mcpbeat

Earth2studio Install

nvidia/earth2studio-install

> Guide installing Earth2Studio via uv or pip, selecting model extras, and configuring the environment. Do NOT use for writing inference code, choosing models, or PhysicsNeMo questions.

6k tokens
context cost
the whole folder, loaded on every use
5
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instructions only
0
copies elsewhere
how many repositories repackaged it
1056
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/NVIDIA/skills --skill earth2studio-install

What comes with it

15 952 bytes besides the instruction
BENCHMARK.md
evals/evals.json
skill-card.md
skill.oms.sig

What it tells the agent to use

found in the instruction text
WebFetch fetches pages from the network

The instruction itself

15 sections, as written by the author

Earth2Studio Installation Skill

Never install packages automatically

You MUST NOT install, upgrade, or modify packages on the user's

behalf. Provide the exact command; the user runs it. No exceptions.

Forbidden: running pip install, uv pip install, uv add,

uv sync, conda install, apt install, or any package manager.

Instead: give the exact command and ask the user to run it.

Explain why the package is needed.

When a package is needed:

  • Identify it
  • Provide the exact command
  • Explain why it is needed
  • Wait for the user to confirm they ran it

Even if the user says "just install it", give the command and require

them to execute it themselves.

Purpose

Help users install Earth2Studio and its optional model dependencies correctly for

their use case. This skill handles package installation, optional-extra selection,

environment variable configuration, and install verification.

Prerequisites

  • Python 3.10+ (3.13 recommended)
  • CUDA-capable GPU with compatible drivers for GPU extras
  • uv (recommended) or pip package manager
  • Internet access (packages installed from PyPI and GitHub)

You are helping a user install Earth2Studio and its optional model

dependencies. Your only job is to get the package installed correctly

for their use case — do not write inference code, do not compose

workflows.

Core principle: docs are the source of truth

Earth2Studio installation commands, version tags, and extra names change

between releases. **Before executing or recommending any install command,

fetch the live installation docs:**

https://nvidia.github.io/earth2studio/userguide/about/install.html

Parse the page for the current version tag, available extras, and any

special build notes. The workflow below is structural guidance — the

specific commands come from the live page.

Instructions

Step 1. Fetch live docs

Use WebFetch on the install URL above. Extract:

  • Current release version tag (e.g. @0.14.0)
  • Available optional extras by category
  • Known build quirks (e.g. --no-build-isolation for pip,

manual pre-installs)

Keep this data in working memory for all subsequent steps.

Step 2. Understand the user's environment

Ask (cap at 3 questions, skip what the user already answered):

  • Package manager — uv (recommended) or pip? If unsure, recommend

uv and link <https://docs.astral.sh/uv/getting-started/installation/>

  • Project context — new project or adding to existing?
  • Python version — recommend the version from the docs

(currently 3.13)

Step 3. Base install

Provide commands from the live docs based on their answers:

  • uv uses a git source (not PyPI) to handle URL-based transitive dependencies
  • pip installs from PyPI but some extras require manual pre-install steps

After the user runs the install, verify:

import earth2studio
earth2studio.__version__

Step 4. Select models and extras

Present the available extras organized by use case. Ask what the user

plans to do — don't dump all options unprompted. Categories from the

docs:

| Category | Example extras |

|----------|---------------|

| Prognostic (forecasting) | aifs, aurora, graphcast, pangu, sfno, stormcast, ... |

| Diagnostic (post-processing) | corrdiff, climatenet, precip-afno, ... |

| Data assimilation (beta) | da-healda, da-interp, da-stormcast |

| Submodules | data, perturbation, statistics |

The exact list comes from the live docs — cite those, not this table.

Ask:

  • Which models do you plan to use?
  • Do you need submodule extras (data sources, perturbation methods,

statistics)?

  • Or install everything? (uv only: --extra all)

Step 5. Install selected extras

Provide the exact commands from the live docs for their selections.

Key warnings to surface:

  • Slow builds: flash-attention (AIFS variants), natten

(Atlas, StormScope), torch-harmonics CUDA extensions (FCN3, SFNO)

— can take 10-30+ minutes

  • pip-specific manual steps: some models require

--no-build-isolation or pre-installing packages like earth2grid,

torch-harmonics, or makani

  • Data assimilation models: require CuPy + cuDF (CUDA 12)

Step 6. Configuration (offer, don't force)

Mention environment variables the user might want to set — only if

relevant (e.g. limited disk, shared filesystem, CI environment):

| Variable | Purpose |

|----------|---------|

| EARTH2STUDIO_CACHE | General cache directory |

| EARTH2STUDIO_DATA_CACHE | Data source cache (overrides general) |

| EARTH2STUDIO_MODEL_CACHE | Model checkpoint cache (overrides general) |

| EARTH2STUDIO_PACKAGE_TIMEOUT | Max seconds for model downloads |

Troubleshooting

If installation fails, point the user to:

  • <https://nvidia.github.io/earth2studio/userguide/support/troubleshooting.html>
  • <https://nvidia.github.io/earth2studio/userguide/support/faq.html>

Common issues:

  • PyTorch/CUDA mismatch: verify torch.cuda.is_available() first
  • flash-attention build failure: CUDA toolkit version must match

PyTorch CUDA

  • ONNX Runtime GPU: may need version-specific install for their CUDA
  • ecCodes missing: required for GRIB data handling; install via

sudo apt-get install libeccodes-dev (Debian/Ubuntu) or

conda install -c conda-forge eccodes

  • Python.h: No such file or directory: missing Python development

headers; install via sudo apt-get install python3-dev

Limitations

  • Cannot help with runtime errors unrelated to missing dependencies
  • Does not cover model checkpoint downloads (those happen at first inference)
  • Data source setup beyond the data extra is out of scope
  • Cannot write inference or training code, or compose Earth2Studio workflows

Ownership and out-of-scope

Owns: package installation, optional-extra selection, environment

variable configuration, install verification.

Does not own: writing inference or training code, composing

Earth2Studio workflows, data source setup beyond the data extra,

model checkpoint downloads (those happen at runtime), troubleshooting

runtime errors unrelated to missing dependencies.

How to use it

Copy the folder

Take nvidia/earth2studio-install from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip, uv, apt. Without those the skill loads but fails at the first command.