AI SummaryA booster that helps developers optimize slow Python code through profiling, async/await patterns, and concurrent execution strategies. Ideal for Python developers dealing with performance bottlenecks who need guidance on measurement before optimization.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "python-performance" skill in my project. Please run this command in my terminal: # Install skill into your project (2 files) mkdir -p .claude/skills/man && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/man/SKILL.md "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/man/SKILL.md" && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/man/metadata.json "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/man/metadata.json" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Use when Python code runs slowly, needs profiling, requires async/await patterns, or needs concurrent execution - covers profiling tools, optimization patterns, and asyncio; measure before optimizing (plugin:python@dot-claude)
Python Performance & Concurrency
Profiling, optimization, and async patterns for Python.
Before Writing Code
• Read references/pythonic-style.md for style conventions • Check Python version: 3.13+ enables free-threaded concurrency options • Profile before optimizing - never guess at bottlenecks
Reference Files
| Topic | When to Load | File | |-------|--------------|------| | Pythonic style | Before generating code | ../references/pythonic-style.md | | Semaphores, locks, producers/consumers | Advanced async | references/async-advanced.md | | Database, memory, NumPy optimization | Deep optimization | references/optimization-advanced.md |
cProfile (CPU)
`bash python -m cProfile -o output.prof script.py python -m pstats output.prof # Interactive analysis ` `python import cProfile import pstats with cProfile.Profile() as pr: main() stats = pstats.Stats(pr) stats.sort_stats("cumulative").print_stats(10) `
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