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.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpython-development-python-scaffoldExecute the skills CLI command in your project's root directory to begin installation:
Fetches python-development-python-scaffold from sickn33/antigravity-awesome-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate python-development-python-scaffold. Access via /python-development-python-scaffold in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
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Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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.
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.
$ARGUMENTS
Determine the project type from user requirements:
# 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
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"}
# 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",
]
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"]
# pyproject.toml
[project.scripts]
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
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate 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
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Backendtag: pythonReviews
4.8★★★★★35 reviews- MMia Wang★★★★★Dec 16, 2024
python-development-python-scaffold reduced setup friction for our internal harness; good balance of opinion and flexibility.
- CChaitanya 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.
- PPiyush 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.
- LLayla Rahman★★★★★Nov 7, 2024
python-development-python-scaffold has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AArjun 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.
- SShikha Mishra★★★★★Oct 18, 2024
python-development-python-scaffold has been reliable in day-to-day use. Documentation quality is above average for community skills.
- KKwame 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.
- YYusuf Sharma★★★★★Sep 9, 2024
python-development-python-scaffold is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- RRahul 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.
- AArjun 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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