Claude Code for Python Developers

Python is everywhere. It powers Django apps, FastAPI microservices, data pipelines, machine learning models, automation scripts, and CLI tools. The language is versatile — but that same versatility means it's easy to write Python that technically works but fails at scale, is hard to test, or breaks when someone else touches it.

Claude Code with Python-specific skills doesn't just write Python. It writes Python the way a senior Python engineer would: with type hints, proper error handling, idiomatic patterns, and the architectural decisions that age well.

Python Skills in the SuperSkills Collection

These are the core Python skills:

python-pro — The foundation. Covers idiomatic Python: list comprehensions vs generator expressions, context managers, dataclasses, __slots__, proper exception hierarchies, and the patterns that make Python code Pythonic rather than just functional.

fastapi-expert — FastAPI from routing to deployment: dependency injection, Pydantic models, async handlers, background tasks, middleware, OAuth2 flows, and OpenAPI documentation. Knows when to use async def and when regular def is actually faster.

django-expert — Django depth: ORM optimization, custom managers, signals, middleware, the admin interface, class-based vs function-based views, Django REST Framework, and the testing patterns that actually work with Django's test runner.

pytest-master — Testing discipline: fixtures, parametrize, mocking with unittest.mock and pytest-mock, test organization, coverage configuration, and the patterns that make test suites fast and maintainable.

Type Hints That Actually Help

Python's type system is optional, which means most projects either ignore it entirely or use it inconsistently. The python-pro skill makes Claude's type annotations useful, not decorative.

Before: Vague Signatures

def process_orders(orders, config=None):
    results = []
    for order in orders:
        if validate(order, config):
            results.append(transform(order))
    return results

This compiles, runs, and does something. But what? What's orders? What does it return? What happens with config=None?

After: Self-Documenting Types

from typing import Sequence
from dataclasses import dataclass

@dataclass(frozen=True)
class ProcessingConfig:
    validate_stock: bool = True
    apply_discounts: bool = True
    max_retries: int = 3

def process_orders(
    orders: Sequence[Order],
    config: ProcessingConfig | None = None,
) -> list[ProcessedOrder]:
    active_config = config or ProcessingConfig()
    return [
        transform(order, active_config)
        for order in orders
        if validate(order, active_config)
    ]

The skill reaches for dataclass(frozen=True) for config objects (immutable, hashable, comparable), uses Sequence instead of list for the input (accepts any iterable), and returns a concrete list because the caller expects a list. These distinctions matter in real codebases.

FastAPI Services That Scale

The fastapi-expert skill changes how Claude structures FastAPI applications. Instead of flat files with route functions, you get a proper application structure.

Dependency Injection for Clean Services

from fastapi import FastAPI, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession
from typing import Annotated

app = FastAPI()

# Reusable dependency — clean and testable
async def get_db() -> AsyncSession:
    async with SessionLocal() as session:
        yield session

DBSession = Annotated[AsyncSession, Depends(get_db)]

@app.get("/users/{user_id}", response_model=UserResponse)
async def get_user(user_id: int, db: DBSession):
    user = await db.get(User, user_id)
    if not user:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail=f"User {user_id} not found"
        )
    return user

The skill knows: use Annotated for dependency types (FastAPI 0.95+ pattern), status constants instead of magic numbers, async session management with proper context managers, and response_model for automatic serialization and OpenAPI docs.

Pydantic Models Done Right

from pydantic import BaseModel, EmailStr, field_validator, model_validator
from datetime import datetime

class UserCreate(BaseModel):
    email: EmailStr
    username: str
    password: str
    birth_year: int

    @field_validator("username")
    @classmethod
    def username_valid(cls, v: str) -> str:
        if not v.replace("_", "").isalnum():
            raise ValueError("Username must be alphanumeric (underscores allowed)")
        if len(v) < 3:
            raise ValueError("Username must be at least 3 characters")
        return v.lower()

    @model_validator(mode="after")
    def check_age(self) -> "UserCreate":
        current_year = datetime.now().year
        if current_year - self.birth_year < 13:
            raise ValueError("Must be at least 13 years old")
        return self

The skill uses field_validator and model_validator (Pydantic v2 patterns), EmailStr for email validation, and demonstrates both field-level and model-level validation — patterns most developers copy from documentation but rarely get right on the first try.

Data Science Workflows

Python data science code tends to be exploratory notebooks turned into production scripts — with all the problems that entails. The python-pro skill helps Claude write data code that's actually maintainable.

Pipeline Pattern Over Sequential Scripts

from dataclasses import dataclass
from typing import Callable
import pandas as pd

@dataclass
class Pipeline:
    steps: list[tuple[str, Callable[[pd.DataFrame], pd.DataFrame]]]

    def run(self, df: pd.DataFrame) -> pd.DataFrame:
        for name, step in self.steps:
            df = step(df)
            assert not df.empty, f"Pipeline step '{name}' produced empty DataFrame"
        return df

def remove_duplicates(df: pd.DataFrame) -> pd.DataFrame:
    return df.drop_duplicates(subset=["user_id", "event_date"])

def fill_missing_values(df: pd.DataFrame) -> pd.DataFrame:
    return df.assign(
        revenue=df["revenue"].fillna(0),
        country=df["country"].fillna("unknown"),
    )

def add_derived_features(df: pd.DataFrame) -> pd.DataFrame:
    return df.assign(
        revenue_per_event=df["revenue"] / df["event_count"].clip(lower=1),
        is_high_value=df["revenue"] > df["revenue"].quantile(0.9),
    )

pipeline = Pipeline(steps=[
    ("remove_duplicates", remove_duplicates),
    ("fill_missing", fill_missing_values),
    ("feature_engineering", add_derived_features),
])

Each step is a pure function (easy to test), named steps aid debugging, the assertion catches empty DataFrames before they cause silent failures downstream.

Async Patterns

The fastapi-expert skill understands the subtleties of async Python that trip up most developers.

import asyncio
import httpx

# Wrong: sequential async calls (defeats the purpose)
async def fetch_user_data_slow(user_id: int) -> dict:
    profile = await fetch_profile(user_id)
    orders = await fetch_orders(user_id)
    preferences = await fetch_preferences(user_id)
    return {"profile": profile, "orders": orders, "preferences": preferences}

# Right: concurrent async calls with asyncio.gather
async def fetch_user_data_fast(user_id: int) -> dict:
    profile, orders, preferences = await asyncio.gather(
        fetch_profile(user_id),
        fetch_orders(user_id),
        fetch_preferences(user_id),
    )
    return {"profile": profile, "orders": orders, "preferences": preferences}

# Right: concurrent HTTP requests with connection pooling
async def bulk_fetch_prices(product_ids: list[int]) -> list[dict]:
    async with httpx.AsyncClient() as client:
        tasks = [
            client.get(f"/api/prices/{pid}")
            for pid in product_ids
        ]
        responses = await asyncio.gather(*tasks)
        return [r.json() for r in responses if r.status_code == 200]

The skill knows the difference between async def (runs in event loop) and regular def (blocking), when asyncio.gather vs asyncio.TaskGroup is appropriate, and how to properly pool HTTP connections.

Testing with pytest

The pytest-master skill produces tests that are actually useful — not just tests that pass.

Fixtures and Parametrize

import pytest
from httpx import AsyncClient
from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine

@pytest.fixture(scope="session")
def engine():
    return create_async_engine("sqlite+aiosqlite:///:memory:")

@pytest.fixture
async def db(engine) -> AsyncSession:
    async with AsyncSession(engine) as session:
        yield session
        await session.rollback()

@pytest.fixture
async def client(db) -> AsyncClient:
    app.dependency_overrides[get_db] = lambda: db
    async with AsyncClient(app=app, base_url="http://test") as client:
        yield client
    app.dependency_overrides.clear()

@pytest.mark.parametrize("email,expected_status", [
    ("valid@example.com", 200),
    ("not-an-email", 422),
    ("", 422),
    ("a" * 256 + "@example.com", 422),
])
async def test_user_creation(client, email, expected_status):
    response = await client.post("/users", json={"email": email, "username": "testuser"})
    assert response.status_code == expected_status

The skill uses scope="session" for the engine (created once), per-test db fixtures with rollback for isolation, dependency overrides for clean testing, and parametrize to test multiple input variations without code duplication.

Django ORM Optimization

The django-expert skill prevents the N+1 query problem and other common Django performance issues:

# Naive — N+1 queries (1 for orders + 1 per order for user)
orders = Order.objects.filter(status="pending")
for order in orders:
    print(f"{order.user.email}: {order.total}")  # hits DB each time

# With django-expert — 2 queries total
orders = (
    Order.objects
    .select_related("user")
    .filter(status="pending")
    .only("total", "created_at", "user__email")
    .order_by("-created_at")
)

# Many-to-many — prefetch_related, not select_related
orders = (
    Order.objects
    .prefetch_related("items__product")
    .filter(user=request.user)
    .select_related("user", "shipping_address")
)

The skill knows: select_related for ForeignKey/OneToOne (SQL JOIN), prefetch_related for ManyToMany/reverse FK (separate query), and only() to avoid loading unused fields on large tables.

Getting Started

1. Install SuperSkills to ~/.claude/skills/
2. Open your Python project in Claude Code
3. Skills activate automatically based on imports and file context — fastapi-expert kicks in when FastAPI imports are detected, pytest-master activates in test files
4. Start building: "Create a user authentication endpoint," "Write tests for this service," "Optimize this Django query"

The first time Claude writes a FastAPI endpoint with proper dependency injection, Pydantic v2 validators, async patterns, and OpenAPI docs without being prompted — that's the moment you realize why skills exist.

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