What tools does this skill support for Python profiling?

This skill provides patterns for cProfile (CPU), memory_profiler (RAM), line_profiler (line-by-line), and py-spy for sampling production processes.

When should I use NumPy vs pure Python for performance?

This skill demonstrates that NumPy is significantly faster for numerical operations and large-scale data manipulation due to its vectorized operations and C-based backend.

Can this skill help with production performance issues?

Yes, it includes instructions for using py-spy, which allows you to profile a running Python process without restarting the application or modifying code.

How can I reduce Python memory usage using this skill?

The skill suggests using generator expressions instead of list comprehensions and implementing __slots__ in classes to significantly reduce object overhead.

Python Performance Optimization

Name: Python Performance Optimization
Author: gwickman

bygwickman

0•

分析と監視

Optimizes Python applications by identifying bottlenecks through comprehensive profiling and implementing high-efficiency coding patterns.

This skill provides a specialized framework for Claude to diagnose and resolve performance issues in Python codebases. It enables sophisticated CPU and memory profiling using tools like cProfile and line_profiler, while offering actionable optimization strategies such as memory-efficient generators, optimized string handling, and vectorization with NumPy. It is an essential resource for developers looking to reduce application latency, minimize memory footprints, and scale Python applications effectively by moving from slow, iterative logic to high-performance implementation patterns.

主な機能

01Comprehensive CPU profiling using cProfile and line-by-line analysis tools

02Algorithmic optimizations for faster lookups and reduced function overhead

030 GitHub stars

04Memory usage tracking and leak detection with memory_profiler and __slots__

05Production-level profiling using py-spy for running Python processes

06Implementation of memory-efficient data structures like generator expressions

ユースケース

01Optimizing API response times by reducing function call overhead and improving string handling

02Debugging and resolving high memory consumption in long-running Python services

03Refactoring slow data processing pipelines to improve throughput and execution speed

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gwickman/auto-dev-test-target-1 python-performance-optimization

For use in Claude.ai and ChatGPT

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