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: /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

Phase 1 — Prepare the manifest

A CSV with one row per image and a patient/subject ID column (patient_id / subject_id / case_id), plus image and label path columns. The ID column is load-bearing: the split is done at the patient level off this column.

Phase 2 — Generate the repo

python3 ${CLAUDE_SKILL_DIR}/scripts/scaffold.py \
  --manifest <manifest.csv> --task segmentation --out model_repo --seed 42 \
  --in-channels 1 --out-channels 1
# --task = segmentation | classification | detection | synthesis | ssl | finetune
#   (out-channels = num classes for classification/finetune, target channels for synthesis)
# fine-tuning a pretrained backbone (transfer learning) on collected clinical data:
python3 ${CLAUDE_SKILL_DIR}/scripts/scaffold.py \
  --manifest <manifest.csv> --task finetune --out model_repo --seed 42 \
  --out-channels <num_classes> --from-pretrained timm:resnet50.a1_in1k
#   emits PRETRAINED.md (provenance) + a frozen→unfrozen train.py with discriminative LRs;
#   record the exact pretrained source so the fine-tune is reproducible.

This writes model_repo/ with config.yaml, model.py (the task's model — U-Net / CNN / Faster R-CNN / Pix2Pix / SimCLR encoder), dataset.py (reads the frozen split), losses.py (task-appropriate), train.py, evaluate.py, requirements.txt, REPRODUCIBILITY.md, methods_stub.md, and — the key artifact — splits/split_assignment.csv + splits/split_seed.txt. The split is patient-disjoint by construction (a deterministic group split) and the emitted code seeds every RNG, sets cuDNN deterministic, builds the training loader from the train split only, and infers under model.eval() + torch.no_grad().

Phase 3 — Verify the build (network-free)

# this skill's own training-hygiene gate
python3 ${CLAUDE_SKILL_DIR}/scripts/check_training_hygiene.py --repo model_repo --strict
# the split-leakage gate (proves patient disjointness) — owned by /model-validation

Route the emitted splits/split_assignment.csv to /model-validation (check_split_leakage.py --splits model_repo/splits/split_assignment.csv --strict) for the patient-disjointness proof, and (optionally, locally with torch installed) bash ${CLAUDE_SKILL_DIR}/scripts/scaffold_challenge/verify.sh to smoke the forward pass.

Phase 4 — Plug in your data and train

Implement dataset.py's _load_image / _load_label for your modality (DICOM / NIfTI / TIFF via nibabel / pydicom / tifffile / TorchIO / MONAI transforms). For production, swap model.py for MONAI UNet / SegResNet or an nnU-Net plan (see ${CLAUDE_SKILL_DIR}/references/training_guide.md). For a fine-tuning repo (--task finetune), fill PRETRAINED.md and set the freeze schedule / discriminative learning rates (see ${CLAUDE_SKILL_DIR}/references/finetuning_guide.md, which also covers MedSAM/SAM adaptation and train-only diffusion augmentation). Run python train.py (best model selected on the val split), then python evaluate.py (predictions on the test split, touched once).

Phase 5 — Validate, evaluate, publish

Hand off to /model-validation (validation-tier + comparator + metric-selection audit), /model-evaluation + /analyze-stats (Dice + HD95/NSD with CIs), /make-figures, and /write-paper (fill the methods_stub.md [VERIFY] placeholders) + /check-reporting (CLAIM 2024 / TRIPOD+AI). For reproducibility-safe wiring of experiment tracking (W&B / MLflow), config / data / environment versioning, and the MLOps reporting checklist, see ${CLAUDE_SKILL_DIR}/references/mlops_guide.md (a wiring + reporting reference — it points to the frameworks, it does not replace them).

Runnability — honest contract

The generated repo is runnable, but runnability is not a CI guarantee. The default gates prove the network-free properties (the emitted split is patient-disjoint + seeded; the emitted training code is hygienic) by parsing the produced artifacts — no torch is executed. A torch forward-pass smoke (build + forward shape + gradients flow + reproducible loss) is a self-skipping tier in the challenge verify.sh and a documented local command; it is never counted as CI coverage of runnability.

Anti-Hallucination

• Never fabricate training or evaluation metrics. The scaffold emits [VERIFY] placeholders; every number must come from the user's executed run and from /model-evaluation + /analyze-stats.
• Never emit a split that is not patient-disjoint or not seed-locked. The generator does this by construction; do not hand-edit the split table to introduce overlap or remove the seed.
• Never claim the generated repo was trained or that it achieved a result — it is a starting point the user runs.
• If a library API, default, or architecture detail is uncertain, flag [VERIFY] and ask rather than guessing.

Deterministic gates

• scripts/scaffold.py — the generator (stdlib + numpy; deterministic given manifest + seed).
• scripts/check_training_hygiene.py — AST linter: all RNGs seeded, cuDNN deterministic, eval() + no_grad() inference, no training on a non-train split, and (fine-tuning) a recorded pretrained-weight provenance when pretrained weights are loaded (PRETRAINED_PROVENANCE_MISSING).
• scripts/scaffold_challenge/verify.sh — the build → validate chain, network-free (torch tier self-skips).

Boundaries

architecture-zoo (choose)
  └─ model-scaffold (this skill: generate the reproducible repo)
       ├─ check_training_hygiene.py   (training-code hygiene)
       ├─ model-validation            (split-leakage proof + validation design)
       ├─ model-evaluation -> analyze-stats   (metrics + CIs)
       └─ write-paper + check-reporting        (Methods stub -> compliant manuscript)