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Earth2studio Install

NVIDIAOfficialCreated by NVIDIA Earth-2 Team

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk

From the author

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.
— the description by NVIDIA Earth-2 Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill assists users in the correct installation and configuration of the Earth2Studio framework. It focuses on providing accurate, up-to-date installation commands for various environments using uv or pip, helping users select appropriate model extras, and setting up necessary environment variables. The skill strictly adheres to a manual installation policy, requiring users to execute all commands themselves to ensure safety and transparency.

What it does

  • Provides verified installation commands for Earth2Studio based on live documentation.
  • Guides users in selecting relevant optional extras for prognostic, diagnostic, and data assimilation tasks.
  • Assists in configuring environment variables for cache and timeout management.
  • Verifies successful installation through Python environment checks.
  • Offers troubleshooting guidance for common build issues like CUDA mismatches or missing system dependencies.

When to use it

  • When you need to install Earth2Studio for the first time in a new or existing project.
  • When you need to add specific model extras to an existing Earth2Studio installation.
  • When you encounter environment configuration issues related to cache or data paths.
  • When you need to verify that your current Python environment is correctly set up for Earth2Studio.

Advantages

  • Ensures installation commands are aligned with the latest official documentation.
  • Prevents unauthorized system modifications by requiring user-led execution.
  • Helps manage complex dependency requirements for specialized model extras.
  • Provides clear pathways for troubleshooting common build and environment errors.

Usage examples

“How do I install Earth2Studio using uv?”
The agent will fetch the latest installation instructions from the official documentation, recommend the appropriate uv command, and guide you through the verification process.
“I need to add the GraphCast model to my Earth2Studio setup. What should I do?”
The agent will identify the correct extra for GraphCast, provide the specific installation command, and warn you about potential build times or manual pre-requisites.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/earth2studio-install/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip earth2studio-install.zip -d .agents/skills

Documentation by NVIDIA Earth-2 Team

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:

  1. Identify it

  2. Provide the exact command

  3. Explain why it is needed

  4. 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):

  1. Package manager — uv (recommended) or pip? If unsure, recommend uv and link https://docs.astral.sh/uv/getting-started/installation/

  2. Project context — new project or adding to existing?

  3. 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:

CategoryExample 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
Submodulesdata, perturbation, statistics

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

Ask:

  1. Which models do you plan to use?

  2. Do you need submodule extras (data sources, perturbation methods, statistics)?

  3. 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):

VariablePurpose
EARTH2STUDIO_CACHEGeneral cache directory
EARTH2STUDIO_DATA_CACHEData source cache (overrides general)
EARTH2STUDIO_MODEL_CACHEModel checkpoint cache (overrides general)
EARTH2STUDIO_PACKAGE_TIMEOUTMax seconds for model downloads

Troubleshooting

If installation fails, point the user to:

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.

Files

BENCHMARK.md

Evaluation Report

Evaluation of the earth2studio-install skill before publication through NVSkills-Eval.

This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the skill. The goal is to document whether the skill is safe, discoverable, effective, and useful for agents before it is published for broader workflow use.

Evaluation Summary

  • Skill: earth2studio-install

  • Evaluation date: 2026-05-29

  • NVSkills-Eval profile: external

  • Environment: local

  • Dataset: 4 evaluation tasks

  • Attempts per task: 2

  • Pass threshold: 50%

  • Overall verdict: PASS

Agents Used

  • claude-code

  • codex

Metrics Used

Reported benchmark dimensions:

  • Security: checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.

  • Correctness: checks whether the agent follows the expected workflow and produces the correct final output.

  • Discoverability: checks whether the agent loads the skill when relevant and avoids using it when irrelevant.

  • Effectiveness: checks whether the agent performs measurably better with the skill than without it.

  • Efficiency: checks whether the agent uses fewer tokens and avoids redundant work.

Underlying evaluation signals used in this run:

  • security (Security): checks for unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): verifies that the agent loaded the expected skill and workflow.

  • skill_efficiency (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage.

  • accuracy (Accuracy): grades final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): checks whether the overall user task completed successfully.

  • behavior_check (Behavior Check): verifies expected behavior steps, including safety expectations.

  • token_efficiency (Token Efficiency): compares token usage with and without the skill.

Test Tasks

The benchmark dataset contained 4 evaluation tasks:

  • Positive tasks: 4 tasks where the skill was expected to activate.

  • Negative tasks: 0 tasks where no skill was expected.

  • Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred.

Task composition is derived from the evaluation dataset when possible. Entries with expected_skill set are treated as positive skill-activation cases, while entries with expected_skill: null are treated as negative activation cases.

Results

DimensionNumclaude-codecodex
Security8100% (+0%)100% (+0%)
Correctness897% (+3%)88% (+5%)
Discoverability888% (+0%)50% (-4%)
Effectiveness880% (+2%)92% (+8%)
Efficiency873% (-0%)43% (-3%)

Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.

Tier 1: Static Validation Summary

Tier 1 validation passed with observations. NVSkills-Eval ran 9 checks and found 2 total findings.

Top findings:

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/earth2studio-install/SKILL.md)

  • LOW SCHEMA/author_format: Author must be of the form 'Name <email@host>' (skills/earth2studio-install/SKILL.md)

Tier 2: Deduplication Summary

Tier 2 validation passed. NVSkills-Eval ran 2 checks and found 0 total findings.

Notable observations:

  • Context Deduplication: Collected 1 file(s)

  • Inter-Skill Deduplication: Parsed skill 'earth2studio-install': 183 char description

Publication Recommendation

The skill is suitable to proceed toward NVSkills-Eval publication based on this benchmark. Skill owners should keep this file with the skill and refresh it when the evaluation dataset, skill behavior, or target agents materially change.

Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Names Claude Code tools, Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Earth-2 Team
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/earth2studio-install · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA Earth-2 Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download earth2studio-install.zip