Architecture zoo

Skill Aperivue/medsci-skills/skills/architecture-zoo

Agent Skills for medical research — literature search, reporting-guideline & citation checks, statistics, publication figures, submission. Works with Claude Code, Codex, Cursor & GitHub Copilot. Built by a physician-researcher, tested on real publications. MIT.

Install

npx -y skills add Aperivue/medsci-skills --skill architecture-zoo

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Choose a model architecture for a medical-imaging research question before scaffolding. Maps the task (classification, segmentation, detection, transfer), modality and dimensionality, labelled-data scale, and class imbalance to a shortlist of architectures, each grounded in its source paper with a when-to-use, a medical-imaging use, a reference implementation, the typical validation setup, and the matching model-scaffold template. Covers the foundational curriculum (ResNet, DenseNet, EfficientNet, ViT, Swin; U-Net, 3-D U-Net, Attention/Residual U-Net, nnU-Net (+ ResEnc/MedNeXt/STU-Net), Mask R-CNN; SAM/MedSAM, nnInteractive/VISTA3D interactive-3D, TotalSegmentator, BiomedCLIP, DINO/MAE/SimCLR; and graph neural nets — GCN/GraphSAGE/GAT/GIN/BrainGNN — for brain connectomes). It teaches archetypes and the task-to-architecture logic (including the "scale the CNN, new≠better" rigour caveat), not a live SOTA leaderboard.

SKILL.md

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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.