Individual skill
model-selection
Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.
skills/model-selection/SKILL.md
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- 373
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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
- research Derived category
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Read excerpt
# Model-Selection Skill
Two questions, in this order. Phases 1–3 answer a literature question with a stable answer: which
architecture **family** suits the task. Phases 4–6 answer a provenance question: which concrete
**artifact** — repository, revision, checkpoint — will be run, and what its numbers may claim.
The skill writes decision notes and runs one stdlib gate; it never downloads, runs, trains or
benchmarks a model, and it describes archetypes, not a live SOTA leaderboard.
Elsewhere: building the repo → `/model-scaffold`; profiling data and planning preprocessing →
`/imaging-data`; validation design, metrics, uncertainty, explainability → `/model-assessment`;
documenting a model you built → `/model-card`; an LLM/MLLM, including benchmark contamination in
that setting → `/mllm-eval`; AI-vs-expert reader study → `/design-ai-benchmarking`.
## Workflow