When you're trying to reproduce an AI research repo and need to set up the environment before running anything, this handles the tedious bootstrap work. It generates conservative conda commands, maps out where checkpoints and datasets should live, and identifies missing dependencies before you waste time on failed runs. The skill focuses specifically on the setup phase after you've picked a reproduction target but before executing any model code. It's honest about what it can't resolve automatically and flags asset sourcing problems early. One solid approach to avoid the usual "missing CUDA toolkit discovered 3 hours into training" surprises.
claude skill add lllllllama/ai-paper-reproduction-skill:env-and-assets-bootstrap