prompt refinement suggestions
- Workflow optimization recommendations
Example prompts
- "Analyze the last 10 interactions of my agent and suggest improvements."
- "Identify any repetitive actions in this agent's workflow."
- "How can I improve the quality of responses from my agent when it’s summarizing documents?"
Tips & gotchas
This skill requires access to the agent's interaction history for analysis. Results are most effective with agents performing complex or multi-step tasks, as simpler agents may not reveal significant optimization opportunities.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
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| Snyk | Pass |
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