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Fastapi Expert

CommunityPopular
Jeffallan
fastapi-expert

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.

Overview

PublisherJeffallan
Repositoryclaude-skills
Skill namefastapi-expert
Stars
11.5K
Forks
1.1K
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Jeffallan on GitHub. Read the source before you install it.

Installation

Install the Fastapi Expert AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/fastapi-expert .claude/skills/fastapi-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fastapi Expert in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Fastapi Expert on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Fastapi Expert is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

FastAPI Expert

Deep expertise in async Python, Pydantic V2, and production-grade API development with FastAPI.

When to Use This Skill

  • Building REST APIs with FastAPI
  • Implementing Pydantic V2 validation schemas
  • Setting up async database operations
  • Implementing JWT authentication/authorization
  • Creating WebSocket endpoints
  • Optimizing API performance

Core Workflow

  1. Analyze requirements — Identify endpoints, data models, auth needs
  2. Design schemas — Create Pydantic V2 models for validation
  3. Implement — Write async endpoints with proper dependency injection
  4. Secure — Add authentication, authorization, rate limiting
  5. Test — Write async tests with pytest and httpx; run pytest after each endpoint group and verify OpenAPI docs at /docs

Checkpoint after each step: confirm schemas validate correctly, endpoints return expected HTTP status codes, and /docs reflects the intended API surface before proceeding.

Minimal Complete Example

Schema + endpoint + dependency injection in one cohesive unit:

python
# schemas.py
from pydantic import BaseModel, EmailStr, field_validator, model_config

class UserCreate(BaseModel):
    model_config = model_config(str_strip_whitespace=True)

    email: EmailStr
    password: str
    name: str | None = None

    @field_validator("password")
    @classmethod
    def password_strength(cls, v: str) -> str:
        if len(v) < 8:
            raise ValueError("Password must be at least 8 characters")
        return v

class UserResponse(BaseModel):
    model_config = model_config(from_attributes=True)

    id: int
    email: EmailStr
    name: str | None = None
python
# routers/users.py
from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession
from typing import Annotated

from app.database import get_db
from app.schemas import UserCreate, UserResponse
from app import crud

router = APIRouter(prefix="/users", tags=["users"])

DbDep = Annotated[AsyncSession, Depends(get_db)]

@router.post("/", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
async def create_user(payload: UserCreate, db: DbDep) -> UserResponse:
    existing = await crud.get_user_by_email(db, payload.email)
    if existing:
        raise HTTPException(status_code=status.HTTP_409_CONFLICT, detail="Email already registered")
    return await crud.create_user(db, payload)
python
# crud.py
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.models import User
from app.schemas import UserCreate
from app.security import hash_password

async def get_user_by_email(db: AsyncSession, email: str) -> User | None:
    result = await db.execute(select(User).where(User.email == email))
    return result.scalar_one_or_none()

async def create_user(db: AsyncSession, payload: UserCreate) -> User:
    user = User(email=payload.email, hashed_password=hash_password(payload.password), name=payload.name)
    db.add(user)
    await db.commit()
    await db.refresh(user)
    return user

JWT Authentication Snippet

python
# security.py
from datetime import datetime, timedelta, timezone
from jose import JWTError, jwt
from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from typing import Annotated

SECRET_KEY = "read-from-env"  # use os.environ / settings
ALGORITHM = "HS256"
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/auth/token")

def create_access_token(subject: str, expires_delta: timedelta = timedelta(minutes=30)) -> str:
    payload = {"sub": subject, "exp": datetime.now(timezone.utc) + expires_delta}
    return jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM)

async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)]) -> str:
    try:
        data = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
        subject: str | None = data.get("sub")
        if subject is None:
            raise ValueError
        return subject
    except (JWTError, ValueError):
        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid credentials")

CurrentUser = Annotated[str, Depends(get_current_user)]

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Pydantic V2references/pydantic-v2.mdCreating schemas, validation, model_config
SQLAlchemyreferences/async-sqlalchemy.mdAsync database, models, CRUD operations
Endpointsreferences/endpoints-routing.mdAPIRouter, dependencies, routing
Authenticationreferences/authentication.mdJWT, OAuth2, get_current_user
Testingreferences/testing-async.mdpytest-asyncio, httpx, fixtures
Django Migrationreferences/migration-from-django.mdMigrating from Django/DRF to FastAPI

Constraints

MUST DO

  • Use type hints everywhere (FastAPI requires them)
  • Use Pydantic V2 syntax (field_validator, model_validator, model_config)
  • Use Annotated pattern for dependency injection
  • Use async/await for all I/O operations
  • Use X | None instead of Optional[X]
  • Return proper HTTP status codes
  • Document endpoints (auto-generated OpenAPI)

MUST NOT DO

  • Use synchronous database operations
  • Skip Pydantic validation
  • Store passwords in plain text
  • Expose sensitive data in responses
  • Use Pydantic V1 syntax (@validator, class Config)
  • Mix sync and async code improperly
  • Hardcode configuration values

Output Templates

When implementing FastAPI features, provide:

  1. Schema file (Pydantic models)
  2. Endpoint file (router with endpoints)
  3. CRUD operations if database involved
  4. Brief explanation of key decisions

Knowledge Reference

FastAPI, Pydantic V2, async SQLAlchemy, Alembic migrations, JWT/OAuth2, pytest-asyncio, httpx, BackgroundTasks, WebSockets, dependency injection, OpenAPI/Swagger

Documentation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Fastapi Expert AI skill do?

Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.

Why use Fastapi Expert on TypingMind?

Because you install it once and use it with any model. Fastapi Expert is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Fastapi Expert in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jeffallan/claude-skills/tree/main/skills/fastapi-expert. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Fastapi Expert?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Fastapi Expert?

As many as you like. As long as a model supports skills, you can use Fastapi Expert with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Fastapi Expert AI skill free?

Yes. It is published on GitHub by Jeffallan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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