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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nvidia/nurec-skills/ncorenpx skills add NVIDIA/nurec-skills --skill ncoregit clone --depth 1 https://github.com/NVIDIA/nurec-skillsWrote 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/ncore)<a href="https://agentmods.dev/skills/nvidia/nurec-skills/ncore"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/ncore.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00273 | $0.14942 |
| Opus 5 | $0.00137 | $0.07471 |
| Sonnet 5 | $0.00055 | $0.02988 |
| Haiku 4.5 | $0.00027 | $0.01494 |
Grade A, and why
ncore scanned grade A with 0 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 6d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 1,059 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NCore V4 Data Conversion
Purpose
Convert any sensor recording (cameras, LiDAR, radar, IMU, depth,
stereo, COLMAP/SfM, ROS 2 bag) into a valid NVIDIA NCore V4 store
so it can be consumed by NuRec / Asset Harvester / ncore_vis, or
wired into a robotics-to-sim ("r2s") pipeline. Drive the existing
in-tree converters (PAI, Waymo, COLMAP/ScanNet++) or author a new
converter from ncore_template/.
Use this skill when: the user has raw sensor data (any rig) that
NuRec or Asset Harvester needs to ingest, or when an existing
converter is failing validate.py / producing NuRec data-quality
complaints.
Do NOT use this skill when:
- The user is already on V4 and only wants to train or render
(use the
nreskill). - The user wants per-object 3D assets from sparse views (use
asset-harvester). - The user only needs to browse / pick an existing NVIDIA dataset
(use
physical-ai-datasets).
This skill teaches an agent to take any sensor dataset and produce a valid
NCore V4 store that NuRec / Asset Harvester / ncore_vis will accept. It covers
both driving the existing in-tree converters (PAI, Waymo, COLMAP/ScanNet++)
and writing a new one for unsupported formats (PandaSet, NuScenes, KITTI,
stereo, mono+depth, mono+lidar, custom robotics rigs).
Table of Contents
- When to use which path
- Install & references
- Mental model — the V4 store
- Path A — drive an existing in-tree converter
- Path B — author a new converter from the template
- V4 conventions you must obey
- Format recipes (AV)
- Format recipes (non-AV / sensor-only)
- Robotics pipeline shards (r2s)
- Validation & end-to-end NuRec
- Common failure modes (and the fix file)
- Additional resources
What ships with it
5 files 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.
- 6d ago First seen · 1,059 lines · 273 tokens per session scan A 78f7fc493a18
ncore is a skill published in the GitHub repository NVIDIA/nurec-skills (35 stars, last pushed 3d ago), licensed Apache-2.0. It adds 273 tokens to every session and 14,942 once invoked, about $0.0014 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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