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python-performance-optimization

Profile CPU and memory usage to identify and fix Python performance bottlenecks.

6|2|Updated Oct 19, 2025
Search Tags:#performance#debugging#python#optimization#profiling#cProfile#memory_profiler
One-click install
npx skills add https://github.com/amurata/cc-tools --skill python-performance-optimization-amurata
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-performance-optimization
Source: https://github.com/amurata/cc-tools/tree/main/plugins/python-development/skills/python-performance-optimization
Command: npx skills add https://github.com/amurata/cc-tools --skill python-performance-optimization-amurata

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires cProfile, memory_profiler, line_profiler, py-spy, numpy, pytest-benchmark, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you identify and fix performance bottlenecks in your Python code, making your applications faster and more resource-efficient.

Core Features & Use Cases

  • CPU Profiling: Pinpoint functions consuming the most execution time.
  • Memory Profiling: Detect and resolve memory leaks and high usage.
  • Optimization Strategies: Learn and apply techniques like list comprehensions, generators, NumPy, and caching.
  • Use Case: Your Python script is taking too long to run. Use this Skill to profile it, find the slow parts, and apply optimizations to significantly reduce its execution time.

Quick Start

Use the python-performance-optimization skill to profile the execution time of the slow_function in the provided Python script.

Frequently Asked Questions about python-performance-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find bottlenecks in slow Python code?▼

To identify Python performance bottlenecks, you can profile CPU execution time using cProfile and line_profiler to pinpoint the exact functions and lines consuming the most resources.

What is the best way to detect memory leaks in a Python application?▼

Detecting memory leaks in Python requires profiling memory usage with memory_profiler to monitor memory consumption line-by-line and locate high usage areas for resolution.

How do I profile a specific Python function for execution time?▼

You can profile a specific Python function by running it through cProfile or py-spy to measure execution time, and then apply strategies like list comprehensions or caching to optimize it.

Does NumPy help reduce Python script latency?▼

NumPy reduces Python script latency by replacing standard loops with optimized array operations, significantly improving computational speed and overall resource utilization.

Can I use pytest-benchmark to measure Python optimization results?▼

Yes, you can use pytest-benchmark to measure and validate Python optimization results by comparing execution metrics before and after applying performance improvements.

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