model-scaffold

name	model-scaffold
description	Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, image-to-image synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone (transfer learning) — the missing middle link between choosing an architecture and validating a trained model. Emits a patient-level seed-locked split as an auditable artifact, a task-appropriate model, train and evaluate scripts that seed every RNG and infer under eval mode, a config, requirements, a reproducibility record, and a Methods stub with VERIFY placeholders (no fabricated numbers). Fine-tuning mode adds a frozen-then-unfrozen schedule, discriminative learning rates, and a pretrained-weight provenance record. The reproducibility guarantees hold by construction, so the build is leakage-safe before any training runs. Integrates with MONAI, nnU-Net, TorchIO, timm, and torchvision — it does not reimplement them.
triggers	model scaffold, scaffold a model, training repo, PyTorch repo, build a model, train a model, fine-tune, finetune, transfer learning, pretrained backbone, MedSAM, SAM adaptation, segmentation, classification, detection, image synthesis, self-supervised, SimCLR, Pix2Pix, Faster R-CNN, U-Net, UNet, nnU-Net, MONAI, timm, torchvision, dataloader, train.py, patient-level split, reproducible training, seed everything, generate training code, medical imaging model
tools	Read, Write, Edit, Bash, Grep, Glob
model	inherit

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: /architecture-zoo (choose) → model-scaffold (build) → /model-validation (validate the split / design) → /model-evaluation + /analyze-stats (metrics) → /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 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-validation.
• Held-out metrics / calibration / bootstrap CIs → /model-evaluation then /analyze-stats.
• Choosing the architecture for the research question → /architecture-zoo (when available).
• Reimplementing MONAI / nnU-Net → out of scope (the scaffold integrates them).
• LLM / MLLM evaluation → /mllm-eval.

Workflow