python-development-python-scaffold▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
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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.
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 protected]"}
]
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]
how to use python-development-python-scaffoldHow to use python-development-python-scaffold on Cursor
AI-first code editor with Composer
1Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add python-development-python-scaffold
2Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill python-development-python-scaffoldThe skills CLI fetches python-development-python-scaffold from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.
3Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?││ ── Universal (.agents/skills) ── always included ────│ • Amp│ • Antigravity│ • Cline│ • Codex│ ●Cursor(selected)│ • Cursor│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/python-development-python-scaffoldReload or restart Cursor to activate python-development-python-scaffold. Access the skill through slash commands (e.g., /python-development-python-scaffold) or your agent's skill management interface.
⚠Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
List & Monetize Your Skill
Submit your Claude Code skill and start earning
GET_STARTED →Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
✓Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
✓Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
✓Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.8★★★★★35 reviews- ★★★★★Mia Wang· Dec 16, 2024
python-development-python-scaffold reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chaitanya Patil· Dec 8, 2024
I recommend python-development-python-scaffold for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Piyush G· Nov 27, 2024
Useful defaults in python-development-python-scaffold — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Layla Rahman· Nov 7, 2024
python-development-python-scaffold has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Arjun Diallo· Oct 26, 2024
Useful defaults in python-development-python-scaffold — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Shikha Mishra· Oct 18, 2024
python-development-python-scaffold has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kwame Flores· Sep 21, 2024
Useful defaults in python-development-python-scaffold — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yusuf Sharma· Sep 9, 2024
python-development-python-scaffold is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Sep 1, 2024
Keeps context tight: python-development-python-scaffold is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Arjun Mensah· Aug 28, 2024
python-development-python-scaffold fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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