Project Setup & Dependency Management

Every reliable Python CLI starts with a foundation you rarely think about again: a clean pyproject.toml, a reproducible dependency lockfile, an isolated environment, and a release process that won't surprise you at 2 a.m. This track walks you from an empty directory to a packaged tool that installs the same way on every machine.

If you're starting a brand-new CLI, read the guides in the order below. If you're hardening an existing project, jump straight to the topic you're fighting with today.

Project setup pipeline: Scaffold, Dependencies, Isolation, Quality gates, Version & release — from an empty directory to a shipped CLI.

What this track covers

Choosing and driving your toolchain

uv for Python CLI dependency management — fast lockfile resolution, environment creation, and PEP 621 pyproject.toml workflows with the tool that has become the default for new projects. Includes a head-to-head: uv init vs poetry init for CLI tools.
Poetry workflows for CLI development — lockfile management, script entry points, dependency groups, and publishing to PyPI when your team is already standardized on Poetry.

Isolation and reproducibility

Python CLI environment isolation best practices — use venv, uv, and pyenv to keep dependencies conflict-free and execution reproducible across development and CI. Then go deeper on managing virtual environments for cross-platform CLIs across Linux, macOS, and Windows.

Scaffolding, quality gates, and releases

CLI project scaffolding with Cookiecutter — generate production-ready project skeletons so every new tool starts with the same layout, packaging, and conventions.
Pre-commit hooks for CLI projects — wire up ruff, mypy, and pytest gates that run before every commit, with a step-by-step setup guide for Python CLI repos.
Managing CLI versioning & changelogs — semantic versioning, bump-my-version, and automated changelog generation from Conventional Commits with git-cliff.

A recommended path

Pick a dependency manager — start with uv unless your team already runs Poetry.
Lock down environment isolation so builds are reproducible.
Capture the layout in a Cookiecutter template so the next project is free.
Add pre-commit gates before the codebase grows.
Automate versioning and changelogs before your first release.

Once the foundation is in place, move on to designing the command surface in Modern Python CLI Frameworks & Architecture, and polishing how your tool reads input and talks back in Advanced Input Parsing & User Experience.