Can this skill help identify memory leaks?

Yes, it includes techniques for using memory_profiler to track allocations and implementation patterns like __slots__ to significantly reduce the memory footprint of objects.

Can I use these techniques on a running production script?

Yes, the skill includes guidance on using py-spy, which allows for non-intrusive profiling of running Python processes without needing to restart the application.

Does this require external libraries like NumPy?

While it covers NumPy for numerical speedups, it also provides extensive optimizations for pure Python, such as local variable caching and efficient string concatenation.

How do I profile CPU usage in Python with this skill?

This skill provides specific patterns for using cProfile and py-spy to identify time-consuming functions and visualize execution paths using flame graphs.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

0•

Analytics & Monitoring

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory usage, and improve application latency.

This skill provides comprehensive guidance for diagnosing and resolving performance issues in Python applications. It equips developers with patterns for CPU and memory profiling using industry-standard tools like cProfile and py-spy, alongside practical implementation strategies such as list comprehensions, generator expressions, and NumPy vectorization. Whether you are debugging a slow production process or optimizing a high-throughput data pipeline, this skill helps you implement high-performance algorithms and efficient resource management practices.

Key Features

01Benchmarking workflows using timeit for precise execution measurement

02Advanced optimization patterns including LRU caching and NumPy vectorization

03Comprehensive CPU and memory profiling using cProfile, line_profiler, and py-spy

040 GitHub stars

05Memory-efficient data handling techniques with generator expressions and __slots__

06Identification and resolution of common bottlenecks like global variable access and call overhead

Use Cases

01Refactoring legacy Python code to improve latency and system response times

02Optimizing slow-running data processing pipelines and CPU-intensive operations

03Diagnosing and fixing memory leaks in long-running production applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/floreserlife python-performance-optimization

For use in Claude.ai and ChatGPT

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