Enforce PEP 8, type hints, and testing patterns in Python code.
npx skills add https://github.com/eggboy/skills --skill python-best-practices-eggboy
Please help me install this Agent Skill. Skill: python-best-practices Source: https://github.com/eggboy/skills/tree/main/python-best-practices Command: npx skills add https://github.com/eggboy/skills --skill python-best-practices-eggboy
Python coding best practices, conventions, and architectural patterns for production-ready applications. Use when writing, reviewing, or refactoring Python code to apply modern patterns and idiomatic style.
Install Ruff, configure pyproject.toml, and begin refactoring a representative module to align with PEP 8, type hints, and vectorized data operations.
High-intent search queries and answers about installing and using this skill.
To configure Python linting, install Ruff and define your ruleset in pyproject.toml to enforce PEP 8 and modern conventions across modules. This setup establishes a baseline for production-ready code quality.
FastAPI best practices include using async endpoints, robust error handling, comprehensive OpenAPI docs, and dependency injection to build scalable and maintainable production-grade web applications.
When refactoring Python code, apply type hints and PEP 8 conventions by updating modules iteratively to align with modern idiomatic patterns, ensuring robust type safety and readable code structure.
The dataframe mindset emphasizes vectorization, columnar operations, and method chaining across Pandas, Polars, DuckDB, and Spark to optimize data processing and avoid inefficient row-wise iterations.
For robust testing strategies, using pytest alongside hypothesis and Faker is recommended to enforce comprehensive test coverage and validate code behavior against edge cases in production modules.
Use Python data model features like dunder methods, iterators, context managers, and descriptors when you need custom object behavior, resource management, or advanced iteration patterns in your architecture.
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