python-project
Modern Python project architecture guide for 2025. Use when creating Python projects (APIs, CLI, data pipelines). Covers uv, Ruff, Pydantic, FastAPI, and async patterns.
git clone --depth 1 https://github.com/majiayu000/spellbook /tmp/python-project && cp -r /tmp/python-project/skills/python-project ~/.claude/skills/python-projectSKILL.md
# Python Project Architecture
## Core Principles
- **Type hints everywhere** — Pydantic for runtime, mypy for static
- **uv for everything** — Package management, virtualenv, Python version
- **Ruff only** — Replace Flake8 + Black + isort with single tool
- **src layout** — All code under `src/` directory
- **pyproject.toml only** — No setup.py, no requirements.txt
- **Async all the way** — Once async, stay async through call chain
- **No backwards compatibility** — Delete, don't deprecate. Change directly
- **LiteLLM for LLM APIs** — Use LiteLLM proxy for all LLM integrations
---
## No Backwards Compatibility
> **Delete unused code. Change directly. No compatibility layers.**
```python
# ❌ BAD: Deprecated decorator kept around
import warnings
def old_function():
warnings.warn("Use new_function instead", DeprecationWarning)
return new_function()
# ❌ BAD: Alias for renamed functions
new_name = old_name # "for backwards compatibility"
# ❌ BAD: Unused parameters with underscore
def process(_legacy_param, data):
...
# ❌ BAD: Version checking for old behavior
if version < "2.0":
# old behavior
...
# ✅ GOOD: Just delete and update all usages
def new_function():
...
# Then: Find & replace all old_function → new_function
# ✅ GOOD: Remove unused parameters entirely
def process(data):
...
```
---
## LiteLLM for LLM APIs
> **Use LiteLLM proxy. Don't call provider APIs directly.**
```python
# src/myapp/llm.py
from openai import AsyncOpenAI
from myapp.config import settings
# Connect to LiteLLM proxy using OpenAI SDK
client = AsyncOpenAI(
base_url=settings.litellm_url, # "http://localhost:4000"
api_key=settings.litellm_api_key,
)
async def complete(prompt: str, model: str = "gpt-4o") -> str:
"""Call any LLM through LiteLLM proxy."""
response = await client.chat.completions.create(
model=model, # "gpt-4o", "claude-3-opus", "gemini-pro", etc.
messages=[{"role": "user", "content": prompt}],
)
return response.choices[0].message.content or ""
```
---
## Quick Start
### 1. Initialize Project
```bash
# Install uv (if not installed)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create new project
uv init myapp
cd myapp
# Set Python version
echo "3.12" > .python-version
# Add dependencies
uv add fastapi uvicorn pydantic sqlalchemy httpx
uv add --dev pytest pytest-asyncio ruff mypy
```
### 2. Apply Tech Stack
| Layer | Recommendation |
|-------|----------------|
| Package Manager | uv |
| Linting + Format | Ruff |
| Type Checking | mypy |
| Validation | Pydantic v2 |
| Web Framework | FastAPI |
| Database | SQLAlchemy 2.0 + asyncpg |
| HTTP Client | httpx |
| Testing | pytest + pytest-asyncio |
| Logging | structlog |
### Version Strategy
> **Always use latest. Never pin in templates.**
```toml
[project]
dependencies = [
"fastapi", # uv resolves to latest
"pydantic",
"sqlalchemy",
]
```
- `uv add` fetches latest compatible versions
- `uv.lock` ensures reproducible builds
- `uv sync` installs exact locked versions
### 3. Use Standard Structure (src layout)
```
myapp/
├── pyproject.toml # Single config file
├── uv.lock # Lock file (commit this)
├── .python-version # Python version for uv
├── src/
│ └── myapp/
│ ├── __init__.py
│ ├── __main__.py # Entry point
│ ├── main.py # FastAPI app
│ ├── config.py # Pydantic Settings
│ ├── models/ # Pydantic models
│ │ ├── __init__.py
│ │ └── user.py
│ ├── services/ # Business logic
│ │ ├── __init__.py
│ │ └── user.py
│ ├── repositories/ # Data access
│ │ ├── __init__.py
│ │ └── user.py
│ ├── api/ # HTTP layer
│ │ ├── __init__.py
│ │ ├── deps.py # Dependencies
│ │ └── routes/
│ │ ├── __init__.py
│ │ └── user.py
│ └── core/ # Shared utilities
│ ├── __init__.py
│ ├── exceptions.py
│ └── logging.py
├── tests/
│ ├── __init__.py
│ ├── conftest.py # Fixtures
│ └── test_user.py
└── Makefile
```
---
## Architecture Layers
### main.py — FastAPI Application
```python
# src/myapp/main.py
from contextlib import asynccontextmanager
from fastapi import FastAPI
from myapp.api.routes import router
from myapp.config import settings
from myapp.core.logging import setup_logging
from myapp.db import engine
@asynccontextmanager
async def lifespan(app: FastAPI):
# Startup
setup_logging()
yield
# Shutdown
await engine.dispose()
app = FastAPI(
title=settings.app_name,
lifespan=lifespan,
)
app.include_router(router, prefix="/api/v1")
@app.get("/health")
async def health():
return {"status": "ok"}
```
### config.py — Pydantic Settings
```python
# src/myapp/config.py
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
model_config = SettingsConfigDict(
env_file=".env",
env_file_encoding="utf-8",
)
app_name: str = "myapp"
debug: bool = False
# Database
database_url: str = "postgresql+asyncpg://localhost/myapp"
# LiteLLM
litellm_url: str = "http://localhost:4000"
litellm_api_key: str = ""
settings = Settings()
```
### models/ — Pydantic Models
```python
# src/myapp/models/user.py
from datetime import datetime
from uuid import UUID
from pydantic import BaseModel, EmailStr, Field
class UserBase(BaseModel):
email: EmailStr
name: str = Field(min_length=2, max_length=100)
class UserCreate(UserBase):
pass
class UserUpdate(BaseModel):
email: EmailStr | None = None
name: str | None = Field(default=None, min_length=2, max_length=100)
class User(UserBase):
id: UUID
created_at: datetime
updated_at: datetime
model_config = {"from_attributes": True}
```
### services/ — Business Logic
```python
# src/myapp/services/user.py
from uSenior backend TypeScript architect specializing in Bun/Node.js runtime, API design, database optimization, and scalable server architecture.
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Fullstack code reviewer with 15+ years experience analyzing code for security vulnerabilities, performance bottlenecks, architectural decisions, and best practices.
Senior technical lead who analyzes complex projects and coordinates multi-step development tasks. Delegates to specialized agents and ensures quality delivery.
Use when the user explicitly asks to stage all current changes, create a commit, and push to the remote after safety checks.