Esmfold2
JimLiu/science-skills
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.
Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference).
Install Earth2studio Deterministic Forecast
$ npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills earth2studio-deterministic-forecast --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-deterministic-forecast .claude/skills/earth2studio-deterministic-forecast && 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-deterministic-forecast" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-deterministic-forecast into .claude/skills/earth2studio-deterministic-forecast/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-deterministic-forecast", 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-deterministic-forecastType 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-deterministic-forecast -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills earth2studio-deterministic-forecast --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-deterministic-forecast .agents/skills/earth2studio-deterministic-forecast && 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-deterministic-forecast" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-deterministic-forecast into .agents/skills/earth2studio-deterministic-forecast/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-deterministic-forecast", 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-deterministic-forecast -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills earth2studio-deterministic-forecast --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-deterministic-forecast .cursor/skills/earth2studio-deterministic-forecast && 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-deterministic-forecast" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-deterministic-forecast into .cursor/skills/earth2studio-deterministic-forecast/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-deterministic-forecast", 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-deterministic-forecast--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-deterministic-forecast -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills earth2studio-deterministic-forecast --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-deterministic-forecast .gemini/skills/earth2studio-deterministic-forecast && 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-deterministic-forecast" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-deterministic-forecast into .gemini/skills/earth2studio-deterministic-forecast/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-deterministic-forecast", 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-deterministic-forecastInstalls 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-deterministic-forecast -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-deterministic-forecast .github/skills/earth2studio-deterministic-forecast && 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-deterministic-forecast" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-deterministic-forecast into .github/skills/earth2studio-deterministic-forecast/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-deterministic-forecast", 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-deterministic-forecast -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-deterministic-forecast --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-deterministic-forecast .opencode/skills/earth2studio-deterministic-forecast && 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-deterministic-forecast" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-deterministic-forecast into .opencode/skills/earth2studio-deterministic-forecast/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-deterministic-forecast", 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-deterministic-forecast- GitHub stars
- 3.5k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 460 words
- Files
- 12 (incl. references)
- Skills in repo
- 380
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference).
- Works in 9 steps: Gather Requirements (skip what's already… → Select Model → Select Data Source → …
- Data-only fetch
- SKILL.md covers Prerequisites, Live Doc References, Workflow and Ownership, plus 2 more sections
- Runs Python and Shell scripts from its folder
What it does
Earth2studio Deterministic Forecast is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including reference files (for example `BENCHMARK.md`, `evals/config.yml` and `evals/environment/setup/bootstrap.sh`).
It sits in AI & LLM Engineering. It works with CUDA. 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
- Data-only fetch
Example prompts
- “/earth2studio-deterministic-forecast”
Requirements
- Python 3
- A Bash shell
Workflow steps
9 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 0e0d506. 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
Ships script files (Python and Shell), which the agent can run.
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 Deterministic Forecast loads about 1.4k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 460 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 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 460 words, ~1,371 tokens.
Download SKILL.mdSave it as
.claude/skills/earth2studio-deterministic-forecast/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.- name
- earth2studio-deterministic-forecast
- description
- Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.
- version
- 0.16.0
- license
- Apache-2.0
- metadata.author
- NVIDIA Earth-2 Team
- metadata.tags
- earth2studio, earth2, python, inference, forecast, deterministic
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:
| Component | URL |
|---|---|
| Prognostic models | https://nvidia.github.io/earth2studio/modules/models_px.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 |
| IO backends | https://nvidia.github.io/earth2studio/modules/io.html |
run.deterministic | https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/run.py |
Workflow
1. Gather Requirements (skip what's already provided)
- Time horizon (hours/days/weeks)
- Variables of interest (t2m, wind, geopotential, etc.)
- Region (global or specific like CONUS)
- GPU/VRAM available
2. Select Model
Fetch prognostic models page. Filter by time horizon, region, VRAM. Note model's:
- Input variables (
input_coords["variable"]) - Time step size (
output_coords["lead_time"])
3. Select Data Source
Data source must provide all model input variables. Verify via lexicon at
earth2studio/lexicon/<source>.py. Common pairings: Global models → GFS/ARCO/IFS;
Regional → HRRR.
4. Select IO Backend
Default: ZarrBackend. Use NetCDF4Backend for legacy tools, XarrayBackend
for in-memory/small runs.
5. Calculate nsteps
nsteps = forecast_hours / model_step_hours
Example: 5-day forecast with 6h step → nsteps = 120 / 6 = 20
6. Decide: output_coords Filtering
- Filter variables (
output_coords) when user requests specific variables (e.g., "t2m and wind") - reduces output size - Save all variables (omit
output_coords) when user says "all variables" or doesn't specify - preserves full model output
7. Generate Script
from collections import OrderedDict
import numpy as np
import torch
from earth2studio.models.px import <ModelClass>
from earth2studio.data import <DataSourceClass>
from earth2studio.io import <IOBackendClass>
from earth2studio.run import deterministic
model = <ModelClass>.load_model(<ModelClass>.load_default_package())
data = <DataSourceClass>()
io = <IOBackendClass>("<output_path>")
# Include output_coords ONLY if user requested specific variables
output_coords = OrderedDict({"variable": np.array(["t2m", "u10m"])})
io = deterministic(
time=["YYYY-MM-DDTHH:MM:SS"],
nsteps=<N>,
prognostic=model,
data=data,
io=io,
output_coords=output_coords, # omit if saving all variables
device=torch.device("cuda"),
)8. Manual Loop Alternative
When user explicitly requests manual implementation (NOT using earth2studio.run.deterministic), follow this checklist in order:
- fetch_data - Get initial conditions:
x, coords = fetch_data(data, time, model.input_coords, device) - Setup total_coords - Build coordinate arrays for time and lead_time dimensions
- io.add_array - Initialize IO backend with total_coords before loop
- create_iterator - Create prognostic iterator:
model_iter = model.create_iterator(x, coords) - Loop through nsteps -
for step, (x, coords) in enumerate(model_iter): if step >= nsteps: break - map_coords - Filter output variables if needed:
x_out, coords_out = map_coords(x, coords, output_coords) - split_coords - Prepare for IO write:
x_out, coords_out = split_coords(x_out, coords_out) - io.write - Write each step to backend
Show full SKILL.md (156 more words)Show less
9. Explain Next Steps
- How to change forecast time or run multiple initializations
- How to read output (
xr.open_zarr(...)) - Point to diagnostic workflow for post-processing
Ownership
Owns: Model selection, data source compatibility, IO backend selection,
nsteps calculation, generating earth2studio.run.deterministic scripts.
Does not own: Ensemble workflows, diagnostics, data-only fetch, installation, model training.
Troubleshooting
See references/troubleshooting.md for common errors and solutions.
Reminders
- Always fetch live docs before recommending models or data sources - APIs change between releases
- Verify lexicon compatibility - Model input variables must exist in data source's VOCAB
- Use
load_default_package()- This is the standard pattern for loading model weights - Time format is ISO 8601 - Use
"YYYY-MM-DDTHH:MM:SS"format for thetimeargument - Wind speed needs both components - If user asks for "wind speed", include both
u10mandv10m - nsteps is integer division -
nsteps = total_hours // model_step_hours - ZarrBackend is the default - Only suggest alternatives if user has specific requirements
- GPU is required - All prognostic models require CUDA; CPU inference is not supported
© 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 11 other files (references) in skills/earth2studio-deterministic-forecast of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- evals/config.yml
- evals/environment/Dockerfile
- evals/environment/setup/bootstrap.sh
- evals/evals.json
- evals/targets/eval_1_target.py
- evals/targets/eval_2_target.py
- evals/targets/eval_3_target.py
- references/troubleshooting.md
- skill-card.md
- skill.oms.sig
Open the folder on GitHubat commit 0e0d506
Compare with similar skills
Earth2studio Deterministic Forecast 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 Deterministic Forecast this skillNVIDIA/skills | 3.5k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Esmfold2JimLiu/science-skills | 227 | 4 repos | ~2.5k | Automated safety check: Pass | Apache-2.0 | |
| MUSA GPU Training Optimizeropen-infra-skills/infra-skills | 141 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Benchmark TuneMesh-LLM/mesh-llm | 3.5k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Cuda Kernel OptimizerKernelFlow-ops/cuda-optimized-skill | 212 | — | ~4.3k | Automated safety check: Pass | MIT | |
| DGX Spark Training Gotchaswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT |
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Questions about Earth2studio Deterministic Forecast
What does Earth2studio Deterministic Forecast do?
Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Earth2studio Deterministic Forecast is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference).
When should I use Earth2studio Deterministic Forecast?
Earth2studio Deterministic Forecast fits situations like: data-only fetch.
How do I install Earth2studio Deterministic Forecast in Claude Code?
Run `npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a claude-code`. Or copy the skill folder (skills/earth2studio-deterministic-forecast in NVIDIA/skills) into .claude/skills/earth2studio-deterministic-forecast in your project. Claude Code loads it when a task matches its description.
How do I install Earth2studio Deterministic Forecast in Codex?
Run `npx skills add NVIDIA/skills --skill earth2studio-deterministic-forecast -a codex`. Or copy the skill folder (skills/earth2studio-deterministic-forecast in NVIDIA/skills) into .agents/skills/earth2studio-deterministic-forecast in your project. Codex loads it when a task matches its description.
Can I use Earth2studio Deterministic Forecast 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-deterministic-forecast -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-deterministic-forecast, .gemini/skills/earth2studio-deterministic-forecast, .github/skills/earth2studio-deterministic-forecast and .opencode/skills/earth2studio-deterministic-forecast in your project.
What does Earth2studio Deterministic Forecast need to run?
Going by SKILL.md and its folder, Earth2studio Deterministic Forecast needs Python and a shell for the scripts in its folder. Our summary lists: Python 3; A Bash shell.
Does Earth2studio Deterministic Forecast 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 Deterministic Forecast 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 Deterministic Forecast use?
Earth2studio Deterministic Forecast 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 Deterministic Forecast use?
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 386 tokens, read only when the agent opens those files.
What are the alternatives to Earth2studio Deterministic Forecast?
Skills that share tags, products or a category with Earth2studio Deterministic Forecast: Esmfold2 (JimLiu/science-skills, 227 stars), MUSA GPU Training Optimizer (open-infra-skills/infra-skills, 141 stars), Benchmark Tune (Mesh-LLM/mesh-llm, 3.5k stars) and Cuda Kernel Optimizer (KernelFlow-ops/cuda-optimized-skill, 212 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Earth2studio Deterministic Forecast?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 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.