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SkillAtlasSkill 详情
earth2studio-create-datasource
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 52
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
高风险
- 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。
- 存在潜在风险命令，请谨慎安装。
- 扫描发现：3 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-datasource" 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-datasource" 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-datasource" 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-datasource" 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-datasource" 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-datasource
version: 0.16.0
license: Apache-2.0
metadata:
author: NVIDIA Earth-2 Team <agent-skills@nvidia.com>
tags:
- earth2studio
- earth2
- python
- data-source
- forecast-source
- integration
description: >
Create and validate Earth2Studio data source wrappers (DataSource,
ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores.
Do NOT use for fetching data with existing sources, model inference, or
installation tasks.
argument-hint: URL or description of remote data store (optional)Create and Validate Data Source
Purpose
End-to-end workflow for implementing a new Earth2Studio data source wrapper that connects a remote data store (S3, GCS, Azure, HTTP, HuggingFace) to Earth2Studio's async data fetching infrastructure — from analysis through implementation, testing, validation, and PR submission.
Prerequisites
- Earth2Studio dev environment with
uv(uv run pythonmust work) - Git configured with fork (
origin) and upstream (upstream) remotes - Access to the target remote data store (credentials if private)
- Python 3.10+
Workspace
Use the directory containing pyproject.toml. For Harbor evals, write to
/workspace/output/ preserving paths. Never read evals/targets/.
Instructions
Python Environment: Always use
uv run pythonor the local.venv. Never use the system Python directly.
Follow every step in order.
[CONFIRM] gates: Only Step 1 (Source Type) and Step 12 (Sanity-Check Plots) require explicit user approval. All other
[CONFIRM]markers are advisory — present decisions inline and proceed without blocking.Deliverables first: Write the source file and test file (Steps 6–7) before extended exploration, documentation, registration, CHANGELOG, or PR work. Skip Steps 8–14 when the user asks for implementation only.
Before you finish: Run verification commands in the repo root so results appear in the session log:
uv run pytest test/data/test_<source>.py -x make format && make lintBe concise: Avoid long architecture reports; summarize decisions in a few sentences and move on to file writes.
Hangs or User Feedback If agent becomes stuck or user provides a correction during this skills use, conservatively review relevant part of the skill and improve. Be concise.
One source type per invocation. Invoke again for companion types.
Reference Files
Load these on demand during the relevant steps:
| File | Content | Load at |
|---|---|---|
references/implementation-guide.py | Skeleton source with FILL comments | Steps 3–10 |
references/testing-guide.py | Test skeleton with FILL comments | Step 11 |
references/validation-guide.md | Plot templates, PR body template, Greptile handling | Steps 12–14 (optional, for templates) |
Workflow Overview
Step 0: Obtain reference → Step 1: Determine type → Step 2: Dependencies
→ Step 3: Add deps → Step 4: Create lexicon → Step 5: Update vocab/schema
→ Step 6: Create skeleton → Step 7: Implement source → Step 8: Register
→ Step 9: Documentation → Step 10: CHANGELOG → Step 11: Tests
→ Step 12: Validate & plots (user confirms) → Step 13: PR + sanity comment
→ Step 14: Greptile review
Step 0 — Obtain Remote Data Store Reference
If $ARGUMENTS is provided, use it (URL → WebFetch; file path → read).
If empty, ask:
Please provide a URL, API documentation link, or description of the remote data store. This will be used to understand storage format, access pattern, variable inventory, temporal/spatial resolution.
Step 1 — Determine Source Type
| Protocol | Returns | Has lead_time? | Use |
|---|---|---|---|
| DataSource | xr.DataArray | No | Gridded analysis/reanalysis |
| ForecastSource | xr.DataArray | Yes | Gridded forecast |
| DataFrameSource | pd.DataFrame | No | Sparse/station obs |
| ForecastFrameSource | pd.DataFrame | Yes | Sparse forecast obs |
Key factors: gridded vs sparse → DataArray vs DataFrame; analysis vs forecast → Source vs ForecastSource.
[CONFIRM — Source Type]
Present recommended type with justification. Ask for confirmation.
Step 2 — Examine Remote Store & Propose Dependencies
Analyze: storage backend, file format, authentication, access pattern, temporal/spatial resolution, variable inventory.
Prefer fsspec:
| Backend | Preferred | Avoid |
|---|---|---|
| AWS S3 | s3fs (core dep) | boto3 directly |
| GCS | gcsfs (core dep) | google-cloud-storage |
| Azure | adlfs | azure-storage-blob |
| HTTP | fsspec (core dep) | requests |
| HuggingFace | huggingface_hub (core dep) | custom scripts |
Only fall back to dedicated libraries when fsspec cannot access the store.
Check pyproject.toml — only propose packages not already present.
Core deps include: s3fs, gcsfs, fsspec, zarr, netCDF4, h5py,
pygrib, huggingface-hub, pandas, pyarrow.
[CONFIRM — Dependencies & Access Pattern]
Present: backend, fsspec filesystem, new packages (with license), auth method.
Step 3 — Add Dependencies
Load
references/implementation-guide.pyfrom here through Step 10.
If new packages needed:
uv add --extra data <package>uv lock- Add optional dependency imports using
OptionalDependencyFailurepattern
Step 4 — Create Lexicon Class
Create earth2studio/lexicon/<source_name>.py with:
metaclass=LexiconTypeVOCAB: dict[str, str]mapping E2S names → remote keysget_item(cls, val)returningtuple[str, Callable]- Use
::separator for structured keys
Map remote variables against E2STUDIO_VOCAB (282 entries in
earth2studio/lexicon/base.py).
[CONFIRM — Lexicon & Variable Mapping]
Present: class name, key format, full mapping table, modifiers, reference URL.
Step 5 — Update E2STUDIO_VOCAB / SCHEMA (if needed)
- New vocab: surface = descriptive abbrev; pressure =
{name}{level} - New schema fields: DataFrame sources only, check
E2STUDIO_SCHEMA
[CONFIRM — Vocabulary & Schema Updates]
Skip if no updates needed.
Step 6 — Create Skeleton Data Source File
Follow canonical method ordering:
- Class constants
- SCHEMA
__init___async_init__call__fetch_create_tasksfetch_wrapperfetch_array_validate_time- Helpers
cachepropertyavailableclassmethod
Use async task dataclass pattern for parallel execution.
[CONFIRM — Skeleton]
Present: class name, file path, skeleton code, task dataclass.
Step 7 — Implement the Data Source & Tests
The test file is a co-equal deliverable. Create
test/data/test_<filename>.pyalongside the source. Addtest_<source>_call_mockfor async sources.
Sync sources: Use prep_data_inputs/prep_forecast_inputs, direct __call__.
Async sources: See references/implementation-guide.py for required patterns:
_sync_async, managed_session, gather_with_concurrency, async_retry,
pure async I/O, try/finally cleanup. Constructor params: cache=True,
verbose=True, async_timeout=600, async_workers=16, retries=3.
DataFrame sources add time_tolerance.
Step 8 — Register the Source
earth2studio/data/__init__.py— alphabetical importearth2studio/lexicon/__init__.py— alphabetical import- Verify
pyproject.tomldeps
Step 9 — Update Documentation
- Add to correct RST file (
datasources_analysis.rst/_forecast.rst/_dataframe.rst) - Class docstring: Parameters, Warning (download size), Note (reference URLs), Badges (last)
- All public methods: NumPy-style docstrings
Step 10 — Update CHANGELOG.md
Add entry under the current unreleased version. See
references/implementation-guide.py REGISTRATION CHECKLIST for the format.
One line per source. Do NOT add separate lexicon entries.
Step 11 — Verify Style & Expand Tests
Run make format && make lint && make license. Load references/testing-guide.py
for test skeletons. Required tests: test_<source>_fetch (slow), _cache (slow),
_call_mock, _exceptions, _available. Target 90%+ coverage with --slow.
[CONFIRM — Tests]
Present test file, functions, coverage.
Step 12 — Validate Variables & Sanity-Check
- Validate all lexicon vars against real data (run script, do NOT commit)
- Remove variables with < 10% valid data
- Create sanity-check plot (gridded or sparse template)
- Tell user the plot path and ask for visual confirmation
[CONFIRM — Sanity-Check Plots]
User MUST visually inspect plots. Do not proceed without confirmation.
Step 13 — Branch, Commit & Open PR
- Create branch
feat/data-source-<name> - Commit (do NOT add sanity-check script/images)
- Push to fork
gh pr create --repo NVIDIA/earth2studio- Immediately post sanity-check validation as PR comment with:
- Variable coverage table (name, count, range, unit)
- Data validation summary (regions, storms/stations, time range)
- Key findings (physically reasonable values, conversions verified)
- Full validation script in
<details>block - Image placeholder:
<!-- Drag and drop sanity-check image here -->
[CONFIRM — Ready to Submit]
Verify all steps complete before creating PR.
Step 14 — Automated Code Review
- Poll for Greptile review (5 min timeout)
- Categorize feedback (bug/style/perf/docs/suggestion/false-positive)
- Present triage table to user
- Implement accepted fixes
- Respond to PR comments
- Push
[CONFIRM — Review Triage]
User approves which comments to address.
Examples
User: Add a data source for the NOAA GFS analysis on S3
Agent: [loads skill, proceeds through Steps 0–14]
Limitations
- One source type per invocation
- Requires network access for validation (Step 12)
- SPDX license headers required in all files
Reminders
DO: uv run python, loguru.logger, alphabetical order in __init__.py/RST/CHANGELOG,
canonical method ordering, async utilities (managed_session, gather_with_concurrency,
async_retry), pure async I/O, reference URLs in docstrings, try/finally cleanup.
AVOID: asyncio.to_thread, bare tqdm.gather, xarray for loading, full file downloads.
NEVER: loop.set_default_executor(), commit secrets, commit sanity-check scripts/images.
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