Python Fastapi Todo App logo

Python Fastapi Todo App

Community
andersonamaral2
python-fastapi-todo-app

Creates a simple Todo API with FastAPI, SQLite, and basic CRUD operations.

Overview

Publisherandersonamaral2
RepositoryClaude-Code-to-Deep-Agents-Skills-Converter
Skill namepython-fastapi-todo-app
Stars
58
Forks
8
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by andersonamaral2 on GitHub. Read the source before you install it.

Installation

Install the Python Fastapi Todo App 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.
    https://github.com/andersonamaral2/Claude-Code-to-Deep-Agents-Skills-Converter/tree/main/examples/deep-agents-output
  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/andersonamaral2/Claude-Code-to-Deep-Agents-Skills-Converter.git /tmp/Claude-Code-to-Deep-Agents-Skills-Converter
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-to-Deep-Agents-Skills-Converter/examples/deep-agents-output .claude/skills/python-fastapi-todo-app
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Python Fastapi Todo App 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 Todo App 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 Todo App 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.

Python FastAPI Todo App

Creates a simple Todo API with FastAPI, SQLite, and basic CRUD operations.

Execution Context

This skill runs inside Deep Agents CLI (v0.0.34+). Available tools:

ToolUsage in this skill
write_fileCreate project files (database.py, models.py, main.py, .env, .gitignore)
executeRun shell commands (install deps, start server, run tests)
read_fileRead created files to verify content
write_todosTrack execution plan progress
http_requestTest API endpoints after server starts

Critical execution rules:

  1. Always start by creating the plan via write_todos.
  2. Create files one by one via write_file — never try to generate everything at once.
  3. Test each module via execute immediately after creating it.
  4. Use task to delegate long or parallel subtasks to sub-agents.

Execution Plan (use with write_todos)

When receiving the request, run write_todos with:

  • 1. Verify Python 3.11+ is installed
  • 2. Create project directory and virtual environment
  • 3. Install dependencies (fastapi, uvicorn, sqlalchemy)
  • 4. Create database module (app/database.py)
  • 5. Create models module (app/models.py)
  • 6. Create FastAPI application (app/main.py)
  • 7. Create .env and .gitignore files
  • 8. Start server and test all CRUD endpoints
  • 9. Run smoke tests for all endpoints

When to Use

Use this skill when the user asks to:

  • Create a todo app
  • Build a task manager API
  • Create a simple REST API with Python and FastAPI

Prerequisites Check

Before creating any files, use execute to verify:

bash
# Check Python version
python3 --version || { echo "ERROR: Python 3 not found"; exit 1; }

# Verify Python 3.11+
PYTHON_VERSION=$(python3 -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')")
echo "Python version: $PYTHON_VERSION"

# Check pip is available
python3 -m pip --version || { echo "ERROR: pip not found"; exit 1; }

If Python is not installed, use execute to install:

bash
# On macOS:
brew install python@3.11

# On Linux:
sudo apt-get update && sudo apt-get install -y python3.11 python3.11-venv

Steps

1. Setup Environment

Use execute to create the project directory and virtual environment:

bash
mkdir -p todo-api/app
cd todo-api
python3 -m venv venv
source venv/bin/activate

Use execute to install dependencies:

bash
cd todo-api && source venv/bin/activate
pip install fastapi uvicorn sqlalchemy

Test via execute:

bash
cd todo-api && source venv/bin/activate
python3 -c "import fastapi; import uvicorn; import sqlalchemy; print('All dependencies OK')"

2. Create Database Module

Use write_file to create todo-api/app/__init__.py:

python

Use write_file to create todo-api/app/database.py:

python
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

DATABASE_URL = "sqlite:///./todos.db"

engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False})
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()

Test via execute:

bash
cd todo-api && source venv/bin/activate
python3 -c "from app.database import engine, SessionLocal, Base; print('database module OK')"

3. Create Models

Use write_file to create todo-api/app/models.py:

python
from sqlalchemy import Column, Integer, String, Boolean
from .database import Base

class Todo(Base):
    __tablename__ = "todos"

    id = Column(Integer, primary_key=True, index=True)
    title = Column(String, index=True)
    description = Column(String, default="")
    completed = Column(Boolean, default=False)

Test via execute:

bash
cd todo-api && source venv/bin/activate
python3 -c "from app.models import Todo; print(f'Model OK: {Todo.__tablename__}')"

4. Create FastAPI Application

Use write_file to create todo-api/app/main.py:

python
import os
from fastapi import FastAPI, HTTPException, Depends
from sqlalchemy.orm import Session
from typing import List
from pydantic import BaseModel

from .database import SessionLocal, engine
from .models import Todo as TodoModel, Base

Base.metadata.create_all(bind=engine)

app = FastAPI()

# Dependency
def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()

class TodoCreate(BaseModel):
    title: str
    description: str = ""
    completed: bool = False

class TodoUpdate(BaseModel):
    title: str = None
    description: str = None
    completed: bool = None

class TodoResponse(BaseModel):
    id: int
    title: str
    description: str
    completed: bool

    class Config:
        from_attributes = True

@app.get("/todos", response_model=List[TodoResponse])
def list_todos(db: Session = Depends(get_db)):
    return db.query(TodoModel).all()

@app.post("/todos", response_model=TodoResponse)
def create_todo(todo: TodoCreate, db: Session = Depends(get_db)):
    db_todo = TodoModel(**todo.model_dump())
    db.add(db_todo)
    db.commit()
    db.refresh(db_todo)
    return db_todo

@app.put("/todos/{todo_id}", response_model=TodoResponse)
def update_todo(todo_id: int, todo: TodoUpdate, db: Session = Depends(get_db)):
    db_todo = db.query(TodoModel).filter(TodoModel.id == todo_id).first()
    if not db_todo:
        raise HTTPException(status_code=404, detail="Todo not found")
    
    update_data = todo.model_dump(exclude_unset=True)
    for key, value in update_data.items():
        setattr(db_todo, key, value)
    
    db.commit()
    db.refresh(db_todo)
    return db_todo

@app.delete("/todos/{todo_id}")
def delete_todo(todo_id: int, db: Session = Depends(get_db)):
    db_todo = db.query(TodoModel).filter(TodoModel.id == todo_id).first()
    if not db_todo:
        raise HTTPException(status_code=404, detail="Todo not found")
    
    db.delete(db_todo)
    db.commit()
    return {"message": "Todo deleted successfully"}

if __name__ == "__main__":
    import uvicorn
    host = os.getenv("API_HOST", "0.0.0.0")
    port = int(os.getenv("API_PORT", 8000))
    uvicorn.run(app, host=host, port=port)

Test via execute:

bash
cd todo-api && source venv/bin/activate
python3 -c "from app.main import app; print(f'FastAPI app OK: {len(app.routes)} routes')"

5. Environment Setup

Before execution, verify required environment variables via execute:

bash
# Check optional variables (with defaults)
echo "API_HOST=${API_HOST:-0.0.0.0} (default: 0.0.0.0)"
echo "API_PORT=${API_PORT:-8000} (default: 8000)"

Use write_file to create todo-api/.env:

API_HOST=0.0.0.0
API_PORT=8000

Use write_file to create todo-api/.gitignore:

venv/
__pycache__/
*.pyc
*.db
.env

6. Add Project Notes

Use write_file to create todo-api/AGENTS.md:

markdown
# Todo API — Project Notes

- Framework: FastAPI with SQLAlchemy ORM
- Database: SQLite (file: todos.db)
- No authentication required
- Environment: API_HOST, API_PORT configurable via .env
- Endpoints: GET/POST /todos, PUT/DELETE /todos/{id}

7. Test the API

Use execute to start the server in the background:

bash
cd todo-api && source venv/bin/activate
nohup python3 -m app.main > server.log 2>&1 &
echo $! > server.pid
sleep 2
echo "Server PID: $(cat server.pid)"

Use execute to run smoke tests on all endpoints:

bash
echo "=== Testing GET /todos ==="
curl -s -o /dev/null -w "%{http_code}" http://localhost:8000/todos
echo ""

echo "=== Testing POST /todos ==="
curl -s -o /dev/null -w "%{http_code}" -X POST http://localhost:8000/todos \
  -H "Content-Type: application/json" \
  -d '{"title": "Buy milk", "completed": false}'
echo ""

echo "=== Testing PUT /todos/1 ==="
curl -s -o /dev/null -w "%{http_code}" -X PUT http://localhost:8000/todos/1 \
  -H "Content-Type: application/json" \
  -d '{"completed": true}'
echo ""

echo "=== Testing DELETE /todos/1 ==="
curl -s -o /dev/null -w "%{http_code}" -X DELETE http://localhost:8000/todos/1
echo ""

echo "=== All smoke tests complete ==="

Use execute to stop the test server:

bash
cd todo-api && kill $(cat server.pid) 2>/dev/null; rm -f server.pid

Notes

  • Keep it simple, no authentication required
  • Uses SQLite to avoid external database dependencies
  • Follows FastAPI best practices with Pydantic models
  • Supports configuration via environment variables

Usage with Deep Agents CLI

Mode 1 — Build (one-shot)

bash
deepagents -y "Create a Python FastAPI Todo App following the python-fastapi-todo-app skill"

Mode 2 — Interactive

bash
deepagents
> Create a todo API with FastAPI and SQLite

Mode 3 — Non-interactive (CI/CD)

bash
deepagents -n -y -S "pip,python3,curl,mkdir" "Create a FastAPI todo app with CRUD endpoints"

Troubleshooting

Python not found

bash
# Check:
python3 --version
# Install (macOS):
brew install python@3.11
# Install (Linux):
sudo apt-get install python3.11

pip install fails

bash
# Ensure venv is activated:
source venv/bin/activate
# Upgrade pip:
python3 -m pip install --upgrade pip
# Retry:
pip install fastapi uvicorn sqlalchemy

Environment variable not set

bash
# Check what's set:
env | grep API_
# Set manually:
export API_HOST="0.0.0.0"
export API_PORT=8000
# Or load from .env:
set -a && source .env && set +a

Server won't start

bash
# Check if port is in use:
lsof -i :8000
# Check server logs:
cat server.log
# Try a different port:
export API_PORT=8001

Context window overflow

Use /compact to force compaction before continuing.
Consider splitting the task with `task` sub-agents.

Frequently asked questions

What does the Python Fastapi Todo App AI skill do?

Creates a simple Todo API with FastAPI, SQLite, and basic CRUD operations.

Why use Python Fastapi Todo App on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/andersonamaral2/Claude-Code-to-Deep-Agents-Skills-Converter/tree/main/examples/deep-agents-output. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Python Fastapi Todo App?

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 Todo App?

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

Is the Python Fastapi Todo App AI skill free?

Yes. It is published on GitHub by andersonamaral2 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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