Cookiecutter is a command-line utility for creating projects from templates (cookiecutters). It uses Jinja2 to templatize file and directory names, prompts users for input, and generates a customized project structure. The tool supports local and remote templates (via Git/Hg), includes a hook system for customizing the generation process, and provides a Python API for programmatic use. Architecture-wise, it is a modular Python package with distinct components for CLI, configuration, environment setup, template discovery, generation, hooks, prompting, replay, VCS operations, and utilities. Key entry points are the `cookiecutter` CLI command and the `cookiecutter.main.cookiecutter()` function. The system follows a data flow where template location is resolved, context is gathered (from defaults, user config, and prompts), templates are rendered with Jinja2, and output is written to a directory, optionally executing user-defined hooks at various stages.
This is a comprehensive Cookiecutter template for generating production-ready Python projects. It is not a running application itself but a project generator that produces applications, libraries, or services based on user-selected features. The template includes sophisticated post-generation hooks that surgically remove unused files and configure dependencies, Docker Compose services, CI/CD workflows, and documentation based on the chosen options. It enforces modern Python practices: uv for package management, Ruff for linting/formatting, mypy for type checking, pytest for testing, and Pydantic Settings for configuration. The generated projects can include FastAPI, Celery, SQLAlchemy, QuasiQueue, aiocache, Jinja2, Typer CLI, Docker, and GitHub Actions. The template itself demonstrates high craftsmanship with clean Jinja2 templating, conditional logic, and a well-organized file structure. It is actively maintained and uses current tooling (uv, Ruff, etc.), indicating low technical drift.
django-init is a cookiecutter template for generating Django-based REST API projects. It provides a comprehensive starting point with Django 4.1, Python 3.9, Poetry dependency management, Django REST Framework, and optional features like Celery, GraphQL, ASGI, Docker, and various deployment configurations. The template generates projects with custom user models, JWT authentication, proper API versioning, and testing setup. What distinguishes this template is the quality of its reusable components in the base/ directory, particularly the custom DRF mixins for multiple serializers and per-action permissions, specialized routers for singleton resources, and thoughtful exception handling and response classes that demonstrate deep understanding of Django REST Framework's architecture.
Cookiecutter Data Science (CCDS) is a CLI tool that generates standardized, opinionated project scaffolds for data science work. It wraps the cookiecutter templating engine with custom prompting logic that supports nested configuration choices (e.g., cloud storage options with sub-fields for bucket names), and post-generation hooks that dynamically write dependency files in multiple formats (requirements.txt, pyproject.toml, environment.yml, Pipfile, pixi.toml), configure linting/formatting tools (ruff or flake8+black+isort), set up testing frameworks (pytest/unittest), and scaffold code modules with boilerplate for data processing, feature engineering, modeling, and visualization. The tool is designed to be run via the `ccds` command, which defaults to the DrivenData template repository and the installed version of the template. The codebase demonstrates solid software engineering: it monkey-patches cookiecutter's internals in a controlled way to add functionality (prompting with sub-options, custom context file selection), provides a suite of reusable hook utilities for dependency management and custom config injection, and maintains extensive test coverage across multiple operating systems and environment managers (conda, virtualenv, pipenv, uv, pixi, poetry). The architecture is a thin CLI application that delegates to cookiecutter while extending it through monkey-patching and post-generation hooks. It is not a data research project itself but a tool for creating such projects.