Architecture-Zoo Skill

Purpose

This skill turns a medical-imaging research question into a paper-grounded architecture choice — so the build starts from the right archetype (and a known validation setup) rather than from whatever is fashionable, and the choice carries its source citation into the Methods. It is the front end of the model-engineering lane: architecture-zoo (choose) → /model-scaffold (build) → /model-validation (validate).

It is advisory (Layer D): it writes a short decision note, never code or weights. The actual repo is /model-scaffold. It describes archetypes and the task → family → constraint logic, not a live SOTA leaderboard (SOTA churns; the logic does not).

When to use

• You need to pick an architecture/backbone for a classification, segmentation, detection, or transfer-learning question and want it grounded in the literature with a sensible default.

When NOT to use

• Generating the runnable repo → /model-scaffold.
• Auditing a trained model's validation design → /model-validation.
• Metrics / calibration → /model-evaluation + /analyze-stats.
• General study/validity design → /design-study; AI-vs-expert benchmark → /design-ai-benchmarking.
• LLM / MLLM → /mllm-eval.

Workflow

Phase 1 — Frame the question

State the task (classification / segmentation / detection / transfer), the modality + dimensionality (2-D vs 3-D volume), the labelled-data scale (events / structures, not just images), label availability (lots / few / unlabelled pool), and constraints (class imbalance, small structures, interpretability, deployment compute).

Phase 2 — Walk the decision tree

Open ${CLAUDE_SKILL_DIR}/references/index.md and follow task → constraints → default pick. It routes to a family card.

Phase 3 — Read the family card

• ${CLAUDE_SKILL_DIR}/references/classification.md — ResNet / DenseNet / EfficientNet / Inception / ViT / Swin / DeiT.
• ${CLAUDE_SKILL_DIR}/references/segmentation.md — U-Net / 3-D U-Net / V-Net / Attention & Residual U-Net / nnU-Net / SegResNet / Swin-UNETR / Mask R-CNN.
• ${CLAUDE_SKILL_DIR}/references/detection.md — R-CNN family / Faster R-CNN + FPN / Mask R-CNN / RetinaNet / YOLO / DETR.
• ${CLAUDE_SKILL_DIR}/references/synthesis.md — Pix2Pix / CycleGAN / SPADE / diffusion (DDPM, latent) / VAE / fastMRI reconstruction.
• ${CLAUDE_SKILL_DIR}/references/foundation_models.md — SAM / MedSAM / MedSAM2 / TotalSegmentator / SegVol / BiomedCLIP / DINO / MAE / SimCLR / MoCo.
• ${CLAUDE_SKILL_DIR}/references/graph.md — GCN / GraphSAGE / GAT / GIN / BrainGNN for brain connectomes & population graphs (integrate PyTorch Geometric / DGL; not scaffolded by model-scaffold). Each card gives the paper, core idea, when-to-use, medical-imaging use, reference implementation, and the typical validation/experiment setup for that architecture class.

Phase 4 — Write the decision note

Record decisions/architecture_choice.md: the task, the chosen architecture, its source paper, the reason against the constraints, the runner-up + why not, and the matching /model-scaffold template. Naming the source paper is mandatory; cite, never invent, any benchmark number.

Phase 5 — Hand off

Carry the decision note to /model-scaffold (instantiate the template), then /model-validation (split / validation design), /model-evaluation + /analyze-stats (metrics), and /write-paper (the Methods cite the architecture's source paper).

Anti-Hallucination

• Never recommend an architecture without naming its source paper. Every card cites the paper; the decision note must carry that citation.
• Never invent benchmark numbers or paper claims. If a number matters, cite it (verify via /search-lit); if uncertain, write [VERIFY] and ask.
• Never recommend an architecture for a modality or data scale it does not suit (e.g. a from-scratch ViT on a few hundred images, or 2-D slices for a volumetric structure) — the constraints in the decision tree exist to prevent exactly that.
• The zoo is a curated archetype map, not a current SOTA ranking — say so rather than implying a recommendation is the latest best.

Boundaries

architecture-zoo (this skill: choose, paper-grounded)
  └─ model-scaffold (build the reproducible repo from the chosen template)
       └─ model-validation -> model-evaluation -> write-paper (cite the source paper)

It does not build, train, evaluate, or rank live SOTA — it maps the research question to a defensible, paper-grounded archetype and hands the choice to /model-scaffold.