How do I identify the slowest part of my Python script?

This skill utilizes cProfile and pstats to generate a detailed breakdown of function execution times, allowing you to target the specific code blocks causing bottlenecks.

Does it support profiling Python in production?

It includes guidance for using non-intrusive tools like py-spy, which can profile running Python processes and generate flamegraphs with minimal overhead.

When should I use the __slots__ pattern?

The skill recommends using __slots__ when you have millions of small object instances, as it prevents the creation of __dict__ and significantly reduces per-object memory overhead.

Is this skill helpful for Data Science performance?

Absolutely. It covers NumPy vectorization, generator expressions for large datasets, and memory-efficient data structures to optimize data-heavy workflows.

Can this skill help with Python memory leaks?

Yes, it provides implementation patterns for memory_profiler to track memory allocation over time and identify specific objects causing leaks or high consumption.

Python Performance Optimization

Name: Python Performance Optimization
Author: drgaciw

bydrgaciw

分析与监控

Optimizes Python application performance through advanced profiling, memory management, and efficient coding patterns.

关于

This skill equips Claude with specialized knowledge to identify and resolve performance bottlenecks in Python applications. It provides comprehensive guidance on profiling techniques using tools like cProfile, memory_profiler, and py-spy to analyze CPU and memory usage. Users can leverage this skill to implement high-performance patterns such as generator expressions, LRU caching, NumPy vectorization, and slotted classes. Whether you are debugging a slow API, reducing the memory footprint of a data pipeline, or optimizing production algorithms, this skill offers the specific implementation patterns needed to scale Python code effectively.

主要功能

Implementation of high-performance patterns like LRU caching and __slots__
0 GitHub stars
Memory usage analysis and leak detection with memory_profiler
CPU profiling and bottleneck identification using cProfile and pstats
Line-by-line performance benchmarking for granular code optimization
Integration of vectorized operations using NumPy for numerical efficiency

使用场景

Reducing memory consumption in large-scale data processing pipelines
Debugging and resolving slow response times in Python web backends
Optimizing compute-intensive algorithms for production-grade performance

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add drgaciw/academic-compliance-hub-glm python-performance-optimization

For use in Claude.ai and ChatGPT

Download Skill

GitHub

关于