How does this skill improve execution speed?

It identifies execution bottlenecks and suggests optimizations such as NumPy vectorization, LRU caching, and efficient variable access to minimize latency.

Can I use this for Python applications already in production?

Yes, the skill includes patterns for using py-spy, which allows you to profile and monitor running Python processes without restarting them or modifying the source code.

Can this skill help with Python memory leaks?

Yes, it includes patterns for memory profiling and specific optimization strategies like using generators, __slots__, and garbage collection tracking to resolve memory issues.

What tools does this skill use for Python profiling?

This skill utilizes industry-standard tools including cProfile for CPU profiling, line_profiler for line-by-line analysis, and memory_profiler for tracking memory allocations.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

Analytics & Monitoring

Profiles and optimizes Python code to identify bottlenecks, reduce memory consumption, and improve execution speed using industry-standard tools.

About

The Python Performance Optimization skill provides a comprehensive toolkit for developers to diagnose and resolve performance issues in Python applications. By leveraging profiling tools like cProfile and memory_profiler alongside advanced optimization patterns—such as vectorized NumPy operations, effective caching strategies, and memory-efficient data structures—this skill enables the transformation of slow, resource-heavy scripts into high-performance production code. It is particularly useful for optimizing data processing pipelines, reducing latency in web services, and debugging memory leaks in long-running processes.

Key Features

Performance-oriented coding patterns for strings, loops, and lookups
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
0 GitHub stars
Memory leak identification and optimization with generators and __slots__
Algorithmic speedups using NumPy vectorization and LRU caching
Production-level monitoring and flamegraph generation with py-spy

Use Cases

Optimizing memory usage in large-scale data processing pipelines
Reducing the response time of high-traffic Python API endpoints
Identifying and fixing performance bottlenecks in CPU-intensive algorithms

About

Key Features

Performance-oriented coding patterns for strings, loops, and lookups
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
0 GitHub stars
Memory leak identification and optimization with generators and __slots__
Algorithmic speedups using NumPy vectorization and LRU caching
Production-level monitoring and flamegraph generation with py-spy

Use Cases

Optimizing memory usage in large-scale data processing pipelines
Reducing the response time of high-traffic Python API endpoints
Identifying and fixing performance bottlenecks in CPU-intensive algorithms