performance

Profile Python CPU and memory hotspots using cProfile and tracemalloc.

3|Updated May 12, 2026

Search Tags:#performance #python #optimization #concurrency #profiling #asyncio

Ready to run

npx skills add https://github.com/gao-hongnan/omniagents --skill performance-gao-hongnan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: performance
Source: https://github.com/gao-hongnan/omniagents/tree/main/plugins/python/skills/performance
Command: npx skills add https://github.com/gao-hongnan/omniagents --skill performance-gao-hongnan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses performance bottlenecks in Python applications, such as memory leaks, high CPU usage, and inefficient concurrency, by providing standardized patterns for profiling and optimization.

Core Features & Use Cases

Performance Profiling: Provides standardized implementations for cProfile and tracemalloc to identify CPU and memory hotspots.
Concurrency & Backpressure: Offers robust patterns for bounded async task execution and queue management to prevent system starvation.
Resource Management: Includes templates for TTL caching, streaming large files, and efficient logging to ensure long-term application stability.

Quick Start

Use the performance skill to profile the current Python script and identify memory allocation hotspots using tracemalloc.

Frequently Asked Questions about performance

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

FAQPage Schema

How do I identify memory allocation hotspots in Python?▼

To identify memory allocation hotspots in Python, you can use the tracemalloc standard library module to trace memory blocks allocated and track memory usage back to specific lines of code.

How do I prevent system starvation during high-scale async task orchestration in Python?▼

Prevent system starvation in Python async task orchestration by implementing bounded async task execution and queue management patterns, which apply backpressure to control concurrent workloads.

What is the best way to profile CPU usage in Python applications?▼

The best way to profile CPU usage in Python applications is using the cProfile standard library module, which measures execution time across function calls to isolate performance bottlenecks.

Does this Python optimization skill require external dependencies for resource management?▼

No, this Python optimization skill requires zero external dependencies, relying entirely on standard library modules including asyncio, tracemalloc, and cProfile for production-grade resource management.

How do I manage long-term application stability when streaming large files in Python?▼

Manage long-term application stability when streaming large files in Python by utilizing resource management templates for TTL caching and efficient logging to prevent memory exhaustion.