Is this useful for Data Science performance?

Absolutely. It covers advanced numerical optimization techniques using NumPy vectorization and caching strategies that are essential for high-performance data science workflows.

How does this skill help identify Python bottlenecks?

It provides specialized guidance on using profiling tools like cProfile to identify slow functions and line_profiler to pinpoint specific lines of code causing delays.

Can this skill help with Python memory leaks?

Yes, it includes patterns for memory profiling and suggests memory-efficient structures like generators and __slots__ to significantly reduce memory footprints.

Does it support profiling live production applications?

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

What is the difference between list and generator approaches in this skill?

The skill demonstrates how generators provide constant memory usage regardless of data size, which is critical for processing large datasets that would otherwise cause memory errors.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: HermeticOrmus

byHermeticOrmus

•

開発者ツール

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory usage, and improve execution speed using industry-standard tools.

This skill empowers Claude to analyze Python applications for performance issues using professional profiling tools and implementation best practices. It provides actionable guidance on identifying CPU and memory bottlenecks, implementing efficient data structures, and applying advanced optimization patterns like caching, vectorization with NumPy, and memory-efficient class structures. It is particularly useful for developers looking to scale Python applications, reduce latency in data pipelines, or debug complex memory leaks in production environments.

主な機能

01Comprehensive CPU profiling using cProfile and line_profiler

02Memory usage analysis and leak detection with memory_profiler

03High-performance coding patterns for loops, strings, and lookups

04Production-grade profiling with py-spy for running processes

05Advanced caching strategies using functools.lru_cache

062 GitHub stars

ユースケース

01Reducing response times and latency in Python-based web APIs

02Optimizing memory-intensive data processing and ETL pipelines

03Identifying and fixing performance regressions in legacy Python codebases

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/libreuiux-claude-code python-performance-optimization

For use in Claude.ai and ChatGPT

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