python-performance

name	python-performance
description	Profiles Python code for performance bottlenecks and memory issues. Use when Python code is slow or when profiling for optimization before a release.
globs	**/*.py
alwaysApply	false
category	performance
tags	["python","performance","profiling","optimization","cProfile","memory"]
tools	[]
usage_patterns	["performance-analysis","bottleneck-identification","memory-optimization","algorithm-optimization"]
complexity	intermediate
model_hint	standard
estimated_tokens	1200
progressive_loading	true
modules	["modules/profiling-tools.md","modules/optimization-patterns.md","modules/memory-management.md","modules/benchmarking-tools.md","modules/best-practices.md"]

Python Performance Optimization

Profiling and optimization patterns for Python code.

Table of Contents

1. Quick Start

Quick Start

# Basic timing
import timeit
time = timeit.timeit("sum(range(1000000))", number=100)
print(f"Average: {time/100:.6f}s")

Verification: Run the command with --help flag to verify availability.

When To Use

• Identifying performance bottlenecks
• Reducing application latency
• Optimizing CPU-intensive operations
• Reducing memory consumption
• Profiling production applications
• Improving database query performance

When NOT To Use

• Async concurrency - use python-async instead
• CPU/GPU system monitoring - use conservation:cpu-gpu-performance
• Async concurrency - use python-async instead
• CPU/GPU system monitoring - use conservation:cpu-gpu-performance

Modules

This skill is organized into focused modules for progressive loading:

profiling-tools

CPU profiling with cProfile, line profiling, memory profiling, and production profiling with py-spy. Essential for identifying where your code spends time and memory.

optimization-patterns

Eleven proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, database operations, and loop transformations (what works in Python vs the compiler).

memory-management

Memory optimization techniques including leak tracking with tracemalloc and weak references for caches. Depends on profiling-tools.

benchmarking-tools

Benchmarking tools including custom decorators and pytest-benchmark for verifying performance improvements.

best-practices

Best practices, common pitfalls, and exit criteria for performance optimization work. Synthesizes guidance from profiling-tools and optimization-patterns.