Flask REST API with flask-smorest Pattern

This skill helps you set up a Flask REST API following a standardized pattern with flask-smorest for OpenAPI documentation, blueprint architecture, and dataclass models for request/response handling.

When to Use This Skill

Use this skill when:

• Starting a new Flask REST API project
• You want automatic OpenAPI/Swagger documentation
• You need a clean, modular blueprint architecture
• You want type-safe data models using dataclasses with to_dict/from_dict patterns
• You're building a production-ready API server

What This Skill Creates

1. Main application file - Flask app initialization with flask-smorest
2. Blueprint structure - Modular endpoint organization
3. Data models - Dataclasses with to_dict/from_dict methods and validation
4. Singleton manager pattern - Centralized service/database initialization
5. CORS support - Cross-origin request handling
6. Requirements file - All necessary dependencies

Step 1: Gather Project Information

IMPORTANT: Before creating files, ask the user these questions:

1. "What is your project name?" (e.g., "materia-server", "trading-api", "myapp")

   • Use this to derive:
     • Main module:

       {project_name}.py

       (e.g.,

       materia_server.py

       )
     • Port number (suggest based on project, default: 5000)

2. "What features/endpoints do you need?" (e.g., "users", "tokens", "orders")

   • Each feature will become a blueprint

3. "Do you need database integration?" (yes/no)

   • If yes, reference postgres-setup skill for database layer

4. "What port should the server run on?" (default: 5000)

Step 2: Create Directory Structure

Create these directories if they don't exist:

{project_root}/
├── blueprints/          # Blueprint modules (one per feature)
│   ├── __init__.py
│   └── {feature}.py
├── models/              # Dataclass models with to_dict/from_dict
│   ├── __init__.py
│   └── {feature}.py
└── {project_name}.py    # Main application file

Step 3: Create Main Application File

Create

{project_name}.py

using this template:

import os
import logging
from flask import Flask
from flask_cors import CORS
from flask_smorest import Api
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def create_app():
    app = Flask(__name__)
    app.config['API_TITLE'] = '{Project Name} API'
    app.config['API_VERSION'] = 'v1'
    app.config['OPENAPI_VERSION'] = '3.0.2'
    app.config['OPENAPI_URL_PREFIX'] = '/'
    app.config['OPENAPI_SWAGGER_UI_PATH'] = '/swagger'
    app.config['OPENAPI_SWAGGER_UI_URL'] = 'https://cdn.jsdelivr.net/npm/swagger-ui-dist/'

    CORS(app)
    api = Api(app)

    from blueprints.{feature} import blp as {feature}_blp
    api.register_blueprint({feature}_blp)

    logger.info("Flask app initialized")
    return app

if __name__ == '__main__':
    port = int(os.environ.get('PORT', {port_number}))
    app = create_app()
    logger.info(f"Swagger UI: http://localhost:{port}/swagger")
    app.run(host='0.0.0.0', port=port)

CRITICAL: Replace:

• {Project Name}

  → Human-readable project name (e.g., "Materia Server")

• {project_name}

  → Snake case project name (e.g., "materia_server")

• {port_number}

  → Actual port number (e.g., 5151)

• {feature}

  → Feature name from user's response

Step 4: Create Data Models

For each feature, create a models file with dataclasses that include

to_dict

and

from_dict

methods:

File:

models/{feature}.py

from dataclasses import dataclass
from typing import Optional


@dataclass
class {Feature}:
    """
    {Feature} data model.

    Includes validation in from_dict and serialization via to_dict.
    """
    id: str
    name: str
    created_at: int
    updated_at: Optional[int] = None

    def to_dict(self) -> dict:
        """Serialize to dictionary for JSON response."""
        return {
            "id": self.id,
            "name": self.name,
            "created_at": self.created_at,
            "updated_at": self.updated_at
        }

    @classmethod
    def from_dict(cls, data: dict) -> "{Feature}":
        """
        Create instance from dictionary with validation.

        Args:
            data: Dictionary with {feature} data

        Returns:
            {Feature} instance

        Raises:
            ValueError: If required fields are missing or invalid
        """
        if "id" not in data:
            raise ValueError("id is required")
        if "name" not in data:
            raise ValueError("name is required")
        if "created_at" not in data:
            raise ValueError("created_at is required")

        return cls(
            id=str(data["id"]),
            name=str(data["name"]),
            created_at=int(data["created_at"]),
            updated_at=int(data["updated_at"]) if data.get("updated_at") else None
        )

CRITICAL: Replace:

• {Feature}

  → PascalCase feature name (e.g., "TradableToken")

• {feature}

  → Snake case feature name (e.g., "tradable_token")

Step 5: Create Blueprint Files

For each feature/endpoint, create a blueprint file:

File:

blueprints/{feature}.py

import logging
from flask import request, jsonify
from flask.views import MethodView
from flask_smorest import Blueprint, abort

from models.{feature} import {Feature}

logger = logging.getLogger(__name__)

blp = Blueprint('{feature}', __name__, url_prefix='/api', description='{Feature} API')


@blp.route('/{feature}')
class {Feature}ListResource(MethodView):
    def get(self):
        """Get list of {feature}s."""
        try:
            limit = request.args.get('limit', 100, type=int)
            offset = request.args.get('offset', 0, type=int)

            # TODO: Implement logic to fetch {feature}s
            items = []

            return jsonify({
                "data": [item.to_dict() for item in items],
                "limit": limit,
                "offset": offset
            })
        except ValueError as e:
            logger.warning(f"Bad request: {e}")
            abort(400, message=str(e))
        except Exception as e:
            logger.exception(f"Error fetching {feature}s: {e}")
            abort(500, message="Internal server error")

    def post(self):
        """Create a new {feature}."""
        try:
            data = request.get_json()
            if not data:
                abort(400, message="Request body is required")

            item = {Feature}.from_dict(data)

            # TODO: Implement logic to save {feature}

            return jsonify(item.to_dict()), 201
        except ValueError as e:
            logger.warning(f"Validation error: {e}")
            abort(400, message=str(e))
        except Exception as e:
            logger.exception(f"Error creating {feature}: {e}")
            abort(500, message="Internal server error")


@blp.route('/{feature}/<string:item_id>')
class {Feature}Resource(MethodView):
    def get(self, item_id: str):
        """Get a single {feature} by ID."""
        try:
            # TODO: Implement logic to fetch {feature} by ID
            item = None

            if not item:
                abort(404, message=f"{Feature} not found: {item_id}")

            return jsonify(item.to_dict())
        except Exception as e:
            logger.exception(f"Error fetching {feature}: {e}")
            abort(500, message="Internal server error")

    def put(self, item_id: str):
        """Update a {feature}."""
        try:
            data = request.get_json()
            if not data:
                abort(400, message="Request body is required")

            # TODO: Implement logic to update {feature}

            return jsonify({"message": "Updated"})
        except ValueError as e:
            logger.warning(f"Validation error: {e}")
            abort(400, message=str(e))
        except Exception as e:
            logger.exception(f"Error updating {feature}: {e}")
            abort(500, message="Internal server error")

    def delete(self, item_id: str):
        """Delete a {feature}."""
        try:
            # TODO: Implement logic to delete {feature}

            return jsonify({"message": "Deleted"})
        except Exception as e:
            logger.exception(f"Error deleting {feature}: {e}")
            abort(500, message="Internal server error")

CRITICAL: Replace:

• {Feature}

  → PascalCase feature name (e.g., "TradableToken")

• {feature}

  → Snake case feature name (e.g., "tradable_token")

Step 6: Create Common Singleton Manager (If Needed)

If the project needs shared services (database, API clients, etc.), create a singleton manager:

File:

common.py

"""
Singleton manager for shared service instances.

Provides centralized initialization of database connections, API clients,
and other shared resources.
"""

import os
import logging


logger = logging.getLogger(__name__)


class ServiceManager:
    """
    Singleton manager for shared service instances.

    Ensures only one instance of each service is created and reused
    across all blueprints.
    """

    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(ServiceManager, cls).__new__(cls)
            cls._instance._initialized = False
        return cls._instance

    def __init__(self):
        if self._initialized:
            return

        # Initialize services
        self._db = None
        self._initialized = True
        logger.info("ServiceManager initialized")

    def get_database(self):
        """
        Get database connection instance.

        Returns:
            Database connection instance (lazy initialization)
        """
        if self._db is None:
            # Import database driver
            from src.{project_name}.database import Database

            # Get connection parameters from environment
            db_host = os.environ.get('{PROJECT_NAME}_DB_HOST', 'localhost')
            db_name = os.environ.get('{PROJECT_NAME}_DB_NAME', '{project_name}')
            db_user = os.environ.get('{PROJECT_NAME}_DB_USER', '{project_name}')
            db_passwd = os.environ.get('{PROJECT_NAME}_DB_PASSWORD')

            if not db_passwd:
                raise ValueError("{PROJECT_NAME}_DB_PASSWORD environment variable required")

            self._db = Database(db_host, db_name, db_user, db_passwd)
            logger.info("Database connection initialized")

        return self._db


# Global singleton instance
service_manager = ServiceManager()

CRITICAL: Replace:

• {PROJECT_NAME}

  → Uppercase project name (e.g., "MATERIA_SERVER")

• {project_name}

  → Snake case project name (e.g., "materia_server")

Step 7: Create Environment Configuration

File:

example.env

Create or update

example.env

with required environment variables:

# Server Configuration
PORT={port_number}
DEBUG=False

# Database Configuration (if applicable)
{PROJECT_NAME}_DB_HOST=localhost
{PROJECT_NAME}_DB_NAME={project_name}
{PROJECT_NAME}_DB_USER={project_name}
{PROJECT_NAME}_DB_PASSWORD=your_password_here

# Optional: CORS Configuration
ALLOWED_ORIGINS=http://localhost:3000,http://localhost:8080

CRITICAL: Replace:

• {port_number}

  → Actual port number (e.g., 5151)

• {PROJECT_NAME}

  → Uppercase project name (e.g., "MATERIA_SERVER")

• {project_name}

  → Snake case project name (e.g., "materia_server")

File:

.env

(gitignored)

Instruct the user to copy

example.env

to

.env

and fill in actual values:

# Copy example.env to .env and update with actual values
cp example.env .env

Update .gitignore

Add

.env

to

.gitignore

if not already present:

# Environment variables
.env

Step 8: Create Requirements File

Create

requirements.txt

with dependencies (no version pinning):

# Flask and API framework
Flask
flask-smorest
flask-cors

# Environment variable management
python-dotenv

# Production server (optional but recommended)
gunicorn

# Database (if needed)
psycopg2-binary

Step 9: Create Blueprints init.py

Create

blueprints/__init__.py

:

"""
Blueprint modules for {Project Name} API.

Each blueprint represents a distinct feature or resource endpoint.
"""

Step 10: Document Usage

Create or update README.md with:

Setup

# Copy example environment file
cp example.env .env

# Edit .env and fill in actual values
# Then install dependencies
pip install -r requirements.txt

Running the Server

# Development mode
python {project_name}.py

# Production mode with Gunicorn
gunicorn -w 4 -b 0.0.0.0:{port_number} '{project_name}:create_app()'

Environment Variables

Copy

example.env

to

.env

and configure:

Server Configuration:

• PORT

  - Server port (default: {port_number})

• DEBUG

  - Enable debug mode (default: False)

Database (if applicable):

• {PROJECT_NAME}_DB_HOST

  - Database host (default: localhost)

• {PROJECT_NAME}_DB_NAME

  - Database name (default: {project_name})

• {PROJECT_NAME}_DB_USER

  - Database user (default: {project_name})

• {PROJECT_NAME}_DB_PASSWORD

  - Database password (REQUIRED)

API Documentation

Once running, access Swagger UI at:

http://localhost:{port_number}/swagger

Design Principles

This pattern follows these principles:

Architecture:

1. Blueprint Organization - Modular endpoint organization, one blueprint per feature
2. MethodView Classes - Class-based views for HTTP methods (get, post, put, delete)
3. Separation of Concerns - Routes, models, and business logic separated
4. Singleton Manager - Centralized service initialization prevents duplicate connections
5. Application Factory -

   create_app()

   pattern for testing and flexibility

Data Models:

1. Dataclasses - Type-safe data models using Python dataclasses
2. to_dict/from_dict - Consistent serialization and deserialization pattern
3. Validation in from_dict - Input validation with clear error messages
4. Self-Contained - Each model handles its own validation and serialization

API Design:

1. OpenAPI/Swagger - Automatic documentation via flask-smorest
2. Error Handling - Consistent error responses with proper HTTP status codes
3. CORS Support - Cross-origin requests for frontend consumption
4. JSON Responses - All endpoints return JSON via jsonify()

Best Practices:

1. Environment-Based Config - All secrets via environment variables
2. Logging - Structured logging throughout
3. Idempotent Operations - Safe to call multiple times
4. Production Ready - Gunicorn support out of the box
5. Testing Friendly - Application factory enables easy testing

Integration with Other Skills

Database Integration

If database is needed, use postgres-setup skill first:

User: "Set up postgres database for my project"

Then reference the database in your blueprints via the singleton manager:

from common import service_manager

db = service_manager.get_database()

Package Structure

If publishing as a package, use python-pypi-setup skill:

User: "Set up Python package for PyPI"

Example Usage in Claude Code

User: "Set up Flask API server for my project"

Claude: "What is your project name?"

User: "crypto-tracker"

Claude: "What features/endpoints do you need?"

User: "prices, tokens, portfolio"

Claude: "Do you need database integration?"

User: "yes"

Claude: "What port should the server run on?"

User: "8080"

Claude:

1. Creates

   crypto_tracker.py

   with Flask app
2. Creates

   models/

   directory with dataclass models:

   • prices.py

     ,

     tokens.py

     ,

     portfolio.py

3. Creates

   blueprints/

   directory with endpoint handlers:

   • prices.py

     ,

     tokens.py

     ,

     portfolio.py

4. Creates

   common.py

   with ServiceManager singleton
5. Creates

   requirements.txt

   with dependencies
6. Documents environment variables needed
7. Provides startup instructions

Optional: Docker Support

If user requests Docker, reference the flask-docker-deployment skill for production-ready containerization with automated versioning and health checks.