When should I use this skill instead of just rewriting code?

You should use this skill whenever your application fails to meet production performance requirements, as it helps identify if the issue is algorithmic, I/O-bound, or CPU-bound.

How does this skill identify slow Python code?

It provides a structured workflow starting with cProfile and line-by-line profiling to identify exact bottlenecks before suggesting algorithmic or architectural changes.

Does it support high-performance libraries like NumPy?

Yes, the skill offers specific guidance on vectorization with NumPy, JIT compilation with Numba, and using Cython for performance-critical hot paths.

Can this skill help with memory leaks?

Yes, it includes specialized patterns for memory profiling, checking for high consumption, and using generators to handle large datasets without exhausting RAM.

Python Performance Optimization

Name: Python Performance Optimization
Author: NickCrew

byNickCrew

•

Herramientas para Desarrolladores

Optimizes Python applications through systematic profiling, algorithmic improvements, and advanced acceleration techniques.

Acerca de

This skill provides expert guidance for diagnosing and resolving performance bottlenecks in Python code, ranging from memory leaks and high CPU usage to slow API response times. It enforces a disciplined 'measure-before-optimize' workflow using tools like cProfile and line profilers, offering specific implementation patterns for algorithmic complexity reduction, memory management via generators, and computational acceleration using NumPy, Numba, and multiprocessing. It is ideal for developers looking to reduce cloud infrastructure costs or scale data-heavy applications.

Características Principales

Concurrency and parallelism implementation for CPU-bound and I/O-bound tasks
Memory efficiency strategies using generators and lazy evaluation
7 GitHub stars
Algorithmic complexity reduction and data structure optimization patterns
Profile-guided bottleneck identification using cProfile and memory profilers
Computational acceleration via NumPy vectorization and JIT compilation with Numba

Casos de Uso

Scaling scientific computing tasks through vectorization and compiled Python extensions
Reducing cloud infrastructure costs by optimizing resource-heavy data processing pipelines
Improving web application responsiveness by eliminating slow logic and optimizing database interactions

Acerca de

Características Principales

Concurrency and parallelism implementation for CPU-bound and I/O-bound tasks
Memory efficiency strategies using generators and lazy evaluation
7 GitHub stars
Algorithmic complexity reduction and data structure optimization patterns
Profile-guided bottleneck identification using cProfile and memory profilers
Computational acceleration via NumPy vectorization and JIT compilation with Numba

Casos de Uso

Scaling scientific computing tasks through vectorization and compiled Python extensions
Reducing cloud infrastructure costs by optimizing resource-heavy data processing pipelines
Improving web application responsiveness by eliminating slow logic and optimizing database interactions