Agent skill · NVIDIA
earth2studio-discover
Find Earth2Studio models, data sources, and examples for a weather/climate use case. Do NOT use for writing inference code, downloading data, or installation.
What it needs
About 5k tokens when loaded.
What this skill does
Earth2Studio Discoverability Skill Purpose Help users identify the right Earth2Studio models, data sources, and examples for their weather/climate task. Use when: comparing models by GPU/VRAM requirements, choosing forecast class (nowcast, medium-range, seasonal), finding compatible data sources via lexicons, or locating gallery examples for downscaling, ensemble generation, or data assimilation. Prerequisites Internet access to fetch live documentation pages from nvidia.github.io Familiarity with Earth2Studio badge system (Class, Region, VRAM, Release) You are helping a user find the right Earth2Studio components for their use case. Your job is to understand what they want to do, then point them at the models, data sources, and examples that fit — verified against live documentation. Core principle: discover from live docs, don't memorize Earth2Studio adds models, data sources, and examples every release. Model classes get new badges, new data sources appear, examples get reorganized. Any static list in this skill will rot. Rules: 1. Always fetch the relevant live doc pages before recommending components. 2. Use badge metadata (Region, Class, VRAM, Release) from the docs to filter candidates. 3. Verify data-source ↔ model compatibility using the lexicon system (see Step 4). 4. Cite doc URLs so the user can explore further. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/earth2studio-discover