NVIDIA NuRec Skills is a set of agent instructions for running neural reconstruction and rendering workflows in NVIDIA Omniverse NuRec. It targets autonomous-vehicle and robotics simulation, guiding agents across the public containers, repositories, and artifacts that make up the NuRec stack.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NVIDIA/nurec-skillsnpx agentmods add skills/nvidia/nurec-skills/physical-ai-datasetsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/nurec-skills/physical-ai-datasets)<a href="https://agentmods.dev/skills/nvidia/nurec-skills/physical-ai-datasets"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/physical-ai-datasets/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/nurec-skills/physical-ai-datasets"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/physical-ai-datasets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 33 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 92 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 150 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Privilege Escalation · line 146 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00255 | $0.13103 |
| Opus 5 | $0.00128 | $0.06552 |
| Sonnet 5 | $0.00051 | $0.02621 |
| Haiku 4.5 | $0.00026 | $0.01310 |
Grade B, and why
physical-ai-datasets scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt -y install git git-lfs Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Tooling | `wget … scripts/download.py; python download.py --odir <path> --file_types hdmap,lidar,synthetic` | How it starts
The opening of the file, as written. The whole thing — 888 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NVIDIA Physical AI Datasets (Hugging Face)
Purpose
Help the agent find, evaluate, and download an NVIDIA PhysicalAI-*
dataset on Hugging Face that fits the user's downstream task —
autonomous-vehicle reconstruction, robotics manipulation, GR00T
post-training, spatial-intelligence research, grasping, or sim-ready
content — and then hand off to the sibling skill (ncore, nre,
asset-harvester, nurec-fixer) or upstream NVIDIA tool that
actually consumes it.
Use this skill when: the user asks "is there an NVIDIA dataset
for X?", "where do I get NCore / NuRec / GR00T sample data?", or is
shopping the Hugging Face NVIDIA org for PhysicalAI-* collections.
Do NOT use this skill when:
- The user already knows the dataset and just wants to run a pipeline — jump straight to the consuming skill.
- The user needs a non-NVIDIA dataset (Waymo, nuScenes, KITTI, …) — this catalog is NVIDIA-only.
- The user wants to train Cosmos / GR00T / Isaac Sim itself — that's the upstream tool's job, not this catalog's.
Overview
Catalog of NVIDIA's open Physical AI dataset family on Hugging Face. Pick by task (Section 2 § lookup table) or family (Sections 3–10). Every entry lists: dataset path, size, format, license, gating, and the downstream skill in this repo that consumes it.
Source of truth: https://huggingface.co/nvidia (filter
PhysicalAI-) and the curated Physical AI collection. When upstream cards drift, re-check the HF page; this skill mirrors the cards as of Apr 2026.
Prerequisites
- HuggingFace account with the dataset card opened in a browser at least once, and the gating checkbox accepted on every dataset you intend to download.
- HuggingFace user access token exported as
HF_TOKEN(create at https://huggingface.co/settings/tokens). git,git-lfs, andhuggingface_hub[cli]on PATH.- Storage room sized to the dataset you're pulling (see the per-row
size column; some are < 1 GB, the AV dataset is 133 TB — always
pre-filter with
--includeorphysical_ai_av).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 888 lines · 255 tokens per session scan B 4637bc2d3ab8
physical-ai-datasets is a skill published in the GitHub repository NVIDIA/nurec-skills (37 stars, last pushed 7d ago), licensed Apache-2.0. It adds 255 tokens to every session and 13,103 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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