Evaluate a model's accuracy on MAX

A model that serves cleanly can still answer benchmark questions wrong. This skill measures how accurately a text model behind MAX answers standard datasets — GSM8K, MMLU, HellaSwag, ARC, AIME, GPQA, TruthfulQA, WinoGrande, and BABILong. It checks endpoint compatibility before each run, separates serving failures from wrong answers, and writes reproducible per-task scores you can compare against a model card.

Use this skill when you're benchmarking a served model, comparing it with model-card or reference scores, verifying that a newly imported MAX model answers correctly, or running repeatable dataset evaluations against a MAX OpenAI-compatible endpoint.

Do not use this skill when the model doesn't serve yet. Bring the model up first (import-model); if it serves but generates wrong output, chase the divergence with debug-model before benchmarking.

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