Add example Ruff configurations for various domains

[Kilo59]

Create a configs/ directory in the repository with nested pyproject.toml or ruff.toml files. This structure allows users to sync curated domain-specific configurations out of the box using the --path flag.

Proposed Structure

configs/
├── kitchen-sink/
│   └── ruff.toml
├── fastapi/
│   └── ruff.toml
└── data-science-engineering/
    └── ruff.toml

Domain Configurations (Sub-Issues)

We have broken out the creation and discussion of these specific configurations into their own sub-issues. We strongly encourage contributors to join the discussion on these issues, propose rules/settings they find valuable in their daily work, and submit PRs for new domain-specific configs!

💡 Have an idea for a new domain config? We encourage contributors to open new issues proposing entirely new domains (e.g., django, aws-lambda, etc.), along with what makes their configurations unique.

1. Kitchen Sink (Done in Example Config: Kitchen Sink #85): configs/kitchen-sink/ruff.toml

   • Enables ALL possible Ruff rules.
   • Includes comments explaining each rule or collection of rules.
   • Provides links to the official Ruff documentation.
   • Includes relevant configuration block settings (e.g. [lint.isort] and [format]).

2. FastAPI/Web App: configs/fastapi/ruff.toml

   • See Sub-Issue: Example Config: FastAPI / Web App #86
   • Enabled rules relevant to web development (e.g., ANN for annotations, T20 for print statements, UP for upgrade).
   • Optimized for modern asynchronous applications.
   • Includes [lint.flake8-fastapi] (if applicable) and typing settings.

3. Data Science/Data Engineering: configs/data-science-engineering/ruff.toml

   • See Sub-Issue: Example Config: Data Science / Data Engineering #87
   • Focused on DS/DE tools and patterns.
   • Enabled rules for libraries like NumPy, Pandas, etc. (e.g., NPY, PD).
   • Specific settings for jupyter notebooks, if applicable.

Goal

These examples should be actually useful for their respective domains and will serve as high-quality snippets to pull into our documentation. Users will be able to sync using: ruff-sync https://github.com/Kilo59/ruff-sync --path configs/fastapi

[passionworkeer]

Great initiative! For the FastAPI config, you might want to include:

• ANN (annotations) - essential for type-heavy async code
• ASYNC (async safety) - catch blocking calls in async functions
• T20 - catch print statements
• UP - automated pyupgrade rules

For Data Science, in addition to NPY and PD, consider:

• RUF ( Ruff-specific rules) - often overlooked but useful
• C4 (comprehensions) - can speed up pandas operations

Would be happy to contribute a draft PR for the FastAPI config!

[passionworkeer]

建议从 'kitchen-sink' 配置开始，它会很有参考价值。可以按规则类别分组配置，并在每个配置项添加注释说明适用场景。对于 FastAPI 配置，可以重点关注异步代码常见的陷阱，如未使用的变量和类型注解。对于数据科学配置，可以预设 NumPy 和 Pandas 的特定规则。