NVIDIA/nurec-skills

NVIDIA/nurec-skills

Description: Agent skills for Neural Reconstruction Engine

Language: Python

License: Apache-2.0

Stars: 7

Forks: 2

Open issues: 0

Created: 2026-05-20T18:40:04Z

Pushed: 2026-06-05T20:58:26Z

Default branch: main

Fork: no

Archived: no

README:

NuRec Skills

Agent skills for NVIDIA Omniverse NuRec and the surrounding neural-reconstruction stack — installable, version-pinned SKILL.md bundles that teach a coding agent how to ingest sensor data, train reconstructions, render novel views, harvest 3D objects, clean up artifacts, and find the right NVIDIA dataset for the job.

The canonical home is .

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](./skills/nurec-index/SKILL.md) — it routes any NuRec task to the right sibling skill below.

| Name | Folder | Pinned upstream | Purpose | |------|--------|-----------------|---------| | [nurec-index](./skills/nurec-index/SKILL.md) | skills/nurec-index/ | hand-curated | Router. Read first. Picks the right skill for any NuRec task. | | [physical-ai-datasets](./skills/physical-ai-datasets/SKILL.md) | skills/physical-ai-datasets/ | hand-curated | Catalog of every NVIDIA PhysicalAI-* dataset on Hugging Face — AV, robotics, NuRec scenes, benchmarks. | | [ncore](./skills/ncore/SKILL.md) | skills/ncore/ | upstream 2026.04 | Convert any sensor recording (cameras, LiDAR, radar, IMU, depth, stereo) into NCore V4 — the format NRE consumes. Includes a converter template. | | [nre](./skills/nre/SKILL.md) | skills/nre/ | NRE release_26.04 (nvcr.io/nvidia/nre/{nre,nre-tools}) | Train 3DGUT/3DGRT Gaussian reconstructions, render novel views (local or via gRPC), export PLY/mesh/depth, edit actors, evaluate quality. | | [asset-harvester](./skills/asset-harvester/SKILL.md) | skills/asset-harvester/ | `NVIDIA/asset-harvester` main (Apache-2.0) | Extract per-object 3D Gaussian Splat assets from sparse AV-clip views via SparseViewDiT + TokenGS. | | [nurec-fixer](./skills/nurec-fixer/SKILL.md) | skills/nurec-fixer/ | `nvidia/DiffusionHarmonizer` + `NVIDIA/harmonizer` | Post-process, evaluate, or fine-tune novel-view renders with NVIDIA DiffusionHarmonizer, the current public harmonizer for reconstruction artifacts and inserted-object appearance. |

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,…