Can this skill help resolve Python memory leaks?

Yes, it includes memory profiling techniques and optimization strategies like using generators, __slots__, and object lifecycle management to reduce memory footprints.

What tools does this skill support for Python profiling?

The skill provides implementation patterns for cProfile, line_profiler, memory_profiler, and py-spy for both development and production monitoring.

Does it provide optimizations for numerical data?

Yes, it covers advanced optimization using NumPy for vectorized operations, which can offer significant speedups over pure Python loops.

Is this skill useful for debugging production applications?

Absolutely. It includes patterns for using py-spy, which allows you to profile running Python processes and generate flamegraphs without restarting the application.

Python Performance Optimization

Name: Python Performance Optimization
Author: carloscroc

bycarloscroc

分析と監視

Profiles and optimizes Python code to identify bottlenecks, reduce memory consumption, and improve execution speed using industry-standard tools and patterns.

概要

Python Performance Optimization is a specialized skill for Claude Code designed to help developers measure and enhance the efficiency of their Python applications. It provides comprehensive guidance on CPU and memory profiling, line-by-line execution analysis, and the implementation of high-performance coding patterns. By leveraging tools like cProfile and py-spy alongside techniques such as caching, vectorization with NumPy, and memory-efficient data structures, this skill enables developers to drastically reduce latency and infrastructure costs in both development and production environments.

主な機能

Numerical operation optimization using NumPy vectorization
0 GitHub stars
Line-by-line performance benchmarking to isolate specific bottlenecks
Memory usage analysis and leak detection with memory_profiler
Implementation of high-performance patterns like LRU caching and generators
Comprehensive CPU profiling using cProfile and real-time py-spy monitoring

ユースケース

Reducing memory footprints in containerized applications to lower cloud infrastructure costs
Optimizing data processing pipelines and heavy numerical computations for faster execution
Diagnosing and fixing slow API response times or database query overhead

概要

主な機能

Numerical operation optimization using NumPy vectorization
0 GitHub stars
Line-by-line performance benchmarking to isolate specific bottlenecks
Memory usage analysis and leak detection with memory_profiler
Implementation of high-performance patterns like LRU caching and generators
Comprehensive CPU profiling using cProfile and real-time py-spy monitoring

ユースケース

Reducing memory footprints in containerized applications to lower cloud infrastructure costs
Optimizing data processing pipelines and heavy numerical computations for faster execution
Diagnosing and fixing slow API response times or database query overhead