model-scaffold

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.

When to use

• You have a data manifest (one row per image, with a patient/subject ID) and want a reproducible, leakage-safe starting repo for a segmentation, classification, detection, synthesis or self-supervised model.
• You want to fine-tune a pretrained backbone (transfer learning — the common clinician workflow: a timm / MONAI / MedSAM checkpoint adapted to your collected clinical data) with the freeze schedule, discriminative learning rates, and pretrained-weight provenance recorded (--task finetune).

When NOT to use

• Auditing an already-trained model's validation design → /model-assessment.
• Held-out metrics / calibration / bootstrap CIs → /model-assessment then /analyze-stats.
• Choosing the architecture for the research question → /model-selection.
• Reimplementing MONAI / nnU-Net → out of scope (the scaffold integrates them).
• LLM / MLLM evaluation → /mllm-eval.