Can I use this for production debugging?

Yes, it includes patterns for py-spy which allows you to profile running Python processes and generate flamegraphs with minimal overhead in production.

When should I choose generators over lists?

Use generators when processing large datasets where you only need to iterate through items once; they use constant memory regardless of the data size, unlike lists.

What tools are used for profiling in this skill?

This skill covers cProfile for CPU profiling, memory_profiler for memory usage, line_profiler for line-by-line analysis, and py-spy for production environments.

How do I identify a memory leak in Python?

Use the memory profiling patterns provided to track allocation over time and identify objects that are not being garbage collected using the memory_profiler tool.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

開発者ツール

Profiles, analyzes, and optimizes Python code to eliminate bottlenecks and reduce resource consumption.

概要

This skill provides a comprehensive toolkit for identifying and fixing performance issues in Python applications. It guides users through various profiling techniques—including CPU, memory, and line-level analysis—using industry-standard tools like cProfile and memory-profiler. Beyond diagnostics, it offers proven implementation patterns for algorithmic improvements, caching strategies, and memory-efficient data structures, making it indispensable for developers building high-scale or latency-sensitive Python services.

主な機能

Production-safe profiling and flamegraph generation with py-spy
Numerical optimization techniques using NumPy and vectorized operations
0 GitHub stars
Advanced memory optimization using generators and __slots__
Performance-focused implementation patterns for strings, lists, and dicts
Deep profiling with cProfile, line_profiler, and memory_profiler

ユースケース

Reducing memory footprints and fixing memory leaks in long-running processes
Debugging slow response times in web applications or data pipelines
Optimizing CPU-intensive algorithms and numerical computations

概要

主な機能

Production-safe profiling and flamegraph generation with py-spy
Numerical optimization techniques using NumPy and vectorized operations
0 GitHub stars
Advanced memory optimization using generators and __slots__
Performance-focused implementation patterns for strings, lists, and dicts
Deep profiling with cProfile, line_profiler, and memory_profiler

ユースケース

Reducing memory footprints and fixing memory leaks in long-running processes
Debugging slow response times in web applications or data pipelines
Optimizing CPU-intensive algorithms and numerical computations