python-pro_skill

Overview

This skill makes you productive with idiomatic, high-performance Python. I provide refactoring, advanced feature implementations (decorators, metaclasses, async), and rigorous testing to deliver maintainable code. The focus is on clear type-hinted code, measurable performance gains, and pragmatic design patterns.

How this skill works

I inspect your codebase or snippet, identify hotspots and anti-patterns, and propose concrete changes using Python best practices. Deliverables include rewritten modules with type hints, pytest suites and fixtures, simple benchmarks, and profiling summaries when required. I prefer standard library solutions first and introduce third-party tools only when they add clear value.

When to use it

• Refactoring legacy Python to be more idiomatic and maintainable
• Optimizing CPU or memory bottlenecks in critical code paths
• Implementing advanced patterns: decorators, descriptors, metaclasses
• Converting synchronous code to async/await or adding concurrency
• Raising test coverage and adding robust edge-case tests

Best practices

• Follow PEP 8 and type hints; run mypy and linters as part of CI
• Prefer composition and small, single-responsibility functions
• Use generators and streaming APIs for large datasets to save memory
• Add focused benchmarks and profile before optimizing
• Write pytest tests with fixtures and mocks for deterministic behavior

Example use cases

• Refactor a tangled module into clean classes/functions with type hints and comprehensive tests
• Add async/await support to I/O-bound services and provide concurrency patterns
• Create a decorator-based caching layer with configurable expiration and tests
• Profile an algorithmic hotspot, propose algorithmic or data-structure changes, and show benchmark improvements
• Design and implement dependency-injection patterns for easier testing and extensibility