python-development-python-scaffold

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 hint

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Best use case

python-development-python-scaffold is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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 hint

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 hint

Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.

Practical example

Example input

Use the "python-development-python-scaffold" skill to help with this workflow task. Context: 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 hint

Example output

A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.

When to use this skill

  • Use this skill when you want a reusable workflow rather than writing the same prompt again and again.

When not to use this skill

  • Do not use this when you only need a one-off answer and do not need a reusable workflow.
  • Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/python-development-python-scaffold/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/python-development-python-scaffold/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/python-development-python-scaffold/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How python-development-python-scaffold Compares

Feature / Agentpython-development-python-scaffoldStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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 hint

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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

```bash
# 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**:
```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**:
```python
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

```bash
# 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**:
```toml
[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**:
```toml
[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

```python
# 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**:
```python
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**:
```env
# 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**:
```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.

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