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python-best-practices

python-best-practices

github.com/alleneubankDevelopment502.2k installsUpdated 18w ago
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About python-best-practices

A Python skill for coding agents covering type-first design, error handling, structured logging, and language-specific idioms. It also outlines optional type-checking tools and when to consider them.

Key features

  1. 01Immutable dataclasses
  2. 02Typed unions and NewType
  3. 03Structural typing with Protocol
  4. 04Exception chaining
  5. 05Structured logging

Use cases

  1. 01Modeling immutable domain data
  2. 02Preventing invalid typed values
  3. 03Preserving tracebacks when raising errors
  4. 04Choosing a Python type checker

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alleneubank/claude-code --skill python-best-practices

After installing, type/python-best-practicesin Claude Code to confirm it loaded.

Details

From the project README

Python Best Practices

Follows type-first, functional, and error handling patterns from CLAUDE.md. This skill covers language-specific idioms only.

Make Illegal States Unrepresentable

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()

Python-Specific Error Handling

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

Structured Logging

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

Optional: ty

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 integration
  • mypy — mature, extensive plugin ecosystem

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