python-type-safety

# Python Type Safety Leverage Python's type system to catch errors at static analysis time. Type annotations serve as enforced documentation that tooling validates automatically. ## When to Use This Skill - Adding type hints to existing code - Creating generic, reusable classes - Defining structural interfaces with protocols - Configuring mypy or pyright for strict checking - Understanding type narrowing and guards - Building type-safe APIs and libraries ## Core Concepts ### 1. Type Annotations Declare expected types for function parameters, return values, and variables. ### 2. Generics Write reusable code that preserves type information across different types. ### 3. Protocols Define structural interfaces without inheritance (duck typing with type safety). ### 4. Type Narrowing Use guards and conditionals to narrow types within code blocks. ## Quick Start ```python def get_user(user_id: str) -> User | None: """Return type makes 'might not exist' explicit.""" ... # Type checker enforces handling None case user = get_user("123") if user is None: raise UserNotFoundError("123") print(user.name) # Type checker knows user is User here ``` ## Fundamental Patterns ### Pattern 1: Annotate All Public Signatures Every public function, method, and class should have type annotations. ```python def get_user(user_id: str) -> User: """Retrieve user by ID.""" ... def process_batch( items: list[Item], max_workers: int = 4, ) -> BatchResult[Proc...