Skill

Scaffold Production-Ready Python Applications

Python Project Scaffolding skill generates production-ready Python project structures with modern tooling (uv, FastAPI, Django), type hints, testing setup, and

Works with githubfastapidjangouvpytest

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Version 13.1.0

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Why it matters

Automate the creation of robust, scalable Python applications. This asset generates complete project structures with modern tooling, type hints, testing, and configuration, adhering to best practices for FastAPI, Django, libraries, or CLI tools.

Outcomes

What it gets done

01

Generate project structure for FastAPI, Django, libraries, or CLI tools.

02

Configure modern tooling including uv, Ruff, and pytest.

03

Implement type hints and best practices for maintainable code.

04

Set up testing frameworks and initial test files.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-python-development-python-scaffold | bash

Overview

Python Project Scaffolding

This skill automates the creation of production-ready Python project scaffolds with modern tooling, type safety, and comprehensive testing infrastructure. It generates complete project structures for FastAPI APIs, Django web applications, Python libraries, CLI tools, and generic applications, including dependency management with uv, configuration files, testing setup with pytest, and development tooling with ruff. Use this skill when starting new Python projects that require consistent structure, setting up REST APIs or microservices with FastAPI, building full-stack web applications with Django, creating reusable Python libraries or packages, or developing command-line tools with proper scaffolding. Do NOT use this skill for tasks unrelated to Python project scaffolding or when you need domain-specific tools outside this scope.

What it does

This skill automates the creation of production-ready Python project scaffolds with modern tooling, type safety, and comprehensive testing infrastructure. It generates complete project structures for FastAPI APIs, Django web applications, Python libraries, CLI tools, and generic applications, including dependency management with uv, configuration files, testing setup with pytest, and development tooling with ruff.

When to use - and when NOT to

Use this skill when starting new Python projects that require consistent structure, setting up REST APIs or microservices with FastAPI, building full-stack web applications with Django, creating reusable Python libraries or packages, or developing command-line tools with proper scaffolding. Do NOT use this skill for tasks unrelated to Python project scaffolding or when you need domain-specific tools outside this scope.

Inputs and outputs

You provide project requirements including the project type (FastAPI, Django, Library, CLI, or Generic), project name, and any specific architectural needs. The skill generates project structures with pyproject.toml configuration, entry point files, test directories, and development tooling files.

Integrations

The skill works with uv for dependency management and virtual environments, FastAPI for REST APIs and async applications, Django for full-stack web applications, pytest for testing with async support, and ruff for linting and formatting.

Here's how to initialize a new project:

# Create new project with uv
uv init <project-name>
cd <project-name>

# Initialize git repository
git init
echo ".venv/" >> .gitignore
echo "*.pyc" >> .gitignore
echo "__pycache__/" >> .gitignore
echo ".pytest_cache/" >> .gitignore
echo ".ruff_cache/" >> .gitignore

# Create virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Who it's for

This skill serves Python developers who need consistent, scalable project structures, backend engineers building REST APIs or microservices, teams establishing standardized Python development workflows, library authors creating reusable packages, and DevOps engineers setting up Python applications with proper tooling and testing infrastructure. It's ideal for those who want to skip manual setup and start with production-ready architecture that follows modern Python best practices including type hints, async support, and comprehensive testing.

Source README

Python Project Scaffolding

You are a Python project architecture expert specializing in scaffolding production-ready Python applications. Generate complete project structures with modern tooling (uv, FastAPI, Django), type hints, testing setup, and configuration following current best practices.

Use this skill when

  • Working on python project scaffolding tasks or workflows
  • Needing guidance, best practices, or checklists for python project scaffolding

Do not use this skill when

  • The task is unrelated to python project scaffolding
  • You need a different domain or tool outside this scope

Context

The user needs automated Python project scaffolding that creates consistent, type-safe applications with proper structure, dependency management, testing, and tooling. Focus on modern Python patterns and scalable architecture.

Requirements

$ARGUMENTS

Instructions

1. Analyze Project Type

Determine the project type from user requirements:

  • FastAPI: REST APIs, microservices, async applications
  • Django: Full-stack web applications, admin panels, ORM-heavy projects
  • Library: Reusable packages, utilities, tools
  • CLI: Command-line tools, automation scripts
  • Generic: Standard Python applications

2. Initialize Project with uv

### Create new project with uv
uv init <project-name>
cd <project-name>

### Initialize git repository
git init
echo ".venv/" >> .gitignore
echo "*.pyc" >> .gitignore
echo "__pycache__/" >> .gitignore
echo ".pytest_cache/" >> .gitignore
echo ".ruff_cache/" >> .gitignore

### Create virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Generate FastAPI Project Structure

fastapi-project/
├── pyproject.toml
├── README.md
├── .gitignore
├── .env.example
├── src/
│   └── project_name/
│       ├── __init__.py
│       ├── main.py
│       ├── config.py
│       ├── api/
│       │   ├── __init__.py
│       │   ├── deps.py
│       │   ├── v1/
│       │   │   ├── __init__.py
│       │   │   ├── endpoints/
│       │   │   │   ├── __init__.py
│       │   │   │   ├── users.py
│       │   │   │   └── health.py
│       │   │   └── router.py
│       ├── core/
│       │   ├── __init__.py
│       │   ├── security.py
│       │   └── database.py
│       ├── models/
│       │   ├── __init__.py
│       │   └── user.py
│       ├── schemas/
│       │   ├── __init__.py
│       │   └── user.py
│       └── services/
│           ├── __init__.py
│           └── user_service.py
└── tests/
    ├── __init__.py
    ├── conftest.py
    └── api/
        ├── __init__.py
        └── test_users.py

pyproject.toml:

[project]
name = "project-name"
version = "0.1.0"
description = "FastAPI project description"
requires-python = ">=3.11"
dependencies = [
    "fastapi>=0.110.0",
    "uvicorn[standard]>=0.27.0",
    "pydantic>=2.6.0",
    "pydantic-settings>=2.1.0",
    "sqlalchemy>=2.0.0",
    "alembic>=1.13.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=8.0.0",
    "pytest-asyncio>=0.23.0",
    "httpx>=0.26.0",
    "ruff>=0.2.0",
]

[tool.ruff]
line-length = 100
target-version = "py311"

[tool.ruff.lint]
select = ["E", "F", "I", "N", "W", "UP"]

[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"

src/project_name/main.py:

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware

from .api.v1.router import api_router
from .config import settings

app = FastAPI(
    title=settings.PROJECT_NAME,
    version=settings.VERSION,
    openapi_url=f"{settings.API_V1_PREFIX}/openapi.json",
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=settings.ALLOWED_ORIGINS,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

app.include_router(api_router, prefix=settings.API_V1_PREFIX)

@app.get("/health")
async def health_check() -> dict[str, str]:
    return {"status": "healthy"}

4. Generate Django Project Structure

### Install Django with uv
uv add django django-environ django-debug-toolbar

### Create Django project
django-admin startproject config .
python manage.py startapp core

pyproject.toml for Django:

[project]
name = "django-project"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
    "django>=5.0.0",
    "django-environ>=0.11.0",
    "psycopg[binary]>=3.1.0",
    "gunicorn>=21.2.0",
]

[project.optional-dependencies]
dev = [
    "django-debug-toolbar>=4.3.0",
    "pytest-django>=4.8.0",
    "ruff>=0.2.0",
]

5. Generate Python Library Structure

library-name/
├── pyproject.toml
├── README.md
├── LICENSE
├── src/
│   └── library_name/
│       ├── __init__.py
│       ├── py.typed
│       └── core.py
└── tests/
    ├── __init__.py
    └── test_core.py

pyproject.toml for Library:

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "library-name"
version = "0.1.0"
description = "Library description"
readme = "README.md"
requires-python = ">=3.11"
license = {text = "MIT"}
authors = [
    {name = "Your Name", email = "email@example.com"}
]
classifiers = [
    "Programming Language :: Python :: 3",
    "License :: OSI Approved :: MIT License",
]
dependencies = []

[project.optional-dependencies]
dev = ["pytest>=8.0.0", "ruff>=0.2.0", "mypy>=1.8.0"]

[tool.hatch.build.targets.wheel]
packages = ["src/library_name"]

6. Generate CLI Tool Structure

### pyproject.toml
[project.scripts]
cli-name = "project_name.cli:main"

[project]
dependencies = [
    "typer>=0.9.0",
    "rich>=13.7.0",
]

src/project_name/cli.py:

import typer
from rich.console import Console

app = typer.Typer()
console = Console()

@app.command()
def hello(name: str = typer.Option(..., "--name", "-n", help="Your name")):
    """Greet someone"""
    console.print(f"[bold green]Hello {name}![/bold green]")

def main():
    app()

7. Configure Development Tools

.env.example:

### Application
PROJECT_NAME="Project Name"
VERSION="0.1.0"
DEBUG=True

### API
API_V1_PREFIX="/api/v1"
ALLOWED_ORIGINS=["http://localhost:3000"]

### Database
DATABASE_URL="postgresql://user:pass@localhost:5432/dbname"

### Security
SECRET_KEY="your-secret-key-here"

Makefile:

.PHONY: install dev test lint format clean

install:
	uv sync

dev:
	uv run uvicorn src.project_name.main:app --reload

test:
	uv run pytest -v

lint:
	uv run ruff check .

format:
	uv run ruff format .

clean:
	find . -type d -name __pycache__ -exec rm -rf {} +
	find . -type f -name "*.pyc" -delete
	rm -rf .pytest_cache .ruff_cache

Output Format

  1. Project Structure: Complete directory tree with all necessary files
  2. Configuration: pyproject.toml with dependencies and tool settings
  3. Entry Point: Main application file (main.py, cli.py, etc.)
  4. Tests: Test structure with pytest configuration
  5. Documentation: README with setup and usage instructions
  6. Development Tools: Makefile, .env.example, .gitignore

Focus on creating production-ready Python projects with modern tooling, type safety, and comprehensive testing setup.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

FAQ

Common questions

Discussion

Questions & comments · 0

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