Can this skill help reduce my Python application's memory usage?

Yes, it provides specific patterns for using generators instead of lists, implementing __slots__ in classes, and tracking memory leaks with specialized profilers.

How do I measure the execution time of small code snippets?

The skill demonstrates how to use the timeit module for accurate benchmarking and the time module for quick, simple measurements.

Which Python profiling tools are covered by this skill?

This skill includes implementation patterns for cProfile (CPU), memory_profiler (RAM), line_profiler (line-by-line), and py-spy for production environment profiling.

Is it possible to profile a running Python process without stopping it?

Yes, the skill provides patterns for using py-spy, a sampling profiler that can attach to running Python processes to generate flame graphs and top-function reports.

Does this support optimization for data science tasks?

Yes, it includes advanced patterns for using NumPy for vectorized numerical operations, which provides significant speedups over pure Python loops.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

•

Analytics & Monitoring

Profiles and optimizes Python code to eliminate bottlenecks, reduce memory usage, and improve application execution speed.

About

The Python Performance Optimization skill provides Claude with advanced capabilities to diagnose and resolve performance issues within Python applications. By integrating comprehensive profiling patterns—including CPU, memory, and line-by-line analysis—it enables the identification of slow functions and resource-heavy operations. Beyond diagnostics, it offers actionable optimization strategies such as implementing efficient data structures, leveraging caching mechanisms, utilizing NumPy for numerical acceleration, and adopting memory-efficient coding patterns. This skill is indispensable for developers looking to scale applications, reduce cloud infrastructure costs, and ensure high-responsiveness in production environments.

Key Features

2 GitHub stars
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
Algorithmic acceleration via NumPy vectorization and functools caching
Bottleneck identification for slow API endpoints and data pipelines
Production-level monitoring guidance with py-spy for running processes
Memory-efficient coding patterns using generators, __slots__, and string joins

Use Cases

Optimizing recursive algorithms and heavy mathematical computations
Debugging and fixing latency issues in web applications and services
Reducing the memory footprint of large-scale data processing scripts

About

Key Features

2 GitHub stars
CPU and memory profiling using cProfile, line_profiler, and memory_profiler
Algorithmic acceleration via NumPy vectorization and functools caching
Bottleneck identification for slow API endpoints and data pipelines
Production-level monitoring guidance with py-spy for running processes
Memory-efficient coding patterns using generators, __slots__, and string joins

Use Cases

Optimizing recursive algorithms and heavy mathematical computations
Debugging and fixing latency issues in web applications and services
Reducing the memory footprint of large-scale data processing scripts