What problem does it solve?
It provides a comprehensive set of best practices, coding standards, and technical guidelines to ensure high-quality, reliable, and maintainable Python code in production environments.
Core Features & Use Cases
- Code Quality & Safety: Implements strict typing, proper exception handling, and code linting with Ruff.
- Data Validation & Contracts: Uses Pydantic v2 models for robust data validation across modules.
- Async Best Practices: Guides the correct implementation of async I/O, resource management, and dependency injection.
- Testing & Debugging: Emphasizes pytest fixtures, async testing, and performance profiling.
- Logging & Observability: Standardizes structured logging via structlog for clarity and traceability.
- Configuration & Packaging: Advises on environment management, project setup, and dependency handling.
- Security & Error Handling: Encourages explicit exceptions, validation, and safe coding practices.
Quick Start
Configure your Python environment with the recommended tools and follow the guidelines for structuring your codebase to align with production standards.
