Model-Scaffold Skill

Purpose

This skill stamps out a runnable PyTorch training repo for a medical-imaging task — --task segmentation (U-Net), classification (CNN / timm backbone), detection (torchvision Faster R-CNN / FPN), synthesis (Pix2Pix generator + PatchGAN), ssl (SimCLR encoder), or finetune (transfer-learning a pretrained backbone with a frozen→unfrozen schedule + a provenance record) — with the reproducibility guarantees baked in by construction — so the build is leakage-safe and reproducible before a single epoch runs. It is the imaging analogue of how /analyze-stats generates runnable statistical code: the generator produces the repo, you run the training on your GPU / Colab, and the lane's deterministic gates verify the network-free parts.

It is the missing middle link in the lane: /model-selection (choose) → model-scaffold (build) → /model-assessment (validate the split / design, compute metrics) → /analyze-stats → /write-paper + /check-reporting (publish). It integrates MONAI / nnU-Net / TorchIO (referenced in the generated requirements.txt); it does not reimplement them.