Does it support profiling applications already running in production?

Yes, it provides instructions for using py-spy, a sampling profiler that can attach to running Python processes without requiring a restart or significantly affecting performance.

Can this skill help with Python memory leaks?

Yes, it includes specialized patterns for using memory-profiler to track object allocations and suggests using generators and __slots__ to significantly reduce memory footprints.

How do I identify the slowest part of my Python script?

You can use the cProfile module or the line_profiler pattern provided in this skill to generate a detailed breakdown of execution time per function and per line of code.

When should I switch from pure Python to NumPy?

This skill recommends switching to NumPy for numerical operations and large-scale mathematical calculations where vectorized operations can offer massive speedups over standard Python loops.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: 3commas-io

by3commas-io

Developer Tools

Profiles, analyzes, and optimizes Python code to eliminate bottlenecks, reduce latency, and minimize memory consumption.

About

The Python Performance Optimization skill provides a comprehensive framework for diagnosing and fixing efficiency issues in Python applications. It guides developers through the full optimization lifecycle, from high-level CPU profiling with cProfile and py-spy to granular line-by-line analysis and memory leak detection. By implementing proven patterns such as generator expressions, slotted classes, and caching strategies, this skill helps transform slow scripts into high-performance production systems. It is particularly useful when scaling data pipelines, reducing infrastructure costs, or improving the responsiveness of web services.

Key Features

Line-by-line execution analysis with line_profiler
High-performance implementation patterns for data structures
Advanced optimization using NumPy and lru_cache
0 GitHub stars
Memory usage tracking and optimization via memory_profiler
Comprehensive CPU profiling using cProfile and pstats

Use Cases

Optimizing memory-intensive data processing pipelines to prevent OOM errors
Profiling live production processes using non-intrusive sampling tools
Identifying and resolving performance bottlenecks in slow Python applications

About

Key Features

Line-by-line execution analysis with line_profiler
High-performance implementation patterns for data structures
Advanced optimization using NumPy and lru_cache
0 GitHub stars
Memory usage tracking and optimization via memory_profiler
Comprehensive CPU profiling using cProfile and pstats

Use Cases

Optimizing memory-intensive data processing pipelines to prevent OOM errors
Profiling live production processes using non-intrusive sampling tools
Identifying and resolving performance bottlenecks in slow Python applications