Scaffold Production-Ready Python Applications
Scaffolds production-ready Python projects - FastAPI, Django, library, or CLI - with uv, Ruff, and pytest configured from the start.
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Why it matters
Automate the creation of robust, scalable Python applications. This asset generates complete project structures with modern tooling, type hints, testing, and configuration, adhering to best practices for FastAPI, Django, libraries, or CLI tools.
Outcomes
What it gets done
Generate project structure for FastAPI, Django, libraries, or CLI tools.
Configure modern tooling including uv, Ruff, and pytest.
Implement type hints and best practices for maintainable code.
Set up testing frameworks and initial test files.
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-python-development-python-scaffold | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Python Project Scaffolding
Scaffolds complete Python project structures - FastAPI, Django, library, or CLI - with pyproject.toml, entry point, tests, and uv/Ruff/pytest tooling configured. Use when starting a new Python project that needs a consistent, type-safe structure, not for adding scaffolding to an existing project.
What it does
A scaffolding skill that generates complete, production-ready Python project structures for five project types: FastAPI (async REST APIs and microservices, with a layered src/project_name structure spanning api/v1/endpoints, core, models, schemas, and services, a CORS-enabled FastAPI app with a health endpoint, and a pyproject.toml pinning fastapi, uvicorn, pydantic, sqlalchemy, and alembic), Django (full-stack web apps scaffolded via django-admin startproject plus startapp, with django-environ, psycopg, and gunicorn dependencies), a publishable library (hatchling build backend, a py.typed marker for type-hint distribution, MIT license classifiers), a CLI tool (Typer-based with a project.scripts entry point and Rich for console output), and generic Python applications. Every project type uses uv for environment and dependency management (uv init, uv venv, uv add, uv sync, uv run) and Ruff for linting and formatting, targeting Python 3.11+. Generated tooling beyond the project structure includes a .env.example with application, API, database, and security sections, and a Makefile with install, dev, test, lint, format, and clean targets that wrap the equivalent uv run commands.
When to use - and when NOT to
Use when starting a new Python project and needing a consistent, type-safe structure with modern tooling rather than assembling pyproject.toml and config files by hand - choosing between FastAPI, Django, a library, a CLI tool, or a generic app based on the stated requirements. Not for adding scaffolding to an already-structured existing project, and not for teams standardized on pip, poetry, or pipenv instead of uv without adapting the generated commands - the scaffolding assumes uv as the package and environment manager throughout.
Inputs and outputs
Input is the target project type (FastAPI, Django, library, CLI, or generic) plus any project-specific requirements. Output is a complete directory tree, a pyproject.toml with pinned dependencies and tool configuration for Ruff and pytest, an entry point such as main.py or cli.py, a pytest test structure, a README, and development tooling including .env.example, Makefile, and .gitignore.
### 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
Integrations
Built around uv for dependency and environment management, FastAPI with Uvicorn, Pydantic, SQLAlchemy, and Alembic, Django with django-environ, psycopg, and gunicorn, Typer and Rich for CLI tools, hatchling as the library build backend, Ruff for linting and formatting, and pytest with pytest-asyncio or pytest-django for testing.
Who it's for
Python developers and teams starting a new project who want a consistent, production-ready structure - correct dependency pinning, Ruff configuration, and test setup from commit one - rather than copying scaffolding from a previous project.
FAQ
Common questions
Discussion
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