python-backend

jiatastic/open-python-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jiatastic/open-python-skills --skill python-backend
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summary

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

  • Covers async-first REST API development with FastAPI, including dependency injection, Pydantic validation, and structured project organization
  • Implements authentication patterns for JWT/OAuth2, password hashing, CORS, and API key management
  • Provides SQLAlchemy async database setup with transactions, eager loading, and migration strategies
  • Includes Redis/Upstash caching, rate limiting with s
skill.md

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

  1. Async-first - Use async/await for I/O operations
  2. Type everything - Pydantic models for validation
  3. Dependency injection - Use FastAPI's Depends()
  4. Fail fast - Validate early, use HTTPException
  5. 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

how to use python-backend

How to use python-backend on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add python-backend
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jiatastic/open-python-skills --skill python-backend

The skills CLI fetches python-backend from GitHub repository jiatastic/open-python-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/python-backend

Reload or restart Cursor to activate python-backend. Access the skill through slash commands (e.g., /python-backend) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.473 reviews
  • Ishan Chen· Dec 28, 2024

    python-backend reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aditi Diallo· Dec 20, 2024

    Keeps context tight: python-backend is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chen Kapoor· Dec 16, 2024

    We added python-backend from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Noor Anderson· Dec 16, 2024

    python-backend fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Dec 4, 2024

    I recommend python-backend for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Charlotte Bhatia· Dec 4, 2024

    We added python-backend from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Oshnikdeep· Nov 23, 2024

    Solid pick for teams standardizing on skills: python-backend is focused, and the summary matches what you get after install.

  • Charlotte Ghosh· Nov 23, 2024

    python-backend fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Mei Reddy· Nov 19, 2024

    python-backend has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Rahul Santra· Nov 15, 2024

    Useful defaults in python-backend — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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