python-project
This Claude Code skill provides a modern Python project architecture template for 2025, emphasizing type safety, async patterns, and streamlined tooling. Use it when starting new Python projects like APIs, CLIs, or data pipelines to establish conventions around uv for package management, Ruff for linting and formatting, Pydantic for validation, and LiteLLM for unified LLM API access while avoiding backwards compatibility layers.
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 uAudit AND optimize a CLAUDE.md / AGENTS.md instruction file — score it against the five high-leverage patterns, flag anti-patterns, then apply approved fixes in place. Use when the user says 优化 CLAUDE.md / 优化 AGENTS.md / optimize my agent doc / 帮我改 claudemd, or after an audit when they want the fixes applied (not just reported).
Generate or update repository-specific AGENTS.md instruction files from real repo evidence. Use when asked to create, design, scaffold, split, or improve root or scoped AGENTS.md files for Codex/Claude/agent workflows, especially when a repo needs directory-specific rules, validation commands, generated-file boundaries, or a short agent onboarding router.
REST/GraphQL/gRPC API design best practices. Use when designing APIs, defining contracts, handling versioning. Covers OpenAPI 3.2, GraphQL Federation, gRPC streaming.
Mobile app UI design expert for iOS and Android. Use when designing app interfaces, creating design systems, ensuring accessibility, or following platform guidelines. Covers Material Design 3, Human Interface Guidelines, color theory, typography, and 2025 trends.
End-to-end app feature inventory and user-story testing workflow with a canonical tracker. Use when the user asks to audit every feature, derive expected behavior from code, test user journeys, or explicitly fix and retest documented UX or logistical defects.
Design architecture foundations before implementation. Use when asked to design or refactor architecture, choose Rust/Go crate, package, module, runtime, workflow, or service boundaries, compare mature project architecture, prevent stacked one-off PRs, audit migration debt in existing repos, or produce a spec before coding.
Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.
Ask Grok or Gemini via opencli (browser-session based, no API cost). Use when the user says "问 grok", "问 gemini", "ask grok", "ask gemini", "用 grok 查", "grok 怎么看", "让 gemini 分析", or wants a second opinion from Grok/Gemini without paying API tokens. Routes to opencli which drives the already-logged-in Chrome session.