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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

https://github.com/wshobson/agents/blob/main/plugins/python-development/skills/python-type-safety/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/wshobson/agents/blob/main/plugins/python-development/skills/python-type-safety/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
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-safety

Or download just the file:

curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/python-development/skills/python-type-safety/SKILL.md -o SKILL.md
Technical 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

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