Performance Optimization Skill

Overview

This skill teaches profiling, analyzing, and optimizing Python code performance.

Principles

• •Profile First: Measure before optimizing
• •Focus on Bottlenecks: Optimize where it matters
• •Consider Trade-offs: Balance performance vs readability
• •Verify Improvements: Measure after changes

Key Capabilities

1. Profiling

• •CPU profiling with cProfile, py-spy
• •Memory profiling with memory_profiler
• •Line-by-line profiling

2. Optimization Strategies

• •Algorithm optimization
• •Data structure selection
• •Caching strategies
• •Concurrency patterns

3. Caching

• •functools.lru_cache
• •Redis caching
• •Memoization patterns

When to Use This Skill

Load this skill when:

• •Profiling slow code
• •Identifying performance bottlenecks
• •Optimizing algorithms
• •Implementing caching
• •Adding concurrency

Sections

• •profiling.md: cProfile, py-spy, memory_profiler
• •caching.md: lru_cache, Redis, memoization
• •concurrency.md: asyncio, multiprocessing, threading
• •algorithms.md: Algorithm optimization techniques