python-type-safety
Helps your agent use data types in Python to prevent errors. It makes code safer and easier to understand.
What it does
Helps you write more reliable Python code. It uses 'type hints' so the computer can check if you're using data correctly before the program even runs.
When to use it
- ✓When adding type checks to existing code.
- ✓When creating code parts that can be used with different data types.
- ✓When setting up tools to automatically check your Python code.
Example of what to ask your AI
“How can I ensure my function always receives an integer?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
You add 'labels' (type annotations) to your code to say what data type each part should have. Tools like mypy or pyright read these labels and warn you if something is wrong.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill python-type-safetyOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/python-development/skills/python-type-safety/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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)
…
FAQ
What are 'type hints'?
They are like labels you put on your code to indicate the expected data type (e.g., text, integer, decimal number). They help the computer understand and check your code.
Why use this if Python is flexible?
Python's flexibility can lead to hard-to-find errors. Type hints help catch these errors earlier, making your code more robust and easier to maintain.
Do I need to change all my code?
Not necessarily. You can start by adding type hints to important parts of your code, like functions that others will use.
Details
Original description by 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.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4936
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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