> 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.
npx skills add https://github.com/NVIDIA/skills --skill earth2studio-install
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:
Even if the user says "just install it", give the command and require
them to execute it themselves.
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.
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.
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.
Use WebFetch on the install URL above. Extract:
@0.14.0)--no-build-isolation for pip,manual pre-installs)
Keep this data in working memory for all subsequent steps.
Ask (cap at 3 questions, skip what the user already answered):
uv and link <https://docs.astral.sh/uv/getting-started/installation/>
(currently 3.13)
Provide commands from the live docs based on their answers:
After the user runs the install, verify:
import earth2studio
earth2studio.__version__
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:
statistics)?
--extra all)Provide the exact commands from the live docs for their selections.
Key warnings to surface:
(Atlas, StormScope), torch-harmonics CUDA extensions (FCN3, SFNO)
— can take 10-30+ minutes
--no-build-isolation or pre-installing packages like earth2grid,
torch-harmonics, or makani
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 |
If installation fails, point the user to:
Common issues:
torch.cuda.is_available() firstPyTorch CUDA
sudo apt-get install libeccodes-dev (Debian/Ubuntu) or
conda install -c conda-forge eccodes
headers; install via sudo apt-get install python3-dev
data extra is out of scopeOwns: 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.
Generate Hugging Face Hub (huggingface_hub) release notes from cached PR JSON files. Use when asked to draft release notes from PR files.
Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translation to iOS/macOS/visionOS apps.
Routes any legal task to the right LLM, like OpenRouter but for legal work and grounded in benchmarks instead of brand loyalty. Built from mid-2026 legal evals (legalbenchmarks.ai, Vals AI × Stanford LegalBench across 124 models, Harvey's Legal Agent Benchmark, the Atticus Project's CUAD/MAUD/ACORD) plus translation evidence (WMT25, SwiLTra-Bench, ArabLegalEval). Covers five verticals: contract drafting, info extraction, legal research, contract review, and legal translation (including Arabic/MENA). Each asks up to four questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then returns a primary model, a fallback, what to avoid, and what a human must verify. Core principle: capability is not controllability, so every route ends with a verification step. Not legal advice; a lawyer owns the output.
Generates standalone interactive HTML "deal cards" that translate complex regulations into negotiation-ready reference tools, systematically distinguishing mandatory obligations from negotiable implementation choices. Use when the user needs an interactive regulatory guide for (1) contract negotiation support, (2) client education or internal training, (3) regulatory briefings for commercial stakeholders, or (4) structured comparison between required and flexible compliance paths. Primary focus on EU digital regulation (Data Act, AI Act, CRA, DORA, NIS2, GDPR) but the structural pattern transfers to any regulation where separating hard obligations from implementation choice is the point. Supports bilingual output where the jurisdiction calls for it.
> Pick the right LLM for CONTRACT DRAFTING — generating, redlining, or rewriting contract language from instructions. Vendor-neutral routing grounded in mid-2026 legal benchmarks (legalbenchmarks.ai Contract Drafting). Asks up to 4 quick questions (cost, speed, accuracy/ stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model should I use to draft this clause/agreement", "best AI for drafting contracts", "route this drafting task", or is about to generate/redline contract text and hasn't fixed a model.
Draft matter status reports from emails, call notes, and updates. Internal and client-facing formats, RAG logic, variance commentary, escalation flags. Use when asked to draft a status report, write a project update, summarise matter progress, prepare a client report, create a weekly or monthly update, convert emails into a status summary, or produce any kind of matter reporting. Also triggers when the user pastes email threads and asks what the status is, or needs to turn internal updates into client-facing reports.
Use as stage 2 of the Butterbase journey, after journey-idea has written 01-idea.md. Translates the idea + capability map into a concrete Butterbase plan — tables (with columns/types/RLS shape), auth providers, function list (name + trigger), storage buckets, AI/RAG/realtime/durable usage, and the chosen frontend stack. In hackathon mode, ruthlessly cuts scope into a "ship now" vs "post-hackathon" split. Produces docs/butterbase/02-plan.md.
| Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries.
Take nvidia/earth2studio-install from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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.
The instructions reference pip, uv, apt.
Without those the skill loads but fails at the first command.