Is this skill suitable for production environments?

Yes, it includes guidance for using py-spy, which is a sampling profiler designed to attach to running processes with minimal overhead, making it safe for production use.

Can this skill help with Python memory leaks?

Yes, it provides patterns for using memory_profiler to track allocations and implements memory-saving techniques like generators and __slots__ to reduce the memory footprint.

How does it improve recursion performance?

It provides implementation guides for functools.lru_cache to significantly speed up recursive algorithms through memoization and result caching.

Does it cover numerical optimization?

Absolutely, it includes specific patterns for replacing slow Python loops with high-performance vectorized operations using NumPy.

What tools does this skill support for profiling?

It supports cProfile for CPU profiling, line_profiler for line-by-line analysis, memory_profiler for tracking allocations, and py-spy for production monitoring.

Python Performance Optimization

Name: Python Performance Optimization
Author: Microck

byMicrock

•

分析与监控

Optimizes Python applications through comprehensive profiling, memory analysis, and high-performance implementation patterns.

This skill provides Claude with advanced capabilities to identify and resolve performance bottlenecks in Python codebases. It covers a wide range of optimization techniques, from low-level profiling using tools like cProfile and line_profiler to high-level architectural improvements like vectorization with NumPy, efficient memory management with generators, and caching strategies. Whether you are debugging slow production services, reducing memory leaks, or speeding up data processing pipelines, this skill guides the process of measuring, analyzing, and refactoring Python code for maximum efficiency.

主要功能

01Automated caching implementations using functools.lru_cache

02Production-safe performance monitoring and flamegraph generation with py-spy

03Memory-efficient coding patterns using generators and __slots__

042 GitHub stars

05CPU and memory profiling using cProfile, line_profiler, and memory_profiler

06High-performance numerical optimization with NumPy vectorization

使用场景

01Reducing latency and response times in slow web applications

02Identifying and fixing memory leaks in long-running processes

03Accelerating data processing pipelines and CPU-intensive algorithms

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/opendots-microck python-performance-optimization

For use in Claude.ai and ChatGPT

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