python-performance-profiler

Profile Python applications for CPU and memory bottlenecks using cProfile and memory_profiler.

2|1|Updated Jan 25, 2026

Search Tags:#optimization #memory #profiling #Python #CPU #analyze

Ready to run

npx skills add https://github.com/jorgealves/agent_skills --skill python-performance-profiler

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-performance-profiler
Source: https://github.com/jorgealves/agent_skills/tree/main/python-performance-profiler
Command: npx skills add https://github.com/jorgealves/agent_skills --skill python-performance-profiler

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps identify CPU and memory bottlenecks in Python code by leveraging cProfile and memory_profiler, enabling targeted optimizations for mission-critical services.

Core Features & Use Cases

CPU profiling with cProfile to pinpoint hot code paths.
Memory profiling with memory_profiler to track memory growth and leaks.
Use Case: Optimize performance for mission-critical Python services across development, testing, and production environments.

Quick Start

Run a quick profiling session on a Python script to generate CPU and memory reports, e.g.: python -m cProfile -o profile.out your_script.py and python -m memory_profiler your_script.py.

Frequently Asked Questions about python-performance-profiler

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

FAQPage Schema

How do I profile Python CPU bottlenecks in a mission-critical script?▼

Profile Python CPU bottlenecks using cProfile to generate execution reports and pinpoint hot code paths. Run a command like python -m cProfile -o profile.out your_script.py during development or testing to identify performance hotspots.

How do I find memory leaks in a Python application?▼

Find memory leaks in a Python application by running memory_profiler to track memory growth over execution. This identifies specific lines causing memory spikes, enabling targeted optimizations for Python services.

Can I use cProfile and memory_profiler to debug Python performance in production?▼

Yes, you can use cProfile and memory_profiler to debug Python performance in production. The Skill operates locally without transmitting data externally, making it safe to locate CPU and memory hotspots in mission-critical environments.

Do I need a specific Python environment to analyze CPU and memory usage?▼

Yes, you need a local Python environment with cProfile and memory_profiler installed to analyze CPU and memory usage. No external data transmission occurs, ensuring secure local profiling across development and testing stages.

What is the best way to identify Python code hotspots for optimization?▼

The best way to identify Python code hotspots for optimization is combining cProfile for CPU execution paths and memory_profiler for memory allocation tracking. This dual approach provides comprehensive bottleneck identification for targeted service improvements.

Why does my Python service have high CPU usage and how can I locate the bottleneck?▼

High CPU usage in Python services often stems from inefficient code loops or functions. Locate the exact bottleneck by profiling with cProfile to map execution time across hot code paths, enabling targeted optimization.