Skill Inspector
NVIDIA/SkillSpector
Decides whether an agent skill is safe to install by combining a SkillSpector static scan with the agent's own source review, ending in APPROVE, CAUTION or REJECT.
Find Earth2Studio models, data sources, and examples for a weather/climate use case.
OfficialApache-2.0Auto-check passed
Install Earth2studio Discover
$ npx skills add NVIDIA/skills --skill earth2studio-discover -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills earth2studio-discover --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/earth2studio-discover .claude/skills/earth2studio-discover && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "earth2studio-discover" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-discover into .claude/skills/earth2studio-discover/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-discover", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-discoverType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill earth2studio-discover -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills earth2studio-discover --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/earth2studio-discover .agents/skills/earth2studio-discover && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "earth2studio-discover" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-discover into .agents/skills/earth2studio-discover/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-discover", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill earth2studio-discover -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills earth2studio-discover --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/earth2studio-discover .cursor/skills/earth2studio-discover && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "earth2studio-discover" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-discover into .cursor/skills/earth2studio-discover/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-discover", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/earth2studio-discover--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill earth2studio-discover -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills earth2studio-discover --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/earth2studio-discover .gemini/skills/earth2studio-discover && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "earth2studio-discover" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-discover into .gemini/skills/earth2studio-discover/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-discover", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills earth2studio-discoverInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill earth2studio-discover -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/earth2studio-discover .github/skills/earth2studio-discover && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "earth2studio-discover" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-discover into .github/skills/earth2studio-discover/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-discover", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill earth2studio-discover -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills earth2studio-discover --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/earth2studio-discover .opencode/skills/earth2studio-discover && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "earth2studio-discover" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-discover into .opencode/skills/earth2studio-discover/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-discover", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
earth2studio-discover- GitHub stars
- 3.5k
- Token cost
- ~2.1k tokens
- SKILL.md length
- 894 words
- Files
- 5
- Skills in repo
- 380
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Find Earth2Studio models, data sources, and examples for a weather/climate use case.
- Works in 6 steps: Understand the user's problem → Fetch relevant model docs → Fetch relevant data source docs → …
- Writing inference code
- SKILL.md covers Purpose, Prerequisites, Core principle: discover from… and Live doc references, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Earth2studio Discover is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Find Earth2Studio models, data sources, and examples for a weather/climate use case. Do NOT use for writing inference code, downloading data, or installation.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).
It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- Writing inference code
- Downloading data
Example prompts
- “/earth2studio-discover”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
nvidia.github.iogithub.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Earth2studio Discover loads about 2.1k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 894 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 894 words, ~2,120 tokens.
Download SKILL.mdSave it as
.claude/skills/earth2studio-discover/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- earth2studio-discover
- description
- Find Earth2Studio models, data sources, and examples for a weather/climate use case. Do NOT use for writing inference code, downloading data, or installation.
- version
- 0.16.0
- license
- Apache-2.0
- metadata.author
- NVIDIA Earth-2 Team
- metadata.tags
- earth2studio, earth2, python, discovery, models, data-sources
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:
- Always fetch the relevant live doc pages before recommending components.
- Use badge metadata (Region, Class, VRAM, Release) from the docs to filter candidates.
- Verify data-source ↔ model compatibility using the lexicon system (see Step 4).
- Cite doc URLs so the user can explore further.
Live doc references
Fetch these pages as needed (not all at once — only what the user's question requires):
| Category | URL |
|---|---|
| Prognostic models | https://nvidia.github.io/earth2studio/modules/models_px.html |
| Diagnostic models | https://nvidia.github.io/earth2studio/modules/models_dx.html |
| Data assimilation | https://nvidia.github.io/earth2studio/modules/models_da.html |
| Data sources (analysis) | https://nvidia.github.io/earth2studio/modules/datasources_analysis.html |
| Data sources (forecast) | https://nvidia.github.io/earth2studio/modules/datasources_forecast.html |
| Data sources (dataframe) | https://nvidia.github.io/earth2studio/modules/datasources_dataframe.html |
| Examples gallery | https://nvidia.github.io/earth2studio/examples/index.html |
| Lexicon source | https://github.com/NVIDIA/earth2studio/tree/main/earth2studio/lexicon |
Interaction protocol
Step 1. Understand the user's problem
Extract from what the user has said (ask follow-ups if needed, cap at 3 questions):
- Task type — medium-range forecasting, nowcasting, downscaling/super-resolution, seasonal/subseasonal, data assimilation, climate projection, ensemble generation, derived diagnostics
- Region — global, North America, Europe, Asia, specific country/area
- Temporal scale — hours ahead (nowcast), days ahead (medium-range), weeks/months (seasonal), climate
- Variables of interest — temperature, precipitation, wind, pressure, radiation, specific levels, etc.
- Hardware constraints — GPU type, available VRAM (40GB, 48GB, 80GB, 96GB)
- Deterministic vs. ensemble — single forecast or probabilistic
Good follow-up phrasing: "Are you looking for a single best-estimate forecast or an ensemble with uncertainty?" — not "what's your use case?"
Step 2. Fetch relevant model docs
Based on the user's task type, fetch the appropriate model page(s):
- Forecasting → prognostic models (px)
- Post-processing / downscaling / derived variables → diagnostic models (dx)
- Observation integration → data assimilation (da)
- Often a workflow chains px → dx, so check both
From the doc pages, extract for each candidate model:
- Class badge — NWC, DS, MR, S2S, DA, CM
- Region badge — Global, NA, EU, AS, etc.
- Rec VRAM badge — minimum GPU memory
- Release year — newer models generally supersede older ones in the same class
Filter to models matching the user's task type, region, and hardware. Present a short-list (not the full catalog) with badge metadata.
Step 3. Fetch relevant data source docs
Based on the user's data needs, fetch the appropriate data source page:
- Historical reanalysis → analysis data sources
- Real-time or operational → forecast data sources
- Observations / station data → dataframe data sources
Note which data sources cover the user's region and variables.
Show full SKILL.md (397 more words)Show less
Step 4. Verify compatibility via lexicon
This is the key technical step. Earth2Studio models declare their required input variables via input_coords(). Data sources expose available variables through their lexicon VOCAB. If a data source's lexicon VOCAB keys contain all variables in a model's input_coords (the "variable" dimension), they are compatible.
To verify:
- Check the model's doc page or source for its
input_coords— specifically the variable list - Check the data source's lexicon file at
earth2studio/lexicon/<source>.pyfor its VOCAB keys - Confirm the data source VOCAB covers all variables the model needs
If checking source code directly (e.g. user has a local clone), the lexicon files are at:
earth2studio/lexicon/gfs.py
earth2studio/lexicon/hrrr.py
earth2studio/lexicon/cds.py
earth2studio/lexicon/arco.py
earth2studio/lexicon/wb2.py
... (one per data source)Each defines a VOCAB: dict[str, str | tuple] mapping Earth2Studio variable names to source-specific identifiers.
Surface compatibility results clearly: "GraphCastOperational needs [list of variables] — GFS and ERA5 (via ARCO/CDS) both provide these, but HRRR does not cover pressure levels above X."
Step 5. Suggest examples
Fetch the examples gallery and identify examples that demonstrate the user's workflow pattern. Examples are organized by category:
01_getting_started— basic deterministic, diagnostic, ensemble pipelines02_medium_range— ensemble extension, perturbation, cyclone tracking03_downscaling— CorrDiff, CBottle, ensemble downscaling04_nowcasting— StormCast, StormScope05_data_assimilation— StormCast SDA, HealDA06_seasonal— DLESyM, statistical methods07_misc— distributed inference, IO, custom data, generation08_extend— building custom models, diagnostics, data sources
Point the user at the most relevant 1–3 examples as starting points. Explain what each demonstrates and how it relates to their problem.
Step 6. Return recommendations
Output structure (omit empty sections):
## Your use case
[1-2 sentence restatement of what the user wants to do]
## Recommended models
| Model | Class | Region | VRAM | Why |
|-------|-------|--------|------|-----|
[Short-list with rationale per row]
## Compatible data sources
| Data Source | Coverage | Compatible with |
|-------------|----------|-----------------|
[Verified via lexicon]
## Relevant examples
- [Example name](link) — what it demonstrates
## Next steps
[What to install, what to read next]Keep recommendations to 2–4 models maximum. If multiple options exist, explain the tradeoff (accuracy vs. speed, deterministic vs. ensemble, VRAM, etc.) rather than listing everything.
Limitations
- Recommendations are only as current as the live docs; unreleased models are not discoverable.
- Badge metadata may be incomplete for newly added models.
- Lexicon compatibility checks require source code access for full accuracy; doc-only checks are approximate.
Troubleshooting
| Error | Cause | Solution |
|---|---|---|
| Model page returns 404 | URL changed after a release | Check https://nvidia.github.io/earth2studio/ for updated navigation |
| Lexicon file not found | Data source is new or renamed | Search earth2studio/lexicon/ directory for current filenames |
| Badge missing from model | Model docs not yet updated | Fall back to the model's source code __init__ or README for specs |
Ownership and out-of-scope
Owns: component discovery, model/data-source compatibility checking, badge-based filtering, example recommendation, hardware-fit assessment.
Does not own: installation (use earth2studio-install skill), writing inference code, model training, custom model development, runtime debugging, PhysicsNeMo model discovery.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files in skills/earth2studio-discover of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- evals/evals.json
- skill-card.md
- skill.oms.sig
Open the folder on GitHubat commit 67a13c0
Compare with similar skills
Earth2studio Discover next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Earth2studio Discover this skillNVIDIA/skills | 3.5k | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Skill InspectorNVIDIA/SkillSpector | 20k | 1 repos | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 30k | — | ~604 | Automated safety check: Pass | MIT | |
| NEAR AI Cloud Private Inferenceinternet-court/internet-court-skill | 6.4k | 2 repos | ~1.3k | Automated safety check: Pass | Custom licence | |
| Nemoclaw Maintainer Normalize Title TagsNVIDIA/NemoClaw | 23k | — | ~693 | Automated safety check: Pass | Apache-2.0 |
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Related
Works with
Questions about Earth2studio Discover
What does Earth2studio Discover do?
Find Earth2Studio models, data sources, and examples for a weather/climate use case. Earth2studio Discover is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Find Earth2Studio models, data sources, and examples for a weather/climate use case.
When should I use Earth2studio Discover?
Earth2studio Discover fits situations like: writing inference code; downloading data.
How do I install Earth2studio Discover in Claude Code?
Run `npx skills add NVIDIA/skills --skill earth2studio-discover -a claude-code`. Or copy the skill folder (skills/earth2studio-discover in NVIDIA/skills) into .claude/skills/earth2studio-discover in your project. Claude Code loads it when a task matches its description.
How do I install Earth2studio Discover in Codex?
Run `npx skills add NVIDIA/skills --skill earth2studio-discover -a codex`. Or copy the skill folder (skills/earth2studio-discover in NVIDIA/skills) into .agents/skills/earth2studio-discover in your project. Codex loads it when a task matches its description.
Can I use Earth2studio Discover in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill earth2studio-discover -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/earth2studio-discover, .gemini/skills/earth2studio-discover, .github/skills/earth2studio-discover and .opencode/skills/earth2studio-discover in your project.
What does Earth2studio Discover need to run?
SKILL.md names no scripts, command-line tools or credentials: Earth2studio Discover is instructions for the agent only. Our summary lists: Python 3.
Does Earth2studio Discover access the network?
SKILL.md names 2 domains. As links in the text: nvidia.github.io and github.com. This is read from the text; nothing was executed.
Is Earth2studio Discover safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Earth2studio Discover use?
Earth2studio Discover is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Earth2studio Discover use?
About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Earth2studio Discover?
Skills that share tags, products or a category with Earth2studio Discover: Skill Inspector (NVIDIA/SkillSpector, 20k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Embeddings via 9Router (decolua/9router, 30k stars) and NEAR AI Cloud Private Inference (internet-court/internet-court-skill, 6.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Earth2studio Discover?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.