Python Fastapi Patterns logo

Python Fastapi Patterns

Organization
aiskillstore
python-fastapi-patterns

FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.

Overview

Publisheraiskillstore
Repositorymarketplace
Skill namepython-fastapi-patterns
Stars
427
Forks
45
Bundled files
7
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.

  • 7 bundled files

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

  • Open source

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

Installation

Install the Python Fastapi Patterns 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/aiskillstore/marketplace.git /tmp/marketplace
mkdir -p .claude/skills
cp -r /tmp/marketplace/skills/0xdarkmatter/python-fastapi-patterns .claude/skills/python-fastapi-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Fastapi Patterns 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 Python Fastapi Patterns 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 Python Fastapi Patterns 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 Patterns

Modern async API development with FastAPI.

Basic Application

python
from fastapi import FastAPI
from contextlib import asynccontextmanager

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan - startup and shutdown."""
    # Startup
    app.state.db = await create_db_pool()
    yield
    # Shutdown
    await app.state.db.close()

app = FastAPI(
    title="My API",
    version="1.0.0",
    lifespan=lifespan,
)

@app.get("/")
async def root():
    return {"message": "Hello World"}

Request/Response Models

python
from pydantic import BaseModel, Field, EmailStr
from datetime import datetime

class UserCreate(BaseModel):
    """Request model with validation."""
    name: str = Field(..., min_length=1, max_length=100)
    email: EmailStr
    age: int = Field(..., ge=0, le=150)

class UserResponse(BaseModel):
    """Response model."""
    id: int
    name: str
    email: EmailStr
    created_at: datetime

    model_config = {"from_attributes": True}  # Enable ORM mode

@app.post("/users", response_model=UserResponse, status_code=201)
async def create_user(user: UserCreate):
    db_user = await create_user_in_db(user)
    return db_user

Path and Query Parameters

python
from fastapi import Query, Path
from typing import Annotated

@app.get("/users/{user_id}")
async def get_user(
    user_id: Annotated[int, Path(..., ge=1, description="User ID")],
):
    return await fetch_user(user_id)

@app.get("/users")
async def list_users(
    skip: Annotated[int, Query(ge=0)] = 0,
    limit: Annotated[int, Query(ge=1, le=100)] = 10,
    search: str | None = None,
):
    return await fetch_users(skip=skip, limit=limit, search=search)

Dependency Injection

python
from fastapi import Depends
from typing import Annotated

async def get_db():
    """Database session dependency."""
    async with async_session() as session:
        yield session

async def get_current_user(
    token: Annotated[str, Depends(oauth2_scheme)],
    db: Annotated[AsyncSession, Depends(get_db)],
) -> User:
    """Authenticate and return current user."""
    user = await authenticate_token(db, token)
    if not user:
        raise HTTPException(status_code=401, detail="Invalid token")
    return user

# Annotated types for reuse
DB = Annotated[AsyncSession, Depends(get_db)]
CurrentUser = Annotated[User, Depends(get_current_user)]

@app.get("/me")
async def get_me(user: CurrentUser):
    return user

Exception Handling

python
from fastapi import HTTPException
from fastapi.responses import JSONResponse

# Built-in HTTP exceptions
@app.get("/items/{item_id}")
async def get_item(item_id: int):
    item = await fetch_item(item_id)
    if not item:
        raise HTTPException(status_code=404, detail="Item not found")
    return item

# Custom exception handler
class ItemNotFoundError(Exception):
    def __init__(self, item_id: int):
        self.item_id = item_id

@app.exception_handler(ItemNotFoundError)
async def item_not_found_handler(request, exc: ItemNotFoundError):
    return JSONResponse(
        status_code=404,
        content={"detail": f"Item {exc.item_id} not found"},
    )

Router Organization

python
from fastapi import APIRouter

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

@router.get("/")
async def list_users():
    return []

@router.get("/{user_id}")
async def get_user(user_id: int):
    return {"id": user_id}

# main.py
from app.routers import users, items

app.include_router(users.router)
app.include_router(items.router, prefix="/api/v1")

Quick Reference

FeatureUsage
Path param@app.get("/items/{id}")
Query paramdef f(q: str = None)
Bodydef f(item: ItemCreate)
DependencyDepends(get_db)
AuthDepends(get_current_user)
Response modelresponse_model=ItemResponse
Status codestatus_code=201

Additional Resources

  • ./references/dependency-injection.md - Advanced DI patterns, scopes, caching
  • ./references/middleware-patterns.md - Middleware chains, CORS, error handling
  • ./references/validation-serialization.md - Pydantic v2 patterns, custom validators
  • ./references/background-tasks.md - Background tasks, async workers, scheduling

Scripts

  • ./scripts/scaffold-api.sh - Generate API endpoint boilerplate

Assets

  • ./assets/fastapi-template.py - Production-ready FastAPI app skeleton

See Also

Prerequisites:

  • python-typing-patterns - Pydantic models and type hints
  • python-async-patterns - Async endpoint patterns

Related Skills:

  • python-database-patterns - SQLAlchemy integration
  • python-observability-patterns - Logging, metrics, tracing middleware
  • python-pytest-patterns - API testing with TestClient

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 Python Fastapi Patterns AI skill do?

FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.

Why use Python Fastapi Patterns on TypingMind?

Because you install it once and use it with any model. Python Fastapi Patterns 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 Python Fastapi Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiskillstore/marketplace/tree/main/skills/0xdarkmatter/python-fastapi-patterns. 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 Python Fastapi Patterns?

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 Python Fastapi Patterns?

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

Is the Python Fastapi Patterns AI skill free?

It is published on GitHub by aiskillstore. Check the repository for licensing terms. 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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