What problem does it solve? Setting up or fixing a reproducible research environment is error-prone: wrong package managers, broken shebangs after moving machines, mixed R package tools, and missing system dependencies all cause lost debugging time. This Skill guides the creation and repair of project environments for Python, R, and mixed-language research projects. ## Core Features & Use Cases - Tool selection guidance: Chooses pixi for system/compiled dependencies, uv for pure-Python projects, and rv or renv for R packages without mixing them. - Pixi scaffolding: Initializes pixi.toml with channels, platforms, tasks, lockfile commits, and feature environments for conflicting dependency versions. - Pitfall prevention: Documents real failure modes such as deno_task_shell dollar expansion in R one-liners, non-relocatable copied pixi environments, and stale path dependencies requiring pixi reinstall. - Use Case: When moving a mixed Python/R project to a new machine, use this Skill to rebuild the environment from the lockfile with pixi install, sync R packages with rv sync, and verify tests pass from a clean checkout. ## Quick Start Set up a pixi-based environment for my research project with Python 3.12, pytest, and a committed lockfile.
