Which profiling tools does this skill support?

The skill provides guidance on using cProfile for general CPU profiling, memory_profiler for RAM usage, line_profiler for granular analysis, and py-spy for non-intrusive production monitoring.

Can this skill help resolve memory leaks in Python?

Yes, it includes patterns for identifying memory-intensive operations and provides strategies for using generators and __slots__ to significantly reduce memory overhead.

Does it include optimizations for numerical data?

Absolutely. It covers advanced optimization patterns using NumPy for vectorized operations, which can offer massive speedups over traditional Python loops.

Is it possible to profile a running process without stopping it?

Yes, the skill includes instructions for using tools like py-spy, which can attach to running Python processes to record flamegraphs or dump call stacks without restarts.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

분석 및 모니터링

Profiles and optimizes Python codebases using advanced diagnostic tools and proven efficiency patterns.

소개

This skill provides Claude with specialized knowledge to identify and resolve performance bottlenecks in Python applications. It covers a wide range of diagnostic techniques, including CPU and memory profiling with tools like cProfile and memory_profiler, as well as the implementation of high-performance coding patterns such as generator expressions, LRU caching, and vectorized operations with NumPy. This is an essential toolkit for developers aiming to reduce application latency, minimize memory consumption, and scale Python-based data pipelines or web services efficiently.

주요 기능

CPU profiling with cProfile and py-spy for production monitoring
Implementation of memory-efficient data structures and __slots__
Memory allocation analysis and leak detection with memory_profiler
Advanced optimization using NumPy vectorization and LRU caching
0 GitHub stars
Line-by-line performance bottleneck identification with line_profiler

사용 사례

Debugging slow execution times in production Python scripts or web applications
Reducing the memory footprint of high-volume data processing pipelines
Optimizing algorithmic complexity and reducing I/O wait times in backend services

소개

주요 기능

CPU profiling with cProfile and py-spy for production monitoring
Implementation of memory-efficient data structures and __slots__
Memory allocation analysis and leak detection with memory_profiler
Advanced optimization using NumPy vectorization and LRU caching
0 GitHub stars
Line-by-line performance bottleneck identification with line_profiler

사용 사례

Debugging slow execution times in production Python scripts or web applications
Reducing the memory footprint of high-volume data processing pipelines
Optimizing algorithmic complexity and reducing I/O wait times in backend services