Python

A comprehensive performance reference for Python 3.11+ that gives Claude 42 specific optimization rules across eight categories, from critical async patterns and data structure selection down to micro-optimizations. Most useful when you're writing I/O-heavy code or need to make smart choices about collections, memory usage, and concurrency patterns. Each rule includes concrete performance impact metrics (like "2-5× faster" or "O(n) to O(1)"), so Claude can prioritize what actually matters. The focus on asyncio, connection pooling, and proper data structure selection makes this especially good for backend services and API work. It's opinionated about modern Python idioms, which means you'll get consistent recommendations but might clash if you're stuck on older patterns.

skills.sh installs: 323
GitHub stars: 222
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add pproenca/dot-skills --skill python --agent claude-code

Installs into .claude/skills of the current project.

Files

Python 3.11 Best Practices

Comprehensive performance optimization guide for Python 3.11+ applications. Contains 42 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation.

When to Apply

Reference these guidelines when:

Writing new Python async I/O code
Choosing data structures for collections
Optimizing memory usage in data-intensive applications
Implementing concurrent or parallel processing
Reviewing Python code for performance issues

Rule Categories by Priority

Priority	Category	Impact	Prefix
1	I/O & Async Patterns	CRITICAL	`io-`
2	Data Structure Selection	CRITICAL	`ds-`
3	Memory Optimization	HIGH	`mem-`
4	Concurrency & Parallelism	HIGH	`conc-`
5	Loop & Iteration	MEDIUM	`loop-`
6	String Operations	MEDIUM	`str-`
7	Function & Call Overhead	LOW-MEDIUM	`func-`
8	Python Idioms & Micro	LOW	`py-`