python-type-safety

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npx skills add wshobson/agents --skill python-type-safety

Documentation

SKILL.md

npx skills add ...

Documentation

SKILL.md

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.

npx skills add wshobson/agents --skill python-type-safety

Documentation

SKILL.mdView source

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

Annotate all public APIs - Functions, methods, class attributes
Use T | None - Modern union syntax over Optional[T]
Run strict type checking - mypy --strict in 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. Any is acceptable for truly dynamic data or when interfacing with untyped third-party code
Document with types - Types are enforceable documentation

npx skills add wshobson/agents --skill python-type-safety

Documentation

SKILL.md

npx skills add wshobson/agents --skill python-type-safety

Documentation

SKILL.mdView source

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

Annotate all public APIs - Functions, methods, class attributes
Use T | None - Modern union syntax over Optional[T]
Run strict type checking - mypy --strict in 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. Any is acceptable for truly dynamic data or when interfacing with untyped third-party code
Document with types - Types are enforceable documentation