Workers Performance
Analyze worker task completion rates, identify bottlenecks, and suggest improvements to boost overall team productivity.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add workers-performance npx -- -y @trustedskills/workers-performance
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"workers-performance": {
"command": "npx",
"args": [
"-y",
"@trustedskills/workers-performance"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill enables AI agents to execute performance-related tasks within a Workers environment, likely optimizing runtime efficiency or managing worker lifecycle events. It provides specialized functionality for enhancing the operational speed and resource utilization of serverless functions.
When to use it
- Optimizing cold start times for high-frequency API endpoints.
- Managing dynamic scaling thresholds based on real-time traffic loads.
- Tuning memory limits to reduce garbage collection overhead.
- Implementing custom metrics to monitor worker throughput and latency.
Key capabilities
- Runtime performance optimization features.
- Worker lifecycle management tools.
- Resource allocation tuning mechanisms.
- Efficiency monitoring utilities.
Example prompts
- "Optimize the cold start time for my image processing worker."
- "Adjust memory limits to reduce garbage collection pauses during peak traffic."
- "Set up custom metrics to track worker throughput and latency in real-time."
Tips & gotchas
Ensure your Workers environment has sufficient resources allocated before applying performance optimizations, as aggressive tuning may impact stability. Monitor system logs closely after changes to verify that efficiency gains do not introduce new bottlenecks.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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