Type-first Python development using dataclasses, discriminated unions, NewType, and Protocol to make illegal states unrepresentable.
Works with
Define data models and function signatures before implementation; use frozen dataclasses, Literal-based discriminated unions, and NewType for domain primitives to prevent invalid states at type-check time
Leverage Protocol for structural typing, TypedDict for external data shapes, and exhaustive pattern matching with match statements to catch incomplete lo
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpython-best-practicesExecute the skills CLI command in your project's root directory to begin installation:
Fetches python-best-practices from 0xbigboss/claude-code 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-best-practices. Access via /python-best-practices 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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Accelerate learning and skill development by 2x
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Follows type-first, functional, and error handling patterns from CLAUDE.md. This skill covers language-specific idioms only.
Use Python's type system to prevent invalid states at type-check time.
Frozen dataclasses for immutable domain models:
from dataclasses import dataclass
from datetime import datetime
@dataclass(frozen=True)
class User:
id: str
email: str
name: str
created_at: datetime
# Frozen dataclasses are immutable — no accidental mutation
Discriminated unions with Literal:
from dataclasses import dataclass
from typing import Literal
@dataclass
class Success:
status: Literal["success"] = "success"
data: str
@dataclass
class Failure:
status: Literal["error"] = "error"
error: Exception
RequestState = Success | Failure
def handle_state(state: RequestState) -> None:
match state:
case Success(data=data):
render(data)
case Failure(error=err):
show_error(err)
NewType for domain primitives:
from typing import NewType
UserId = NewType("UserId", str)
OrderId = NewType("OrderId", str)
def get_user(user_id: UserId) -> User:
# Type checker prevents passing OrderId here
...
Protocol for structural typing:
from typing import Protocol
class Readable(Protocol):
def read(self, n: int = -1) -> bytes: ...
def process_input(source: Readable) -> bytes:
# Accepts any object with a read() method — no inheritance required
return source.read()
Chain exceptions with from err to preserve the original traceback:
try:
data = json.loads(raw)
except json.JSONDecodeError as err:
raise ValueError(f"invalid JSON payload: {err}") from err
Use a module-level logger with %s formatting (deferred string interpolation):
import logging
logger = logging.getLogger("myapp.widgets")
def create_widget(name: str) -> Widget:
logger.debug("creating widget: %s", name)
widget = Widget(name=name)
logger.debug("created widget id=%s", widget.id)
return widget
For fast type checking, consider ty from Astral (creators of ruff and uv). Written in Rust, significantly faster than mypy or pyright.
uvx ty check # run directly, no install needed
uvx ty check src/ # check specific path
# pyproject.toml
[tool.ty]
python-version = "3.12"
When to choose:
ty — fastest, good for CI and large codebases (early stage, rapidly evolving)pyright — most complete type inference, VS Code integrationmypy — mature, extensive plugin ecosystemPrerequisites
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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We added python-best-practices from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: python-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.
python-best-practices fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for python-best-practices matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: python-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added python-best-practices from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
python-best-practices fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
python-best-practices has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: python-best-practices is focused, and the summary matches what you get after install.
Keeps context tight: python-best-practices is the kind of skill you can hand to a new teammate without a long onboarding doc.
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