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[ProcessedItem]:
    """Process items concurrently."""
    ...

class UserRepository:
    def __init__(self, db: Database) -> None:
        self._db = db

    async def find_by_id(self, user_id: str) -> User | None:
        """Return User if found, None otherwise."""
        ...

    async def find_by_email(self, email: str) -> User | None:
        ...

    async def save(self, user: User) -> User:
        """Save and return user with generated ID."""
        ...
```

Use `mypy --strict` or `pyright`