claudekit/website/src/content/docs/skills/languages/python.md

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Python	Modern Python development with type hints, async, and best practices

The Python skill provides expertise in modern Python development including type hints, async patterns, dataclasses, and Pythonic idioms.

When Activated

• Working with Python files (.py)
• Writing Python scripts or applications
• Using Python frameworks (Django, FastAPI, Flask)
• Data processing and automation

Core Patterns

Type Hints

from typing import Optional, List, Dict, Union
from collections.abc import Callable

def process_items(
    items: List[str],
    callback: Callable[[str], None],
    config: Optional[Dict[str, Any]] = None
) -> List[str]:
    """Process items with optional callback."""
    return [callback(item) for item in items]

Async/Await

import asyncio
from typing import List

async def fetch_data(url: str) -> dict:
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.json()

async def fetch_all(urls: List[str]) -> List[dict]:
    return await asyncio.gather(*[fetch_data(url) for url in urls])

Context Managers

from contextlib import contextmanager

@contextmanager
def managed_resource():
    resource = acquire_resource()
    try:
        yield resource
    finally:
        release_resource(resource)

# Usage
with managed_resource() as r:
    r.do_something()

Dataclasses

from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class User:
    id: int
    email: str
    name: str
    created_at: datetime = field(default_factory=datetime.now)

    def __post_init__(self):
        self.email = self.email.lower()

Pydantic Models

from pydantic import BaseModel, EmailStr, Field

class UserCreate(BaseModel):
    email: EmailStr
    name: str = Field(min_length=1, max_length=100)
    password: str = Field(min_length=8)

    class Config:
        str_strip_whitespace = True

Best Practices

1. Use type hints for all public functions
2. Use dataclasses or Pydantic for data models
3. Prefer context managers for resource management
4. Use async for I/O-bound operations
5. Follow PEP 8 style guidelines

Common Pitfalls

Mutable Default Arguments

# ❌ BAD: Mutable default
def add_item(item, items=[]):
    items.append(item)
    return items

# ✅ GOOD: Use None and initialize
def add_item(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items

Not Closing Resources

# ❌ BAD: Manual resource management
file = open('data.txt')
data = file.read()
file.close()

# ✅ GOOD: Use with statement
with open('data.txt') as file:
    data = file.read()

Blocking in Async

# ❌ BAD: Blocking call in async function
async def process():
    result = expensive_cpu_work()  # Blocks event loop
    return result

# ✅ GOOD: Run in thread pool
async def process():
    loop = asyncio.get_event_loop()
    result = await loop.run_in_executor(None, expensive_cpu_work)
    return result

Catching Bare Exceptions

# ❌ BAD: Catching everything
try:
    risky_operation()
except:
    handle_error()

# ✅ GOOD: Specific exceptions
try:
    risky_operation()
except (ValueError, TypeError) as e:
    handle_error(e)

Integration with Frameworks

With FastAPI

See FastAPI skill for API development patterns.

With Django

See Django skill for web application patterns.

With pytest

See pytest skill for testing patterns.

Related Skills

• FastAPI - REST API development
• Django - Web applications
• pytest - Testing framework
• PostgreSQL - Database integration