How does this skill help with Python memory leaks?

It provides implementation patterns for using memory_profiler to track allocations and recommends using __slots__ to significantly reduce the memory footprint of class instances.

Can I profile production code without stopping it?

Yes, the skill includes guidance on using py-spy, which can attach to running Python processes to record flamegraphs and dump stacks without needing to restart the application.

Does this skill support numerical optimization?

Yes, it includes advanced patterns for using NumPy vectorization to replace slow Python loops and functools.lru_cache for efficient result caching.

Which tool is best for finding specific slow lines of code?

The skill recommends using line_profiler (kernprof), which provides a granular breakdown of time spent on every single line within a decorated function.

Python Performance Optimization

Name: Python Performance Optimization
Author: amurata

byamurata

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Developer Tools

Profiles and optimizes Python codebases to identify bottlenecks, reduce latency, and minimize memory consumption using industry-standard tools and patterns.

About

This skill provides a comprehensive toolkit for developers to diagnose and resolve performance issues in Python applications. It guides Claude through the entire optimization lifecycle—from initial execution timing and CPU profiling with cProfile to detailed line-by-line analysis and memory leak detection. By implementing proven patterns like list comprehensions, generator expressions, and NumPy vectorization, this skill helps transform slow, resource-heavy scripts into efficient, production-ready code with minimal overhead.

Key Features

Comprehensive CPU profiling using cProfile and call graph analysis
Implementation of high-performance patterns like caching, vectorization, and __slots__
Detailed line-by-line execution timing with line_profiler
Memory usage tracking and leak detection with memory_profiler
Production-grade process monitoring and flamegraph generation with py-spy
3 GitHub stars

Use Cases

Reducing the memory footprint of large-scale data processing pipelines
Identifying and fixing memory leaks in long-running production services
Debugging slow API response times or high CPU usage in Python web backends

About

Key Features

Comprehensive CPU profiling using cProfile and call graph analysis
Implementation of high-performance patterns like caching, vectorization, and __slots__
Detailed line-by-line execution timing with line_profiler
Memory usage tracking and leak detection with memory_profiler
Production-grade process monitoring and flamegraph generation with py-spy
3 GitHub stars

Use Cases

Reducing the memory footprint of large-scale data processing pipelines
Identifying and fixing memory leaks in long-running production services
Debugging slow API response times or high CPU usage in Python web backends