How do I benchmark small code snippets with this skill?

The skill provides standardized templates for using the timeit module to get accurate, repeatable execution time measurements for specific functions or logic blocks.

Does it provide support for numerical and data-heavy optimization?

Absolutely. It includes patterns for vectorizing operations with NumPy and optimizing data structures to handle large datasets more efficiently than standard Python loops.

Which Python profiling tools are supported by this skill?

The skill supports a wide range of tools including cProfile for CPU profiling, memory_profiler for tracking allocations, line_profiler for line-by-line analysis, and py-spy for production-level profiling.

Can this skill help reduce the memory footprint of my Python app?

Yes, it provides specific implementation patterns like using generators instead of lists, implementing __slots__ in classes, and identifying leaks with dedicated memory profiling tools.

Python Performance Optimization

Name: Python Performance Optimization
Author: HermeticOrmus

byHermeticOrmus

Analíticas y Monitorización

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

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This skill provides comprehensive guidance for diagnosing and resolving performance issues in Python applications. It enables Claude to implement advanced profiling techniques using tools like cProfile and memory_profiler, analyze execution metrics, and apply proven optimization patterns such as generator expressions, efficient string handling, and caching. Whether you're debugging a slow data pipeline, reducing memory leaks, or optimizing CPU-intensive algorithms, this skill ensures your Python code runs at peak efficiency through standardized benchmarking and implementation best practices.

Características Principales

Implementation of high-performance patterns including generators, list comprehensions, and slotted classes
Vectorized numerical optimization patterns utilizing NumPy for data-heavy operations
Comprehensive CPU and memory profiling using tools like cProfile, py-spy, and memory_profiler
Advanced caching strategies using functools.lru_cache for significant recursive algorithm speedups
0 GitHub stars
Bottleneck analysis and benchmarking using timeit for precise code path measurement

Casos de Uso

Optimizing CPU-intensive algorithms by replacing slow loops with efficient data structures
Reducing latency and response times in high-traffic Python web applications and APIs
Identifying and fixing memory leaks in long-running data processing pipelines

Acerca de

Características Principales

Implementation of high-performance patterns including generators, list comprehensions, and slotted classes
Vectorized numerical optimization patterns utilizing NumPy for data-heavy operations
Comprehensive CPU and memory profiling using tools like cProfile, py-spy, and memory_profiler
Advanced caching strategies using functools.lru_cache for significant recursive algorithm speedups
0 GitHub stars
Bottleneck analysis and benchmarking using timeit for precise code path measurement

Casos de Uso

Optimizing CPU-intensive algorithms by replacing slow loops with efficient data structures
Reducing latency and response times in high-traffic Python web applications and APIs
Identifying and fixing memory leaks in long-running data processing pipelines