A guide and toolkit for NVIDIA Omniverse NuRec, a system that reconstructs 3D scenes from camera and LiDAR recordings. It works with NVIDIA containers and requires an NVIDIA GPU and NGC API key.

Use it to train and render 3D Gaussian reconstructions, create supporting data, export meshes and other files, modify vehicle scenes, serve renders over gRPC, and measure results.

It provides ready-made commands and files for running the reconstruction workflow without assembling each supporting step yourself.

nre — Skill for Claude Code and Codex by NVIDIA

About the project

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.

NVIDIA/nurec-skills · 36 stars · on GitHub

Install

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.

Any agent

npx skills add NVIDIA/nurec-skills --skill nre

Clone the repo

git clone --depth 1 https://github.com/NVIDIA/nurec-skills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nvidia/nurec-skills/nre/github.svg)](https://agentmods.dev/skills/nvidia/nurec-skills/nre)

Your own site

<a href="https://agentmods.dev/skills/nvidia/nurec-skills/nre"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/nre/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nvidia/nurec-skills/nre"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/nre.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 150 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 5,433 The whole file, excluding the scripts and references it only reads on demand.

Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00150	$0.05433
Opus 5	$0.00075	$0.02717
Sonnet 5	$0.00030	$0.01087
Haiku 4.5	$0.00015	$0.00543

Measured 9d ago against content hash fd2473df552d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade C, and why

nre scanned grade C with 1 finding 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 9d ago.

The scan reads SKILL.md. This mod also ships 10 executable files (references/example-workflows/bash/aux.sh, references/example-workflows/bash/export_usdz.sh, references/example-workflows/bash/nurec.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

nre/nre-tools, `rm -rf ${HOME}/.cache/nre` and your

skills/nre/SKILL.md · 416 lines

How it starts

The opening of the file, as written. The whole thing — 416 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NRE — NVIDIA Omniverse NuRec (Neural Reconstruction Engine)

Purpose

Drive the public NVIDIA Omniverse NuRec / Neural Reconstruction Engine containers (nvcr.io/nvidia/nre/nre, nvcr.io/nvidia/nre/nre-tools) to train a 3DGUT/3DGRT Gaussian reconstruction from an NCore V4 camera+LiDAR clip, render novel views (locally or via gRPC), generate aux data, export PLY/depth/mesh/ego-mask/tracks, package Asset Harvester output into a USDZ, and evaluate rendering metrics.

This skill carries the host-side toolkit around the NRE CLI: NGC credential resolution, cached-image notes, local render recipes, MP4 encoding, warm serve-grpc boot/teardown scripts, a thin Python gRPC client for repeated RGB renders, bundled rig JSONs, pre-baked custom-rig trajectories, and bash / Hydra / OSMO workflow templates.

When to Use / When NOT to Use

Use this skill when the user has an NCore V4 clip (or a USDZ + NRE artifact pair) on a Linux x86_64 host with an NVIDIA GPU and an NGC API key, and wants to train, render, generate aux data, export artifacts, insert/remove actors, run the gRPC server, or evaluate metrics. Concrete triggers:

Train a multi-camera + LiDAR AV clip into a renderable USDZ scene with 3DGUT (or 3DGRT ray-traced) Gaussians.
Generate NuRec auxiliary data (seg, depth, ego mask, DINOv2, LiDAR-seg visibility) using nre-tools.
Render frames locally (no server) along the training rig or a custom rig + offsets.
Render novel views via the sensorsim gRPC API (CARLA, Isaac Sim, AlpaSim, custom simulator), optionally with Difix artifact-removal.
Render LiDAR sweeps via render-grpc --lidar.
Export PLY / ego masks / depth / Poisson mesh / ground mesh / point clouds / cuboid tracks / NCore tracks / custom rig trajectories.
Insert / remove / replace 3D actors with export-external-assets
- render-grpc --edit-assets.
Render the gated HF dataset nvidia/PhysicalAI-Autonomous-Vehicles-NuRec.
Upgrade an old USDZ once (upgrade-artifact).
Inspect / evaluate (export-parsed-config, gaussian-statistics, eval-rendering-metrics, compute-metrics, eval-ground-mesh).
Browse a USDZ or PLY in the in-container viewer.

Read the full file on GitHub · 416 lines

Files

What ships with it

57 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.

Changes

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.

9d ago First seen · 416 lines · 150 tokens per session scan C fd2473df552d

Subscribe to this mod's changes

nre is a skill published in the GitHub repository NVIDIA/nurec-skills (36 stars, last pushed 6d ago), licensed Apache-2.0. It adds 150 tokens to every session and 5,433 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Creative and media Scientific

html-ppt-zhangzara-monochrome

A grant proposal on CRISPR base-editing for sickle-cell disease — the hypothesis, the approach, the milestones, and the risk. Built as a decision-grade academic research deck for grant review committee.

nexu-io/open-design · 54 tokens

html-ppt-zhangzara-pin-and-paper

A field-biology capstone on urban pollinator decline — the survey design, the data, the contribution, and the caveats. Built as a decision-grade coursework defense deck for faculty reviewers.

nexu-io/open-design · 51 tokens

paper-illustration

A workflow for generating academic illustrations, such as architecture diagrams and method visuals, with image generation and repeated review. Claude plans and checks the figure during the process.

wanshuiyin/Auto-claude-code-research-in-sleep · 67 tokens

paper-illustration-image2

Generate publication-quality academic illustrations through a local Codex app-server bridge that uses Codex native image generation. This is a separate experimental alternative to paper-illustration, intended for Claude Code users who want a GPT-image-style renderer without modifying the original skill.

wanshuiyin/Auto-claude-code-research-in-sleep · 59 tokens

paper2video

Turn a research paper, a paper2assets package, or an existing PPT deck into a narrated MP4 video by fully delegating slide authoring to the installed ppt-master skill and fully delegating rendering, subtitles, timeline assembly, and strict media QA to the installed pptx2video skill and its public CLI. Resolves one…

microsoft/ResearchStudio · 157 tokens

figure-composer

Compose one publication-grade multi-panel figure. Start from a one-line claim plus immutable data Artifact Version references, or inspect an existing figure and draft its outline directly. Plan a 12-column panel outline, delegate one worker per panel, compose and inspect the result, then run at most three adversarial…

aipoch/open-science · 93 tokens

nre