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SkillAtlasSkill 详情
earth2studio-create-prognostic
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 70
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
其他
中风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：4 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/earth2studio-create-prognostic" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/earth2studio-create-prognostic" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/earth2studio-create-prognostic" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/earth2studio-create-prognostic" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/earth2studio-create-prognostic" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: earth2studio-create-prognostic
version: 0.16.0
license: Apache-2.0
metadata:
author: NVIDIA Earth-2 Team <agent-skills@nvidia.com>
tags: [earth2studio, prognostic-model, python]
description: >
Create Earth2Studio prognostic (time-stepping forecast) model wrappers.
Do NOT use for diagnostic models, data sources, or installation.
argument-hint: URL or local path to reference inference script (optional)Quick Start Checklist
Do these steps IN ORDER. Do not skip any step.
- Read this SKILL.md completely first
- Get reference script (Step 0)
- Create
earth2studio/models/px/<name>.pywith triple inheritance - Create
test/models/px/test_<name>.pywith mock tests - Run:
uv run pytest test/models/px/test_<name>.py -v - Add/update model extra, install docs, API docs, and changelog (Steps 1-2, 9)
- Run:
make format && make lint
⚠️ CRITICAL: Always use
uv runfor Python commands:
- ✅
uv run pytest .../uv run python ...- ❌
pytest .../python ...(missing dependencies)Stuck or wrong output: Do not keep retrying the same fix. Follow Self-Improvement to patch this skill before continuing.
Purpose
Implement a prognostic model wrapper connecting third-party ML weather models to Earth2Studio. Prognostic models time-integrate forward—given initial state, they predict future states by stepping through time (e.g., 6-hour increments).
Workspace
| Context | Location |
|---|---|
| Harbor eval | Write to /workspace/output/earth2studio/models/px/... |
Harbor + --copy-repo | Full checkout at /workspace/repo |
| Local clone | Directory with pyproject.toml |
Never read evals/targets/ — grader references only.
Reference Files
Load on demand during the matching step:
| File | Content | Load at |
|---|---|---|
references/skeleton-template.py | Full model skeleton with FILL comments | Steps 3–6 |
references/method-templates.py | Canonical method implementations | Steps 4–6 |
references/testing-guide.py | Test skeleton and mock patterns | Step 7 |
references/validation-guide.md | Comparison scripts, PR, code review | Steps 10–11 |
Workflow Steps
Step 0 — Get Reference Script
If $ARGUMENTS provided, use it. Otherwise ask:
Please provide a reference inference script URL/path.
Step 1 — Analyze & Propose Dependencies
Analyze: packages, architecture, I/O shapes, time step, resolution, checkpoint.
Propose pyproject.toml group (alphabetical, add to all). Every
prognostic model must have an optional dependency extra, even when no packages
are required:
model-name = ["package1>=version", "package2"]
# or, when no additional packages are required:
model-name = []
[CONFIRM] Present dependencies and ask user to approve.
Step 2 — Add Dependencies
Edit pyproject.toml: add the model extra alphabetically, even if it is
empty, and update the all aggregate.
Step 3 — Create Model File
File: earth2studio/models/px/<lowercase>.py
Required inheritance (all three):
class ModelName(torch.nn.Module, AutoModelMixin, PrognosticMixin):
Required imports:
import numpy as np
import torch
from earth2studio.models.auto import AutoModelMixin, Package
from earth2studio.models.batch import batch_coords, batch_func
from earth2studio.models.px.base import PrognosticMixin
from earth2studio.models.utils import create_coords_from_lat_lon, handshake_dim
from earth2studio.lexicon import E2STUDIO_VOCAB
from earth2studio.utils import check_optional_dependencies
from loguru import logger
SPDX header (required at top of every .py file):
# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: Apache-2.0
Canonical method order:
__init__2.input_coords3.output_coords(@batch_coords)load_default_package5.load_model6.to(optional)- Private methods 8.
__call__(@batch_func) 9._default_generator create_iterator
Step 4 — Implement Coordinates
input_coords rules:
batch:np.empty(0)time:np.empty(0)(dynamic)lead_time: starts atnp.timedelta64(0, "h")lat: 90 to -90 (north to south); this is the public Earth2Studio convention even if the source model uses the opposite orderlon: 0 to 360- If a checkpoint/model core expects south-to-north latitude, flip tensors internally before/after the core model; do not expose flipped latitude in
input_coordsoroutput_coords - Map variables to
E2STUDIO_VOCAB(282 entries inearth2studio/lexicon/base.py)
output_coords: Use handshake_dim/handshake_coords for input validation, then increment lead_time. Prefer a shared coordinate-check helper and call it from output_coords, __call__, and iterator setup before model execution.
Step 5 — Implement Forward Pass
__call__: @batch_func decorated, shape (batch, time, lead_time, var, lat, lon).
Reshape to model format → call model → reshape back.
create_iterator: MUST yield initial condition first (step 0).
Use front_hook/rear_hook for perturbation injection.
Step 6 — Implement Model Loading
load_default_package: Lock HuggingFace URLs: hf://org/repo@commit
load_model: Use package.resolve(), map_location="cpu", eval() mode,
decorate with @check_optional_dependencies().
Step 7 — Write Tests
File: test/models/px/test_<name>.py
Required tests:
| Function | Purpose |
|---|---|
test_<model>_call | Single forward pass (parametrize device/time) |
test_<model>_iter | Iterator produces sequence |
test_<model>_exceptions | Invalid coords raise errors |
test_<model>_package | Real weights (@pytest.mark.package) |
Create PhooModelName dummy matching interface for mock tests.
Run tests:
uv run pytest test/models/px/test_<name>.py -m "not package" -v
uv run pytest test/models/px/test_<name>.py::test_<model>_package --package -v
Do not omit the package test. If arbitrary random inputs are not physically valid for the real checkpoint, use a stable model-appropriate synthetic input while still loading real weights and running a forward pass.
Step 8 — Register Model (if requested)
- Add to
earth2studio/models/px/__init__.py(alphabetical) - Verify deps in pyproject.toml
Step 9 — Documentation
- Add to
docs/modules/models_px.rst(alphabetical). This is required for every new prognostic model so the API docs include the generated page. - Add to
docs/userguide/about/install.md(alphabetical tab) for the model extra, even when the extra is empty. Include model-specific notes plus bothpip install earth2studio[model-name]anduv add earth2studio --extra model-nameinstructions. - Update
CHANGELOG.mdunder### Added. This is required for every new prognostic model.
Format and lint:
make format && make lint && make license
Step 10 - Validation (if requested)
Follow references/validation-guide.md. Create uncommitted vanilla, E2S,
comparison, and sanity-check scripts; do not commit generated outputs or images.
Use PR-safe placeholders for plots so the user can upload images manually.
[CONFIRM] User must visually inspect plots before proceeding.
Step 11 - PR (if requested)
Follow references/validation-guide.md and use:
references/pr-body-template.mdreferences/pr-comment-template.md
Before creating the PR, verify pyproject.toml has the model extra, the
all extra includes it, install docs include both pip and uv commands, and
docs/modules/models_px.rst plus CHANGELOG.md are updated.
Do not include machine names, absolute paths, device inventory, or uploaded image links in PR text. Use plot placeholders instead.
Examples
Simple Identity Model
User: Create IdentityModel - returns input unchanged, 6h step, 181x360, vars: t2m, u10m, v10m, msl
Agent: [reads SKILL.md, creates identity.py with triple inheritance,
creates test_identity.py, runs pytest, runs make format && lint]
External Model (Pangu)
User: Add Pangu-Weather wrapper
GitHub: https://github.com/198808xc/Pangu-Weather
Agent: [reads SKILL.md, fetches inference.py, creates pangu.py,
creates test_pangu.py, runs pytest]
Key Patterns
Coordinate Template
@property
def input_coords(self) -> CoordSystem:
return CoordSystem({
"batch": np.empty(0),
"time": np.empty(0),
"lead_time": np.array([np.timedelta64(0, "h")]),
"variable": np.array(["t2m", "u10m", ...]),
# Public Earth2Studio convention is north-to-south latitude.
"lat": np.linspace(90, -90, 181),
"lon": np.linspace(0, 359, 360),
})
@batch_coords()
def output_coords(self, input_coords: CoordSystem) -> CoordSystem:
output = input_coords.copy()
output["lead_time"] = input_coords["lead_time"] + np.timedelta64(6, "h")
return output
Iterator Template
def create_iterator(self, x, coords):
yield x, coords # Initial condition (step 0)
while True:
x, coords = self.front_hook(x, coords)
x, coords = self(x, coords)
x, coords = self.rear_hook(x, coords)
yield x, coords
Troubleshooting
| Error | Solution |
|---|---|
OptionalDependencyFailure | uv add --optional <group> <pkg> |
| Coordinate handshake fails | Check handshake_dim indices match dim position |
| Iterator wrong shapes | Debug reshape logic with random input |
ModuleNotFoundError: pytest | Use uv run pytest not pytest |
Reminders
DO:
- Use
uv run pythonfor ALL Python commands - Use
loguru.logger, neverprint() - Inherit
torch.nn.Module + AutoModelMixin + PrognosticMixin - Yield initial condition first in
create_iterator - Use
front_hook()/rear_hook()in_default_generator - Include SPDX header in every .py file
DON'T:
- Create general base classes for reuse
- Commit API keys or comparison scripts
- Read from
evals/targets/
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