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