Static type checking with annotations, generics, protocols, and strict mode enforcement.
Works with
Covers type annotations, generics with TypeVars, structural protocols, and type narrowing patterns for catching errors at analysis time
Includes modern syntax (Python 3.10+ union types), bounded type variables, and generic repository patterns for type-safe APIs
Provides configuration guidance for mypy strict mode and incremental adoption strategies for existing codebases
Demonstrates 10 fundam
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpython-type-safetyExecute the skills CLI command in your project's root directory to begin installation:
Fetches python-type-safety from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate python-type-safety. Access via /python-type-safety in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Leverage Python's type system to catch errors at static analysis time. Type annotations serve as enforced documentation that tooling validates automatically.
Declare expected types for function parameters, return values, and variables.
Write reusable code that preserves type information across different types.
Define structural interfaces without inheritance (duck typing with type safety).
Use guards and conditionals to narrow types within code blocks.
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
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.
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]:
...
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]
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.isPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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python-type-safety has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: python-type-safety is focused, and the summary matches what you get after install.
Keeps context tight: python-type-safety is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend python-type-safety for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
python-type-safety is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
python-type-safety reduced setup friction for our internal harness; good balance of opinion and flexibility.
python-type-safety fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added python-type-safety from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: python-type-safety is the kind of skill you can hand to a new teammate without a long onboarding doc.
python-type-safety is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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