Python Performance

pluginagentmarketplace/custom-plugin-python

This is a comprehensive learning resource for making Python code faster. It covers the full optimization toolkit: profiling to find slow spots, algorithm improvements, memory management, and leveraging compiled extensions like Cython and NumPy. You also get strategies for caching and parallelization. It's structured as a skill for Claude Code, meaning you can invoke it during development sessions to get guidance on performance work. If you're wrestling with sluggish Python applications or need to optimize compute-heavy operations, this gives you a systematic approach rather than random Stack Overflow fixes. The security audits are clean across the board, which matters since performance code often gets messy.

GitHub stars: 6
last updated: Aug 21, 2026

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Install

$ npx -y skills add pluginagentmarketplace/custom-plugin-python --skill 'Python Performance' --agent claude-code

Installs into .claude/skills of the current project.

Files

Python Performance Optimization

Overview

Master performance optimization in Python. Learn to profile code, identify bottlenecks, optimize algorithms, manage memory efficiently, and leverage high-performance libraries for compute-intensive tasks.

Learning Objectives

Profile Python code to identify bottlenecks
Optimize algorithms and data structures
Manage memory efficiently
Use compiled extensions (Cython, NumPy)
Implement caching strategies
Parallelize CPU-bound operations
Benchmark and measure improvements

Core Topics

1. Profiling & Benchmarking

timeit module for micro-benchmarks
cProfile for function-level profiling
line_profiler for line-by-line analysis
memory_profiler for memory usage
py-spy for production profiling
Flame graphs and visualization

Code Example:

import timeit
import cProfile
import pstats

# 1. timeit for micro-benchmarks
def list_comprehension():
    return [x**2 for x in range(1000)]

def map_function():
    return list(map(lambda x: x**2, range(1000)))

SKILL.md on GitHub