What profiling tools does this skill support?

The skill includes patterns for cProfile, line_profiler, memory_profiler, and py-spy for comprehensive performance analysis.

How can I speed up numerical calculations?

The skill provides patterns for using NumPy for vectorized operations, which can be orders of magnitude faster than standard Python loops.

Can I use this skill to profile production applications?

Yes, it provides guidance for using py-spy, which allows you to profile running Python processes without restarting or modifying the code.

Does it help with memory leak detection?

Absolutely. It includes memory profiling tools and optimization patterns like using generators and __slots__ to significantly reduce memory consumption.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: arthur0824hao

byarthur0824hao

Analytics & Monitoring

Profiles and optimizes Python code using industry-standard tools and best practices to resolve bottlenecks and reduce resource consumption.

About

This skill empowers developers to identify and eliminate performance bottlenecks in Python applications through comprehensive profiling and optimization techniques. It provides standardized implementation patterns for CPU and memory profiling using tools like cProfile and memory_profiler, while offering actionable guidance on algorithmic improvements, efficient data structures, and advanced optimizations like NumPy vectorization and LRU caching. Whether you are debugging a slow API or managing memory leaks in production, this skill provides the tools to build faster, leaner Python software.

Key Features

Advanced optimization techniques including NumPy vectorization and LRU caching
0 GitHub stars
Line-by-line performance analysis to pinpoint specific code bottlenecks
Implementation of memory-efficient patterns like generators and __slots__
Automated CPU and memory profiling setup using cProfile and memory-profiler
Production-safe profiling with py-spy for real-time performance monitoring

Use Cases

Debugging slow API response times and identifying lagging data processing pipelines
Optimizing CPU-intensive mathematical operations and algorithmic implementations
Reducing memory footprints and resolving leaks in high-scale Python applications

About

Key Features

Advanced optimization techniques including NumPy vectorization and LRU caching
0 GitHub stars
Line-by-line performance analysis to pinpoint specific code bottlenecks
Implementation of memory-efficient patterns like generators and __slots__
Automated CPU and memory profiling setup using cProfile and memory-profiler
Production-safe profiling with py-spy for real-time performance monitoring

Use Cases

Debugging slow API response times and identifying lagging data processing pipelines
Optimizing CPU-intensive mathematical operations and algorithmic implementations
Reducing memory footprints and resolving leaks in high-scale Python applications