python-backend

Production-ready Python backend patterns for FastAPI, SQLAlchemy, and Upstash.

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "python-backend" with this command: npx skills add jiatastic/open-python-skills/jiatastic-open-python-skills-python-backend

python-backend

Production-ready Python backend patterns for FastAPI, SQLAlchemy, and Upstash.

When to Use This Skill

  • Building REST APIs with FastAPI

  • Implementing JWT/OAuth2 authentication

  • Setting up SQLAlchemy async databases

  • Integrating Redis/Upstash caching and rate limiting

  • Refactoring AI-generated Python code

  • Designing API patterns and project structure

Core Principles

  • Async-first - Use async/await for I/O operations

  • Type everything - Pydantic models for validation

  • Dependency injection - Use FastAPI's Depends()

  • Fail fast - Validate early, use HTTPException

  • Security by default - Never trust user input

Quick Patterns

Project Structure

src/ ├── auth/ │ ├── router.py # endpoints │ ├── schemas.py # pydantic models │ ├── models.py # db models │ ├── service.py # business logic │ └── dependencies.py ├── posts/ │ └── ... ├── config.py ├── database.py └── main.py

Async Routes

BAD - blocks event loop

@router.get("/") async def bad(): time.sleep(10) # Blocking!

GOOD - runs in threadpool

@router.get("/") def good(): time.sleep(10) # OK in sync function

BEST - non-blocking

@router.get("/") async def best(): await asyncio.sleep(10) # Non-blocking

Pydantic Validation

from pydantic import BaseModel, EmailStr, Field

class UserCreate(BaseModel): email: EmailStr username: str = Field(min_length=3, max_length=50, pattern="^[a-zA-Z0-9_]+$") age: int = Field(ge=18)

Dependency Injection

async def get_current_user(token: str = Depends(oauth2_scheme)) -> User: payload = decode_token(token) user = await get_user(payload["sub"]) if not user: raise HTTPException(401, "User not found") return user

@router.get("/me") async def get_me(user: User = Depends(get_current_user)): return user

SQLAlchemy Async

from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine

engine = create_async_engine(DATABASE_URL, pool_pre_ping=True) SessionLocal = async_sessionmaker(engine, expire_on_commit=False)

async def get_session() -> AsyncGenerator[AsyncSession, None]: async with SessionLocal() as session: yield session

Redis Caching

from upstash_redis import Redis

redis = Redis.from_env()

@app.get("/data/{id}") def get_data(id: str): cached = redis.get(f"data:{id}") if cached: return cached data = fetch_from_db(id) redis.setex(f"data:{id}", 600, data) return data

Rate Limiting

from upstash_ratelimit import Ratelimit, SlidingWindow

ratelimit = Ratelimit( redis=Redis.from_env(), limiter=SlidingWindow(max_requests=10, window=60), )

@app.get("/api/resource") def protected(request: Request): result = ratelimit.limit(request.client.host) if not result.allowed: raise HTTPException(429, "Rate limit exceeded") return {"data": "..."}

Reference Documents

For detailed patterns, see:

Document Content

references/fastapi_patterns.md

Project structure, async, Pydantic, dependencies, testing

references/security_patterns.md

JWT, OAuth2, password hashing, CORS, API keys

references/database_patterns.md

SQLAlchemy async, transactions, eager loading, migrations

references/upstash_patterns.md

Redis, rate limiting, QStash background jobs

Resources

  • FastAPI Documentation

  • SQLAlchemy 2.0 Documentation

  • Upstash Documentation

  • Pydantic Documentation

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Coding

arxiv-paper-writer

Use this skill whenever the user wants Claude Code to write, scaffold, compile, debug, or review an arXiv-style academic paper, especially survey papers with LaTeX, BibTeX citations, TikZ figures, tables, and PDF output. This skill should trigger for requests like writing a full paper, creating an arXiv paper project, turning a research topic into a LaTeX manuscript, reproducing the Paper-Write-Skill-Test agent-survey workflow, or setting up a Windows/Linux Claude Code paper-writing loop.

Archived SourceRecently Updated
Coding

cli-proxy-troubleshooting

排查 CLI Proxy API(codex-api-proxy)的配置、认证、模型注册和请求问题。适用场景包括:(1) AI 请求报错 unknown provider for model, (2) 模型列表中缺少预期模型, (3) codex-api-key/auth-dir 配置不生效, (4) CLI Proxy 启动后 AI 无法调用, (5) 认证成功但请求失败或超时。包含源码级排查方法:模型注册表架构、认证加载链路、 SanitizeCodexKeys 规则、常见错误的真实根因。

Archived SourceRecently Updated
Coding

visual-summary-analysis

Performs AI analysis on input video clips/image content and generates a smooth, natural scene description. | 视觉摘要智述技能,对传入的视频片段/图片内容进行AI分析,生成一段通顺自然的场景描述内容

Archived SourceRecently Updated
Coding

frontend-skill

全能高级前端研发工程师技能。擅长AI时代前沿技术栈(React最新 + shadcn/ui + Tailwind CSS v4 + TypeScript + Next.js),精通动效库与交互特效开发。采用Glue Code风格快速实现代码,强调高质量产品体验与高度友好的UI视觉规范。在组件调用、交互特效、全局Theme上保持高度规范:绝不重复造轮子,相同逻辑出现两次即封装为组件。具备安全意识,防范各类注入攻击。开发页面具有高度自适应能力,响应式设计贯穿始终。当用户无特殊技术栈要求时,默认采用主流前沿技术栈。

Archived SourceRecently Updated