SKILL.md
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
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 hereFundamental Patterns
Pattern 1: Annotate All Public Signatures
Every public function, method, and class should have type annotations.
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 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.
# 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.
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.
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: ConfigAdvanced Patterns
Pattern 5: Generic Repository
Create type-safe data access patterns.
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.
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.
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) # TruePattern 8: Common Protocol Patterns
Define reusable structural interfaces.
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 Alias = ... statement syntax (PEP 695) was introduced in Python 3.12, not 3.10. For projects targeting earlier versions (including 3.10/3.11), use the TypeAlias annotation (PEP 613, available since Python 3.10).
# Python 3.12+ type statement (PEP 695)
type UserId = str
type UserDict = dict[str, Any]
# Python 3.12+ type statement with generics (PEP 695)
type Handler[T] = Callable[[Request], T]
type AsyncHandler[T] = Callable[[Request], Awaitable[T]]# Python 3.10-3.11 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.
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:
# 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 = trueIncremental adoption goals:
- All function parameters annotated
- All return types annotated
- Class attributes annotated
- Minimize
Anyusage (acceptable for truly dynamic data)
- Generic collections use type parameters (
list[str]notlist)
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 overOptional[T]
- Run strict type checking -
mypy --strictin 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.Anyis acceptable for truly dynamic data or when interfacing with untyped third-party code
- Document with types - Types are enforceable documentation