Python Type Safety
Seth HobsonCommunityCreated by Seth Hobson
- Skill
- Guidelines
- Software development
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a comprehensive framework for enhancing Python code quality through static type checking. It covers the application of type annotations, the creation of generic classes for reusable logic, and the definition of structural interfaces using protocols. By integrating these practices with tools like mypy or pyright, developers can catch errors early, improve code maintainability, and create self-documenting APIs.
What it does
- Provides patterns for adding type hints to functions, methods, and variables.
- Demonstrates how to build reusable, type-safe generic classes.
- Explains the use of protocols for structural typing without inheritance.
- Shows how to use type narrowing and guards to assist static analysis.
- Offers guidance on configuring strict type checking in CI environments.
- Defines best practices for creating meaningful type aliases.
When to use it
- When you want to catch bugs during static analysis rather than at runtime.
- When building complex APIs or libraries that require clear, enforced contracts.
- When you need to ensure type safety across different data structures.
- When refactoring legacy code to improve reliability and documentation.
- When setting up CI/CD pipelines to enforce code quality standards.
Advantages
- Reduces runtime errors by catching type mismatches during development.
- Serves as living, machine-validated documentation for your codebase.
- Improves IDE autocompletion and developer productivity.
- Enables structural typing, allowing for more flexible interface definitions.
- Supports incremental adoption in existing projects.
Usage examples
- “How do I define a generic repository interface in Python?”
- The skill provides a pattern using TypeVar and Generic to create a base Repository class, ensuring that concrete implementations like UserRepository maintain strict type safety for entities and IDs.
- “How can I check if an object satisfies an interface without inheritance?”
- The skill explains how to use Protocols and the @runtime_checkable decorator to define structural interfaces that objects can satisfy simply by implementing the required methods.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/wshobson/python-type-safety/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip python-type-safety.zip -d .agents/skills
Documentation by Seth Hobson
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: ConfigDetailed worked examples and patterns
Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
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 CIUse 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 codeDocument with types - Types are enforceable documentation
Files
- ATTRIBUTION.mdAdded by AutoGineer990 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md5.6 KiB
- references/details.md5.7 KiB
references/details.md
python-type-safety — detailed worked examples
Advanced 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": "[email protected]"})
# 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.
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
Security
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Attribution and licence
- Created by
- Seth Hobson
- Publisher
- Seth Hobson (Community)
- Source
- wshobson/agents · plugins/python-development/skills/python-type-safety · commit 4bde316
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Seth Hobson.
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