name: python-performance description: Profiles Python code for performance bottlenecks and memory issues version: 1.9.8 triggers: - python - performance - profiling - optimization - cProfile - memory - Python code is slow or when profiling for optimization before a release metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/parseltongue", "emoji": "\u26a1"}} source: claude-night-market source_plugin: parseltongue

Night Market Skill — ported from claude-night-market/parseltongue. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

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

Ten proven optimization patterns including list comprehensions, generators, caching, string concatenation, data structures, NumPy, multiprocessing, and database operations.

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.

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Exit Criteria

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

这是一个质量不错的Python性能优化技能，内容全面、示例丰富、结构清晰。覆盖从代码分析到优化模式再到基准测试的完整流程，代码示例有实际的性能提升数据对比。主要优点是分类清晰、实用性强；不足之处是部分模块内容较为简短，缺乏深入指导。总体适合需要系统性学习Python优化的开发者使用。