Can I profile a running production application?

Yes, it includes patterns for using py-spy, which allows you to attach to and profile running Python processes without needing to restart them.

Does it provide advice on Python-specific coding patterns?

Absolutely. It covers best practices like using list comprehensions, efficient string concatenation, and local variable caching for micro-optimizations.

How does this help with memory management?

It teaches you how to use memory_profiler to find leaks and implement memory-efficient structures like generators and __slots__ to reduce your application's footprint.

What profiling tools does this skill support?

The skill provides implementation patterns for cProfile, line_profiler, memory_profiler, and py-spy to cover CPU, memory, and production-level profiling.

Will this help me speed up mathematical calculations?

Yes, the skill includes specific guides on using NumPy for vectorized operations, which can be significantly faster than standard Python loops.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: kivilaid

bykivilaid

•

Analytics & Monitoring

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory consumption, and improve execution speed.

This skill provides comprehensive guidance for identifying and resolving performance issues in Python applications. It equips developers with patterns for CPU and memory profiling using industry-standard tools like cProfile and line_profiler, alongside advanced optimization strategies such as caching, vectorized operations with NumPy, and memory-efficient data structures. It is particularly useful when debugging slow production code, optimizing data processing pipelines, or scaling resource-intensive services to improve overall application responsiveness.

Key Features

016 GitHub stars

02Advanced optimization patterns for caching and local variable access

03Production-ready monitoring and flamegraph generation with py-spy

04Multi-level profiling including CPU, memory, and line-by-line analysis

05High-performance numerical operations and vectorization with NumPy

06Memory management techniques using generators and __slots__

Use Cases

01Identifying and fixing memory leaks in long-running background processes

02Optimizing large-scale data processing pipelines and ETL jobs

03Reducing latency and response times in high-traffic web applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add kivilaid/plugin-marketplace python-performance-optimization

For use in Claude.ai and ChatGPT

Download Skill