Python Performance Optimizer

About

This skill equips Claude with the expertise to transform slow, resource-heavy Python code into high-performance applications. It covers the end-to-end optimization workflow, from initial CPU and memory profiling using tools like cProfile and memory_profiler to implementing advanced patterns such as generator expressions, LRU caching, and NumPy vectorization. By leveraging this skill, developers can systematically identify bottlenecks, reduce application latency, and minimize memory footprints in any Python environment.

Key Features

• Memory usage tracking and leak detection using memory_profiler
• Line-by-line execution analysis to pinpoint slow code within specific functions
• 1 GitHub stars
• Implementation of high-performance patterns including list comprehensions and generators
• Advanced optimization techniques using NumPy vectorization and LRU caching
• Comprehensive CPU profiling using cProfile and pstats for bottleneck identification

Use Cases

• Reducing backend API latency by optimizing slow database queries and function calls
• Identifying and fixing memory leaks in long-running data processing applications
• Scaling numerical computations by transitioning from pure Python loops to NumPy vectorization