/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)

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](modules/profiling-tools.md)

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](modules/optimization-patterns.md)

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](modules/memory-management.md)

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

[benchmarking-tools](modules/benchmarking-tools.md)

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

[best-practices](modules/best-practices.md)

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

Exit Criteria

• Profiled code to identify bottlenecks
• Applied appropriate optimization patterns
• Verified improvements with benchmarks
• Memory usage acceptable
• No performance regressions