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

pyright

in CI to catch type errors early. For existing projects, enable strict mode incrementally using per-module overrides.

Pattern 2: Use Modern Union Syntax

Python 3.10+ provides cleaner union syntax.

python

# Preferred (3.10+)
def find_user(user_id: str) -> User | None:
    ...

def parse_value(v: str) -> int | float | str:
    ...

# Older style (still valid, needed for 3.9)
from typing import Optional, Union

def find_user(user_id: str) -> Optional[User]:
    ...

Pattern 3: Type Narrowing with Guards

Use conditionals to narrow types for the type checker.

python

def process_user(user_id: str) -> UserData:
    user = find_user(user_id)

    if user is None:
        raise UserNotFoundError(f"User {user_id} not found")

    # Type checker knows user is User here, not User | None
    return UserData(
        name=user.name,
        email=user.email,
    )

def process_items(items: list[Item | None]) -> list[ProcessedItem]:
    # Filter and narrow types
    valid_items = [item for item in items if item is not None]
    # valid_items is now list[Item]
    return [process(item) for item in valid_items]

Pattern 4: Generic Classes

Create type-safe reusable containers.

python

from typing import TypeVar, Generic

T = TypeVar("T")
E = TypeVar("E", bound=Exception)

class Result(Generic[T, E]):
    """Represents either a success value or an error."""

    def __init__(
        self,
        value: T | None = None,
        error: E | None = None,
    ) -> None:
        if (value is None) == (error is None):
            raise ValueError("Exactly one of value or error must be set")
        self._value = value
        self._error = error

    @property
    def is_success(self) -> bool:
        return self._error is None

    @property
    def is_failure(self) -> bool:
        return self._error is not None

    def unwrap(self) -> T:
        """Get value or raise the error."""
        if self._error is not None:
            raise self._error
        return self._value  # type: ignore[return-value]

    def unwrap_or(self, default: T) -> T:
        """Get value or return default."""
        if self._error is not None:
            return default
        return self._value  # type: ignore[return-value]

# Usage preserves types
def parse_config(path: str) -> Result[Config, ConfigError]:
    try:
        return Result(value=Config.from_file(path))
    except ConfigError as e:
        return Result(error=e)

result = parse_config("config.yaml")
if result.is_success:
    config = result.unwrap()  # Type: Config

Advanced Patterns

Pattern 5: Generic Repository

Create type-safe data access patterns.

python

from typing import TypeVar, Generic
from abc import ABC, abstractmethod

T = TypeVar("T")
ID = TypeVar("ID")

class Repository(ABC, Generic[T, ID]):
    """Generic repository interface."""

    @abstractmethod
    async def get(self, id: ID) -> T | None:
        """Get entity by ID."""
        ...

    @abstractmethod
    async def save(self, entity: T) -> T:
        """Save and return entity."""
        ...

    @abstractmethod
    async def delete(self, id: ID) -> bool:
        """Delete entity, return True if existed."""
        ...

class UserRepository(Repository[User, str]):
    """Concrete repository for Users with string IDs."""

    async def get(self, id: str) -> User | None:
        row = await self._db.fetchrow(
            "SELECT * FROM users WHERE id = $1", id
        )
        return User(**row) if row else None

    async def save(self, entity: User) -> User:
        ...

    async def delete(self, id: str) -> bool:
        ...

Pattern 6: TypeVar with Bounds

Restrict generic parameters to specific types.

python

from typing import TypeVar
from pydantic import BaseModel

ModelT = TypeVar("ModelT", bound=BaseModel)

def validate_and_create(model_cls: type[ModelT], data: dict) -> ModelT:
    """Create a validated Pydantic model from dict."""
    return model_cls.model_validate(data)

# Works with any BaseModel subclass
class User(BaseModel):
    name: str
    email: str

user = validate_and_create(User, {"name": "Alice", "email": "a@b.com"})
# user is typed as User

# Type error: str is not a BaseModel subclass
result = validate_and_create(str, {"name": "Alice"})  # Error!

Pattern 7: Protocols for Structural Typing

Define interfaces without requiring inheritance.

python

from typing import Protocol, runtime_checkable

@runtime_checkable
class Serializable(Protocol):
    """Any class that can be serialized to/from dict."""

    def to_dict(self) -> dict:
        ...

    @classmethod
    def from_dict(cls, data: dict) -> "Serializable":
        ...

# User satisfies Serializable without inheriting from it
class User:
    def __init__(self, id: str, name: str) -> None:
        self.id = id
        self.name = name

    def to_dict(self) -> dict:
        return {"id": self.id, "name": self.name}

    @classmethod
    def from_dict(cls, data: dict) -> "User":
        return cls(id=data["id"], name=data["name"])

def serialize(obj: Serializable) -> str:
    """Works with any Serializable object."""
    return json.dumps(obj.to_dict())

# Works - User matches the protocol
serialize(User("1", "Alice"))

# Runtime checking with @runtime_checkable
isinstance(User("1", "Alice"), Serializable)  # True

Pattern 8: Common Protocol Patterns

Define reusable structural interfaces.

python

from typing import Protocol

class Closeable(Protocol):
    """Resource that can be closed."""
    def close(self) -> None: ...

class AsyncCloseable(Protocol):
    """Async resource that can be closed."""
    async def close(self) -> None: ...

class Readable(Protocol):
    """Object that can be read from."""
    def read(self, n: int = -1) -> bytes: ...

class HasId(Protocol):
    """Object with an ID property."""
    @property
    def id(self) -> str: ...

class Comparable(Protocol):
    """Object that supports comparison."""
    def __lt__(self, other: "Comparable") -> bool: ...
    def __le__(self, other: "Comparable") -> bool: ...

Pattern 9: Type Aliases

Create meaningful type names.

Note: The

type

statement was introduced in Python 3.10 for simple aliases. Generic type statements require Python 3.12+.

python

# Python 3.10+ type statement for simple aliases
type UserId = str
type UserDict = dict[str, Any]

# Python 3.12+ type statement with generics
type Handler[T] = Callable[[Request], T]
type AsyncHandler[T] = Callable[[Request], Awaitable[T]]

# Python 3.9-3.11 style (needed for broader compatibility)
from typing import TypeAlias
from collections.abc import Callable, Awaitable

UserId: TypeAlias = str
Handler: TypeAlias = Callable[[Request], Response]

# Usage
def register_handler(path: str, handler: Handler[Response]) -> None:
    ...

Pattern 10: Callable Types

Type function parameters and callbacks.

python

from collections.abc import Callable, Awaitable

# Sync callback
ProgressCallback = Callable[[int, int], None]  # (current, total)

# Async callback
AsyncHandler = Callable[[Request], Awaitable[Response]]

# With named parameters (using Protocol)
class OnProgress(Protocol):
    def __call__(
        self,
        current: int,
        total: int,
        *,
        message: str = "",
    ) -> None: ...

def process_items(
    items: list[Item],
    on_progress: ProgressCallback | None = None,
) -> list[Result]:
    for i, item in enumerate(items):
        if on_progress:
            on_progress(i, len(items))
        ...

Configuration

Strict Mode Checklist

For

mypy --strict

compliance:

toml

# pyproject.toml
[tool.mypy]
python_version = "3.12"
strict = true
warn_return_any = true
warn_unused_ignores = true
disallow_untyped_defs = true
disallow_incomplete_defs = true
no_implicit_optional = true

Incremental adoption goals:

All function parameters annotated
All return types annotated
Class attributes annotated
Minimize
```
Any
```
usage (acceptable for truly dynamic data)
Generic collections use type parameters (
```
list[str]
```
not
```
list
```
)

For existing codebases, enable strict mode per-module using

# mypy: strict

or configure per-module overrides in

pyproject.toml

Best Practices Summary

Annotate all public APIs - Functions, methods, class attributes
Use
T | None
- Modern union syntax over
```
Optional[T]
```
Run strict type checking -
```
mypy --strict
```
in CI
Use generics - Preserve type info in reusable code
Define protocols - Structural typing for interfaces
Narrow types - Use guards to help the type checker
Bound type vars - Restrict generics to meaningful types
Create type aliases - Meaningful names for complex types
Minimize
Any
- Use specific types or generics.
```
Any
```
is acceptable for truly dynamic data or when interfacing with untyped third-party code
Document with types - Types are enforceable documentation

python-type-safety

NPX Install

Tags

SKILL.md Content

Python Type Safety

When to Use This Skill

Core Concepts

1. Type Annotations

2. Generics

3. Protocols

4. Type Narrowing

Quick Start

Fundamental Patterns

Pattern 1: Annotate All Public Signatures

Pattern 2: Use Modern Union Syntax

Pattern 3: Type Narrowing with Guards

Pattern 4: Generic Classes

Advanced Patterns

Pattern 5: Generic Repository

Pattern 6: TypeVar with Bounds

Pattern 7: Protocols for Structural Typing

Pattern 8: Common Protocol Patterns

Pattern 9: Type Aliases

Pattern 10: Callable Types

Configuration

Strict Mode Checklist

Best Practices Summary