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Patterns Python

/SKILL

> Cette compétence fournit des modèles Python complets qui étendent les principes de conception communs avec des idiomes spécifiques à Python.

affaan-maffaan-m
240.5k
4 juin 2026
MIT
// contenu du skill

name: python-patterns

description: >

Python-specific design patterns and best practices including protocols,

dataclasses, context managers, decorators, async/await, type hints, and

package organization. Use when working with Python code to apply Pythonic

patterns.

metadata:

origin: ECC

globs: ["*/.py", "*/.pyi"]


Python Patterns

This skill provides comprehensive Python patterns extending common design principles with Python-specific idioms.

Protocol (Duck Typing)

Use Protocol for structural subtyping (duck typing with type hints):

python
from typing import Protocol

class Repository(Protocol):
    def find_by_id(self, id: str) -> dict | None: ...
    def save(self, entity: dict) -> dict: ...

# Any class with these methods satisfies the protocol
class UserRepository:
    def find_by_id(self, id: str) -> dict | None:
        # implementation
        pass

    def save(self, entity: dict) -> dict:
        # implementation
        pass

def process_entity(repo: Repository, id: str) -> None:
    entity = repo.find_by_id(id)
    # ... process

Benefits:

  • Type safety without inheritance
  • Flexible, loosely coupled code
  • Easy testing and mocking

Dataclasses as DTOs

Use dataclass for data transfer objects and value objects:

python
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class CreateUserRequest:
    name: str
    email: str
    age: Optional[int] = None
    tags: list[str] = field(default_factory=list)

@dataclass(frozen=True)
class User:
    """Immutable user entity"""
    id: str
    name: str
    email: str

Features:

  • Auto-generated __init__, __repr__, __eq__
  • frozen=True for immutability
  • field() for complex defaults
  • Type hints for validation

Context Managers

Use context managers (with statement) for resource management:

python
from contextlib import contextmanager
from typing import Generator

@contextmanager
def database_transaction(db) -> Generator[None, None, None]:
    """Context manager for database transactions"""
    try:
        yield
        db.commit()
    except Exception:
        db.rollback()
        raise

# Usage
with database_transaction(db):
    db.execute("INSERT INTO users ...")

Class-based context manager:

python
class FileProcessor:
    def __init__(self, filename: str):
        self.filename = filename
        self.file = None

    def __enter__(self):
        self.file = open(self.filename, 'r')
        return self.file

    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.file:
            self.file.close()
        return False  # Don't suppress exceptions

Generators

Use generators for lazy evaluation and memory-efficient iteration:

python
def read_large_file(filename: str):
    """Generator for reading large files line by line"""
    with open(filename, 'r') as f:
        for line in f:
            yield line.strip()

# Memory-efficient processing
for line in read_large_file('huge.txt'):
    process(line)

Generator expressions:

python
# Instead of list comprehension
squares = (x**2 for x in range(1000000))  # Lazy evaluation

# Pipeline pattern
numbers = (x for x in range(100))
evens = (x for x in numbers if x % 2 == 0)
squares = (x**2 for x in evens)

Decorators

Function Decorators

python
from functools import wraps
import time

def timing(func):
    """Decorator to measure execution time"""
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"{func.__name__} took {end - start:.2f}s")
        return result
    return wrapper

@timing
def slow_function():
    time.sleep(1)

Class Decorators

python
def singleton(cls):
    """Decorator to make a class a singleton"""
    instances = {}

    @wraps(cls)
    def get_instance(*args, **kwargs):
        if cls not in instances:
            instances[cls] = cls(*args, **kwargs)
        return instances[cls]

    return get_instance

@singleton
class Config:
    pass

Async/Await

Async Functions

python
import asyncio
from typing import List

async def fetch_user(user_id: str) -> dict:
    """Async function for I/O-bound operations"""
    await asyncio.sleep(0.1)  # Simulate network call
    return {"id": user_id, "name": "Alice"}

async def fetch_all_users(user_ids: List[str]) -> List[dict]:
    """Concurrent execution with asyncio.gather"""
    tasks = [fetch_user(uid) for uid in user_ids]
    return await asyncio.gather(*tasks)

# Run async code
asyncio.run(fetch_all_users(["1", "2", "3"]))

Async Context Managers

python
class AsyncDatabase:
    async def __aenter__(self):
        await self.connect()
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        await self.disconnect()

async with AsyncDatabase() as db:
    await db.query("SELECT * FROM users")

Type

// source originale publique
affaan-m/ECC
/.kiro/skills/python-patterns/SKILL.md
Licence : MIT
Projet indépendant, non affilié à Anthropic. Ce skill reste la propriété de son auteur original.
// installer ce skill
Collez cette commande dans votre terminal à la racine de votre projet :
mkdir -p .claude/commands && curl -o ".claude/commands/SKILL.md" "https://raw.githubusercontent.com/affaan-m/ECC/main/.kiro/skills/python-patterns/SKILL.md"
Ensuite dans Claude Code, tapez /SKILL pour l'activer.
open_in_newVoir la source originale
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// informations
Créateuraffaan-m
Étoiles 240.5k
CatégorieBackend
LicenceMIT
Mis à jour4 juin 2026
Format.md
AccèsGratuit
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