How does this skill improve asynchronous performance?

It provides patterns for using uvloop, TaskGroups, and non-blocking file I/O to maximize throughput and prevent common event loop stalls.

Is this skill useful for Data Science applications?

Absolutely. The rules for memory-efficient data handling and fast numeric operations are critical for performance-sensitive data processing pipelines.

Can this skill help reduce server memory usage?

Yes, it includes specific rules for using __slots__, generators for large sequences, and array.array for numeric data, which can reduce memory consumption by up to 50-80%.

Which Python versions does this skill support?

This skill is specifically optimized for Python 3.11 and newer, taking advantage of modern features like TaskGroups, the walrus operator, and zero-cost exceptions.

Python Performance & Best Practices

Name: Python Performance & Best Practices
Author: pproenca

bypproenca

•

API開発

Optimizes Python 3.11+ code for peak performance using 42 prioritized rules covering async I/O, memory management, and efficient data structures.

This skill provides a comprehensive framework for writing, reviewing, and refactoring Python 3.11+ applications with a focus on high-impact performance gains. It offers specific, actionable guidance across critical domains such as structured concurrency with TaskGroups, memory-efficient class design using __slots__, and optimized data structure selection. By integrating these prioritized rules into the development workflow, developers can significantly reduce latency and resource consumption while ensuring their code adheres to modern Pythonic idioms and best practices for high-scale applications.

主な機能

01Prioritized optimization rules across 8 categories for high-impact refactoring

02Python 3.11+ specific micro-optimizations like zero-cost exception handling

03Advanced async I/O patterns including TaskGroups, uvloop, and connection pooling

0447 GitHub stars

05Memory-saving techniques like string interning, generators, and weak references

06Data structure selection guide for O(1) and O(log n) complexity operations

ユースケース

01Scaling high-concurrency asynchronous applications using structured concurrency patterns

02Optimizing data-intensive backend services to reduce memory footprint and latency

03Refactoring legacy Python codebases to leverage modern 3.11+ performance improvements

What are Skills?·How to Install

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npx skillfish add pproenca/dot-skills .curated

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