Agent skill · Data & Analytics

earth2studio-deterministic-forecast

Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexships scriptsApache-2.0
Install
npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 12
SKILL.md size: 5 KB
Bundled scripts: yes
Version: 0.16.0
Declared author: NVIDIA Earth-2 Team
Path: skills/earth2studio-deterministic-forecast/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Earth2Studio Deterministic Forecast Skill Guide users through building deterministic (single-member) weather forecast inference scripts using `earth2studio.run.deterministic`. ## Prerequisites - Earth2Studio installed with CUDA-capable GPU - Python 3.10+, network access for model weights and data ## Live Doc References Fetch relevant docs to verify current APIs before recommending components: |

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About this skill
What does the earth2studio-deterministic-forecast skill do?

Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.

How do I install it?

Run `npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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