Python Development Python Scaffold
Python Development Python Scaffold is an code AI skill with a core value of You are a Python project architecture expert specializing in scaffolding production-ready Python applications. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
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
Quick Facts
mkdir -p ./skills/python-development-python-scaffold && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/python-development-python-scaffold/SKILL.md -o ./skills/python-development-python-scaffold/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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\activate3. 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
# Insta🎯 Best For
- Developers scaffolding new projects
- Prototype builders
- Claude users
- Software engineers
- Development teams
💡 Use Cases
- Bootstrapping React components
- Creating API route handlers
- Python code quality enforcement
- Dependency management
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Python Development Python Scaffold to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Can I customize the generated output?
Yes — modify the skill's prompt instructions to match your project conventions and coding style.
Is Python Development Python Scaffold compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Python Development Python Scaffold?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Python Development Python Scaffold?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/python-development-python-scaffold/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Using generated code without understanding
Understand what generated code does before shipping it to production.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.