Individual skill
model-scaffold
Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
skills/model-scaffold/SKILL.md
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- Last checked
- Nov 11, 2026
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- 376
- Repository last updated
- Nov 10, 2026
- License
- MIT
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- biostatistics Repository topic
- claude-code Repository topic
- claude-skills Repository topic
- clinical-research Repository topic
- diagnostic-accuracy Repository topic
- literature-review Repository topic
- manuscript Repository topic
- medical-ai Repository topic
- medical-research Repository topic
- meta-analysis Repository topic
- engineering Derived category
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# 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)**