Can I profile memory usage without stopping my application?

Yes, this skill includes instructions for using py-spy, which attaches to running Python processes to sample performance data without significant overhead.

How does lru_cache improve Python performance?

The functools.lru_cache decorator stores the results of expensive function calls, allowing subsequent calls with the same arguments to return instantly.

How do I identify the slowest part of my Python script?

Use the cProfile pattern included in this skill to generate a call graph and sort functions by cumulative execution time to pinpoint bottlenecks.

What is the benefit of using __slots__ in Python classes?

Using __slots__ prevents the creation of a dynamic __dict__ for each instance, significantly reducing memory consumption when managing millions of objects.

Python Performance Optimizer

Name: Python Performance Optimizer
Author: wshobson

bywshobson

•

23,194

Analytics & Monitoring

Profiles and optimizes Python code to eliminate execution bottlenecks and reduce memory consumption.

About

This skill empowers developers to systematically improve Python application performance through advanced profiling and optimization techniques. It covers essential tools like cProfile and memory_profiler, alongside practical implementation patterns for caching, memory-efficient data structures, and vectorized operations. Whether you are debugging a slow API, scaling a data pipeline, or fixing memory leaks, this skill provides the metrics and refactoring strategies needed to achieve production-grade efficiency within the Claude Code environment.

Key Features

CPU and memory profiling using cProfile, line_profiler, and memory_profiler
23,194 GitHub stars
Production-safe sampling and flamegraph generation with py-spy
Optimization patterns for data structures, loops, and string concatenation
Memory management techniques using generators and __slots__ for class instances
High-performance numerical operations and vectorization with NumPy

Use Cases

Reducing memory footprints and detecting leaks in long-running services
Benchmarking and refactoring legacy code for better latency and scalability
Identifying and resolving bottlenecks in slow data processing pipelines

About

Key Features

CPU and memory profiling using cProfile, line_profiler, and memory_profiler
23,194 GitHub stars
Production-safe sampling and flamegraph generation with py-spy
Optimization patterns for data structures, loops, and string concatenation
Memory management techniques using generators and __slots__ for class instances
High-performance numerical operations and vectorization with NumPy

Use Cases

Reducing memory footprints and detecting leaks in long-running services
Benchmarking and refactoring legacy code for better latency and scalability
Identifying and resolving bottlenecks in slow data processing pipelines