Python Flask Boilerplate Creator

What This Prompt Does

This prompt generates clean, modular Python Flask application boilerplates. It configures project folder structures, blueprints, database models (SQLAlchemy), request parsers, configurations files, and CORS settings.

System Prompt

You are a senior Python Developer.
Generate a functional Python Flask application boilerplate.
Ensure the project follows modular patterns:
1. Use blueprints to organize API routes cleanly.
2. Initialize SQLAlchemy models and database setup.
3. Configure a settings file parsing environmental values.
4. Provide a requirements.txt with pinned dependencies.
5. Include a standard README.md with startup instructions.

User Prompt Template

Generate a Flask application boilerplate with these requirements:
Application features: {APP_FEATURES}
(e.g., "user login API, file upload route, metric logging")

Database type: {DATABASE_TYPE}
(e.g., "PostgreSQL using Flask-SQLAlchemy")

Example Output

# app/__init__.py
from flask import Flask
from flask_sqlalchemy import SQLAlchemy

db = SQLAlchemy()

def create_app():
    app = Flask(__name__)
    app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite:///app.db"
    db.init_app(app)
    return app

When to Use This

Reach for this prompt when starting a new small-to-medium Flask service and you’d rather skip re-typing the same project scaffolding — blueprint structure, SQLAlchemy setup, config parsing — that every new Flask project needs. It’s especially useful for prototyping an internal tool or API quickly, where a consistent, sensible starting structure matters more than a highly customized architecture from day one.

Tips for Best Results

• List specific routes and their expected behavior in {APP_FEATURES} rather than vague feature names, so the generated boilerplate includes plausible, immediately useful route stubs rather than only empty placeholders.
• Specify the exact database (and version, if it matters for your deployment target) in {DATABASE_TYPE} — the generated SQLAlchemy configuration differs meaningfully between SQLite for local development and PostgreSQL or MySQL for production.
• Treat the output as a starting scaffold to review and adapt to your team’s actual conventions (testing setup, logging configuration, error handling patterns), not a finished, production-ready application.

Regenerating the boilerplate with progressively more specific feature descriptions across a few iterations tends to produce a noticeably better starting point than trying to specify everything perfectly in a single first attempt.

For teams standardizing on a specific project layout, saving the best generated boilerplate as an internal cookiecutter template turns a one-off prompt result into a reusable starting point for every future Flask project.

That upfront investment pays off quickly once several projects share the same reviewed, battle-tested starting structure instead of each one drifting slightly from the last.