SuperNav keeps a pretrained multimodal LLM focused on interpreting requests, understanding scenes and making decisions, while delegating motion execution to navigation tools — no navigation-specific fine-tuning of the model itself. It combines Navigation Skills, agent-oriented tools for physical interaction, and task-progress and context management, tied together by a unified visual-point interface that lets the model specify destinations directly in images and revise decisions from execution feedback. The authors report it outperforms four evaluated baselines on instance-level, multi-object and demand-driven tasks, with category-level HM3D evaluation and a real quadruped robot deployment.