Python Project Scaffolding

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/python-development-python-scaffold

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

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.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Context, Requirements, Output Format, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Working on python project scaffolding tasks or workflows
• Needing guidance, best practices, or checklists for python project scaffolding
• The task is unrelated to python project scaffolding
• You need a different domain or tool outside this scope
• Use when the request clearly matches the imported source intent: 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.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. FastAPI: REST APIs, microservices, async applications
2. Django: Full-stack web applications, admin panels, ORM-heavy projects
3. Library: Reusable packages, utilities, tools
4. CLI: Command-line tools, automation scripts
5. Generic: Standard Python applications
6. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
7. Read the overview and provenance files before loading any copied upstream support files.

Imported Workflow Notes

Imported: 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

Imported: 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.

Examples

Example 1: Ask for the upstream workflow directly

Use @python-development-python-scaffold to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @python-development-python-scaffold against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @python-development-python-scaffold for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @python-development-python-scaffold using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/python-development-python-scaffold

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @prompt-engineer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-engineering

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-engineering-patterns

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-library

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Requirements

$ARGUMENTS

Imported: 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.

Imported: 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.