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Python Type Safety

Seth HobsonCommunityCreated by Seth Hobson

  • Skill
  • Guidelines
  • Software development
  • MIT
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From the author

Python type safety with type hints, generics, protocols, and strict type checking. Use when adding type annotations, implementing generic classes, defining structural interfaces, or configuring mypy/pyright.
— the description by Seth Hobson

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/wshobson/python-type-safety/download
  2. Unzip 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 here

Fundamental 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: Config

Detailed 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

  1. Annotate all public APIs - Functions, methods, class attributes

  2. Use T | None - Modern union syntax over Optional[T]

  3. Run strict type checking - mypy --strict in CI

  4. Use generics - Preserve type info in reusable code

  5. Define protocols - Structural typing for interfaces

  6. Narrow types - Use guards to help the type checker

  7. Bound type vars - Restrict generics to meaningful types

  8. Create type aliases - Meaningful names for complex types

  9. Minimize Any - Use specific types or generics. Any is acceptable for truly dynamic data or when interfacing with untyped third-party code

  10. Document with types - Types are enforceable documentation

Files

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)  # True
Pattern 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 = 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.

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

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