architecture-zoo

# 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 ...