What kind of optimization patterns are included?

The skill covers a wide range of optimizations, from basic list comprehensions and string joins to advanced techniques like LRU caching and NumPy vectorization.

Can this skill help reduce Python memory usage?

Yes, it includes specific patterns for using generators, __slots__, and memory-profiler to identify memory leaks and minimize the footprint of large objects.

Is it possible to profile production Python applications?

Yes, the skill includes patterns for using py-spy, which allows for profiling running Python processes and generating flame graphs without restarting the application.

How does this skill help identify Python bottlenecks?

It provides specialized guidance on using profilers like cProfile, line_profiler, and py-spy to pinpoint the exact functions and lines of code causing execution delays.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: EricGrill

byEricGrill

•

Herramientas para Desarrolladores

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory usage, and improve execution speed.

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This Claude Code skill provides a comprehensive toolkit for identifying and resolving performance issues in Python applications. It guides Claude through advanced profiling techniques using industry-standard tools like cProfile, memory-profiler, and py-spy, while offering proven implementation patterns for algorithmic efficiency, memory management, and high-performance numerical operations. Whether you are debugging a slow script or scaling a production service, this skill helps you write more efficient, faster-executing Python code by applying both better data structures and native library optimizations.

Características Principales

Detailed memory usage analysis and leak detection patterns
Advanced optimization techniques including caching and NumPy vectorization
Line-by-line execution time breakdown for granular optimization
2 GitHub stars
Comprehensive CPU profiling with cProfile and py-spy integration
Performance-oriented coding patterns like generators and slotted classes

Casos de Uso

Optimizing backend API response times by reducing CPU-intensive overhead
Identifying and fixing bottlenecks in slow data processing pipelines
Reducing the memory footprint of large-scale Python applications and web services

Acerca de

Características Principales

Detailed memory usage analysis and leak detection patterns
Advanced optimization techniques including caching and NumPy vectorization
Line-by-line execution time breakdown for granular optimization
2 GitHub stars
Comprehensive CPU profiling with cProfile and py-spy integration
Performance-oriented coding patterns like generators and slotted classes

Casos de Uso

Optimizing backend API response times by reducing CPU-intensive overhead
Identifying and fixing bottlenecks in slow data processing pipelines
Reducing the memory footprint of large-scale Python applications and web services