NuRec Skills

NVIDIA Omniverse NuRec agent skills automate neural reconstruction for autonomous vehicles and robotics simulation, running the reconstruction and rendering workflow end to end.

The canonical home is .

Support

Usage questions and discussion: please post on the NVIDIA Developer Forum (Omniverse / NuRec).

Code-level bugs, documentation issues, and feature requests: file a GitHub issue using the appropriate template. The relevant NVIDIA responder is assigned automatically.

Security vulnerabilities: use NVIDIA’s Vulnerability Disclosure Program. Do not file security issues publicly in this repository.

What’s a skill?

A skill is a single Markdown file (plus a few companion files) that an agent reads on demand to gain task-specific knowledge. Each skill in this repo follows the agentskills.io convention: a YAML frontmatter block (name, description, trigger keywords, compatibility, upstream pointer) followed by a hand-curated recipe. Agents that support the standard — Cursor, Claude Code, Codex, and others — can resolve a skill by name, regardless of where the file is on disk.

These skills are thin coordination layers. They don’t redistribute NVIDIA source; instead, they teach an agent how to drive the public NGC containers, GitHub repos, and HuggingFace artifacts that make up the NuRec stack.

Skills in this repo

Start with nurec-index — it routes any NuRec task to the right sibling skill below.

Repo layout

.agents/skills/  ──►  skills/                # symlink; both paths resolve to the same tree
skills/
├── nurec-index/
│   ├── SKILL.md                             # The router. Read first.
│   └── references/
│       ├── workflows.md
│       ├── teardown.md
│       └── discovery.md
├── physical-ai-datasets/
│   └── SKILL.md
├── ncore/
│   ├── SKILL.md
│   └── ncore_template/                      # Converter scaffold for new sensor formats
├── nre/
│   ├── SKILL.md
│   ├── references/                          # CLI / configuration / cookbook / rig JSONs / etc.
│   └── scripts/                             # validate_setup.py, session_warm_server.sh, …
├── asset-harvester/
│   ├── SKILL.md
│   ├── references/
│   ├── scripts/
│   └── tests.yaml
└── nurec-fixer/
    ├── SKILL.md
    ├── references/
    ├── scripts/
    └── tests.yaml

Each skill is a flat folder rooted at skills//SKILL.md. The .agents/skills/ path is a symlink onto skills/, so cross-skill links like ../nre/SKILL.md keep resolving regardless of which prefix an agent indexes against. Upstream versions (NRE container tag, Asset Harvester commit, DiffusionHarmonizer release branches) are recorded in each skill’s frontmatter metadata: block — bump those when upstream releases shift.

Using these skills

Most modern agent runtimes already auto-discover skills under skills/, .claude/skills/, .cursor/skills/, or ~/.cursor/skills/. The two common ways to consume this repo:

1. Drop the repo next to your project

Clone into your project (or a parent directory the agent indexes):

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

Then ask your agent to do anything in the trigger surface — e.g. “use NuRec to render this clip”, “convert my ROS 2 bag to NCore”, “harvest 3D assets from this driving log”. The agent picks the right skill via nurec-index and follows the recipe.

2. Install a skill into your user-space

Symlink (or copy) one or more skills into your runtime’s user-space skills directory. For Cursor:

mkdir -p ~/.cursor/skills
ln -s "$(pwd)/skills/nurec-index"          ~/.cursor/skills/nurec-index
ln -s "$(pwd)/skills/physical-ai-datasets" ~/.cursor/skills/physical-ai-datasets
ln -s "$(pwd)/skills/ncore"                ~/.cursor/skills/ncore
ln -s "$(pwd)/skills/nre"                  ~/.cursor/skills/nre
ln -s "$(pwd)/skills/asset-harvester"      ~/.cursor/skills/asset-harvester
ln -s "$(pwd)/skills/nurec-fixer"          ~/.cursor/skills/nurec-fixer

Adjust the destination directory (~/.claude/skills, etc.) for other runtimes.

Prerequisites

These skills drive external NVIDIA infrastructure. Each skill lists its own prerequisites in detail; the headline ones:

• OS / arch: Linux x86_64 with NVIDIA drivers (CUDA 12.x). aarch64 is not supported by the NRE containers.
• GPU: Ampere or newer (compute capability ≥ 8.0). 16 GB VRAM is the practical floor for harmonizer inference; 24–48 GB+ is recommended for NRE training, and multi-GPU hosts are expected for DiffusionHarmonizer training.
• Containers: Docker 23+ and the NVIDIA Container Toolkit are required for nre, nre-tools, and nurec-fixer. NGC API key is required to pull nvcr.io/nvidia/nre/* and may also be required for nvcr.io/nvidia/cosmos/* container pulls.
• Hugging Face: an HF_TOKEN is required for any gated dataset or model (nvidia/PhysicalAI-Autonomous-Vehicles*, nvidia/DiffusionHarmonizer, nvidia/DiffusionHarmonizer-Dataset, nvidia/asset-harvester, …).
• Python / conda: required for the Asset Harvester install path and for the NCore in-process API (pip install nvidia-ncore).

Third-party dependencies and bundled code

The skills in this repository are Markdown instructions plus lightweight NVIDIA-authored helper files. They do not require third-party libraries to be discovered or read, and they do not vendor or redistribute third-party OSS source code or third-party binary dependencies. Bundled validation scripts use only the Python standard library and host tools already called out by the relevant skill.

Some workflows documented by the skills instruct users to install or run external upstream tools, containers, Python packages, models, or datasets from their original distribution channels. Those upstream artifacts are not redistributed by this repository and retain their own licenses. See THIRD_PARTY_NOTICES.md for the repository’s third-party notice statement.

Upstream sources of truth

Each skill is thin; the canonical artifacts live upstream:

• NCore — (spec: )

• NRE / NuRec containers — nvcr.io/nvidia/nre/nre, nvcr.io/nvidia/nre/nre-tools (NGC); official documentation page

• Asset Harvester — (paper: ; demo: )

• DiffusionHarmonizer — (open-source code: ; paper: )

• Physical AI datasets — (filter PhysicalAI-); curated collection

When upstream releases shift, refresh the affected files in the skill’s references/ and scripts/ folders and bump the metadata: block (and version:) in its SKILL.md frontmatter.

Contributing

This project is not currently accepting external pull requests. Code-level bugs, documentation issues, and feature requests are welcome through the issue templates. For usage questions and security reports, use the channels listed in Support.

• Frontmatter follows the agentskills.io schema (name, description, version, license, metadata). Trigger keywords belong inside description: so the runtime indexes them.
• New skills go under skills// as a flat layout — SKILL.md at the root, with optional references/, scripts/, and tests.yaml siblings. Pin the upstream version inside the skill’s frontmatter metadata: block, not in the folder path.
• After adding or renaming a skill, update the nurec-index router so it knows how to route to it.

License

This repository is released under dual CC-BY-4.0 AND Apache-2.0 terms. The full Apache-2.0 license text is distributed in LICENSE, and the full Creative Commons Attribution 4.0 International license text is distributed in LICENSE-CC-BY-4.0.

• Code-only files (for example, helper scripts, workflow YAML, test YAML, environment examples, and the ncore_template/ Python package) are licensed under Apache-2.0. NVIDIA authored source files carry SPDX-License-Identifier: Apache-2.0 headers.
• Mixed documentation files (for example, SKILL.md, reference Markdown files, and this README) are licensed under the repository’s dual CC-BY-4.0 AND Apache-2.0 terms. The skill frontmatter records this as license: CC-BY-4.0 AND Apache-2.0.

The skills only drive upstream NVIDIA artifacts (NGC containers, GitHub repos, Hugging Face models and datasets). Those upstream artifacts retain their own licenses — see each skill’s metadata: block for the upstream pointer.