From cpu-usage-monitor
Analyzes code for CPU hotspots, algorithmic complexity, blocking operations, and inefficiencies, delivering optimization recommendations for better performance.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cpu-usage-monitor:monitoring-cpu-usageThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Identify CPU hotspots, analyze algorithmic complexity, and detect blocking operations in application code with targeted optimization recommendations.
Identify CPU hotspots, analyze algorithmic complexity, and detect blocking operations in application code with targeted optimization recommendations.
This skill empowers Claude to analyze code for CPU-intensive operations, offering detailed optimization recommendations to improve processor utilization. By pinpointing areas of high CPU usage, it facilitates targeted improvements for enhanced application performance.
cpu-usage-monitor plugin.This skill activates when you need to:
User request: "Monitor CPU usage in my Python script and suggest optimizations."
The skill will:
User request: "Analyze the CPU load of this Java code and identify areas with high algorithmic complexity."
The skill will:
This skill can be used in conjunction with other code analysis and refactoring tools to implement the suggested optimizations. It can also be integrated into CI/CD pipelines to automatically monitor CPU usage and identify performance regressions.
The skill produces structured output relevant to the task.
5plugins reuse this skill
First indexed Jul 10, 2026
npx claudepluginhub fleet-to-force/claude-code-plugins-plus --plugin cpu-usage-monitorAnalyzes code for CPU hotspots, algorithmic complexity, and blocking operations, providing optimization recommendations to reduce processor load.
Profiles application performance across Node.js, Python, and Java stacks by analyzing CPU, memory, and execution hotspots to identify optimization targets.
Profiles and optimizes Python code using cProfile, memory profilers, and best practices for CPU, memory, I/O, and database performance.