Does it cover numerical or math-heavy optimization?

Yes, it includes best practices for utilizing NumPy and vectorized operations to replace slow pure-Python loops for mathematical tasks.

Is it suitable for profiling live production environments?

Yes, it covers non-intrusive tools like py-spy which are designed to profile running Python processes with minimal overhead.

What profiling tools are supported by this skill?

The skill includes detailed guidance for cProfile, line_profiler, memory_profiler, and py-spy for real-time production profiling.

How do I decide between list comprehensions and generators?

The skill explains that list comprehensions are faster for data access but high in memory usage, while generators are ideal for memory efficiency with large datasets.

Can this skill help identify memory leaks?

Yes, it provides specific patterns for memory profiling and suggests memory-efficient alternatives like generators and __slots__ to reduce footprints.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

Analytics & Monitoring

Profiles and optimizes Python code to eliminate execution bottlenecks and improve resource efficiency.

About

This skill provides a comprehensive toolkit for analyzing and enhancing the performance of Python applications. It guides users through various profiling techniques—including CPU, memory, and line-by-line analysis—to pinpoint slow operations and memory leaks. By implementing best practices such as algorithmic improvements, caching strategies, and specialized data structures like __slots__, it helps developers build high-speed, memory-efficient Python services and data processing pipelines.

Key Features

Line-by-line performance analysis for granular bottleneck detection
Implementation of high-performance patterns like generators and list comprehensions
0 GitHub stars
Numerical optimization strategies using NumPy and vectorized operations
Comprehensive CPU and memory profiling using cProfile and memory_profiler
Advanced caching techniques using LRU caches to reduce redundant computations

Use Cases

Reducing latency in high-traffic Python web applications and APIs
Accelerating large-scale data processing and numerical computation pipelines
Identifying and fixing memory leaks in long-running background processes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/after-the-third-cup python-performance-optimization

For use in Claude.ai and ChatGPT

Download Skill

GitHub