Cookiecutter PyPackage is a cookiecutter template that generates a modern Python package project with production-ready tooling. It produces a src-layout package with a Typer CLI, pytest tests, Ruff linting, ty type checking, uv dependency management, and a justfile task runner. The generated project includes security-hardened GitHub Actions workflows for CI, PyPI publishing via Trusted Publishers, documentation deployment with Zensical, CodeQL and zizmor security scanning, and Dependabot for dependency updates. The template itself is a Python package with a CLI wrapper around cookiecutter, a development watcher for live template iteration, and a comprehensive post-generation hook that optionally sets up a GitHub repository, enables Pages, and creates the PyPI environment. The codebase demonstrates strong engineering practices: comprehensive test coverage, clear separation of concerns, and thoughtful security defaults. It is actively maintained with recent commits and a well-documented changelog.
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.
This repository is a Cookiecutter template for generating Dash component projects. It provides a structured starting point for creating custom Dash components, including Python package structure, React component code, build scripts (webpack), a demo application, and tests. Users run the template via `cookiecutter`, answer prompts about their project (name, component type, licensing), and receive a fully configured project ready for development. The template employs Jinja2 templating to conditionally include files based on user choices, such as asynchronous component loading or class/function-based React components. It includes pre-generation and post-generation hooks to validate user input and automate setup tasks like creating a virtual environment, installing dependencies, and running the initial build. Generated projects follow Dash's conventions for component distribution, including proper packaging for PyPI and NPM. Notably, the template includes a sophisticated webpack configuration implementing code splitting for asynchronous chunks and shared common chunks, optimizing bundle loading. The template also includes validation scripts to ensure the generated project's build configuration is correct. Tests verify the generated project can be built and the component renders correctly in a Dash application. The repository is actively maintained with recent commits and ongoing issue activity.