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SkillAtlasSkill 详情
physical-ai-neural-reconstruction
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 52
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
其他
高风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 存在潜在风险命令，请谨慎安装。
- 扫描发现：5 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/physical-ai-neural-reconstruction" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/physical-ai-neural-reconstruction" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/physical-ai-neural-reconstruction" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/physical-ai-neural-reconstruction" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/physical-ai-neural-reconstruction" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: physical-ai-neural-reconstruction
description: "Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup."
license: Apache-2.0
version: "0.3.0"
tools:
- Read
- Shell
compatibility: >-
Router skill; downstream sibling skills require Docker, NVIDIA Container
Toolkit, GPU, NGC API key, Hugging Face token with PhysicalAI gated
licenses accepted, Python 3.10+, and `huggingface_hub`. Optional:
CARLA / Isaac Sim 5.1 / AlpaSim for simulator integration over
`serve-grpc`.
metadata:
author: NVIDIA Physical AI
tags:
- physical-ai
- nurec
- neural-reconstruction
upstream:
repo: https://github.com/NVIDIA/nurec-skills
branch: main
skills_dir: .agents/skills/
skills_dir_alias: skills/
index_skill: .agents/skills/SKILL.md
index_skill_name: nurec-index
sibling_skills:
- name: physical-ai-datasets
folder: physical-ai-datasets/
upstream: https://huggingface.co/nvidia
- name: ncore
folder: ncore/
upstream: https://github.com/NVIDIA/ncore
- name: nre
folder: nre/
upstream: nvcr.io/nvidia/nre/nre
- name: asset-harvester
folder: asset-harvester/
upstream: https://github.com/NVIDIA/asset-harvester
- name: nurec-fixer
folder: nurec-fixer/
upstream: https://github.com/NVIDIA/harmonizer
hf_model: https://huggingface.co/nvidia/DiffusionHarmonizer
upstream_clone_path: "${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}/nurec-skills"
upstream_override_env: NUREC_SKILLS_UPSTREAM_ROOTPhysical AI Neural Reconstruction (NuRec) Router
Purpose
This is a thin router for NVIDIA Neural Reconstruction (NuRec)
requests. It points at the upstream nurec-index skill at
https://github.com/NVIDIA/nurec-skills and its five sibling skills
(physical-ai-datasets, ncore, nre, asset-harvester,
nurec-fixer). Use this skill to:
- Identify which upstream sibling skill answers a NuRec question.
- Locate, clone, or refresh the canonical
nurec-skillscheckout. - Order multi-step NuRec workflows (data → conversion → train → render → cleanup) before opening the upstream recipe.
The canonical recipes (training, rendering, data conversion, dataset downloads, object harvesting, frame cleanup) live in the upstream sibling skills. Never copy or reconstruct their commands here.
Do NOT use this skill for:
- SimReady packaging of CAD or source meshes → use
omniverse-cad-to-simready. - Generic USD performance tuning unrelated to NuRec → use
omniverse-usd-performance-tuning. - AKS / OSMO / NIM Operator infrastructure setup → use
physical-ai-infrastructure-setup-and-resilient-scaling.
When to Use
Read this skill first whenever a user mentions any of:
nurec, nurec router, nurec index, neural reconstruction,
neural reconstruction engine, NRE, 3DGUT, 3DGRT, USDZ,
NCore V4, sensor sim, novel view synthesis,
PhysicalAI-Autonomous-Vehicles-NuRec, PhysicalAI-NuRec-PPISP,
Cosmos-Drive-Dreams, asset harvester, nurec fixer,
DiffusionHarmonizer, harmonizer, difix, difix3d, serve-grpc,
render-grpc, warm serve-grpc, nre thin client, batch_render_rgb,
nurec teardown, "where do I start with NuRec", "which NuRec skill
should I use for X?".
Decide which upstream sibling skill answers the question, fetch it (see Locate and fetch the upstream skills), then follow that skill's body.
Prerequisites
Router skill itself has no runtime prerequisites beyond git for
fetching the upstream. Downstream sibling skills require:
- Docker + NVIDIA Container Toolkit + GPU — for
nre,nre-tools, andnurec-fixercontainers (nvcr.io/nvidia/nre/nre,nvcr.io/nvidia/nre/nre-tools,nvcr.io/nvidia/cosmos/cosmos-predict2-container:1.2). - NGC API key (
NGC_API_KEY) — for pulling NGC containers. - Hugging Face token (
HF_TOKEN) with thenvidia/PhysicalAI-*,nvidia/DiffusionHarmonizer, andnvidia/asset-harvestergated licenses accepted in advance on Hugging Face. - Python 3.10+ with
huggingface_hubinstalled. - (Optional) CARLA, Isaac Sim 5.1, or AlpaSim for simulator
integration over
serve-grpc.
Verify secrets safely (do not echo values):
hf auth whoami
[ -n "${HF_TOKEN:-}" ] && echo "HF_TOKEN length=${#HF_TOKEN}" || echo "HF_TOKEN unset"
[ -n "${NGC_API_KEY:-}" ] && echo "NGC_API_KEY length=${#NGC_API_KEY}" || echo "NGC_API_KEY unset"
See references/secrets-handling.md
for the bash anti-patterns to avoid.
What is NuRec?
NuRec (NVIDIA Omniverse Neural Reconstruction) takes camera, LiDAR, radar, or stereo recordings — typically from a self-driving car or a robot — and turns them into a 3D scene you can re-render from any viewpoint. Names that come up a lot:
- NRE — "Neural Reconstruction Engine". NuRec is the product; NRE
is the engine that trains and renders. Both route to the upstream
nreskill. - USDZ — the file format of a trained scene. A zip archive that Omniverse, Isaac Sim, and CARLA can open.
- NCore V4 — the input format NRE consumes. Raw recordings must be converted to NCore V4 before training.
- 3DGUT / 3DGRT — the two 3D Gaussian Splatting flavours used internally by NRE. The default Hydra recipe picks one; most users never set it manually.
A typical NuRec project has three stages:
- Get the input — convert your own recording to NCore V4
(
ncore), or download a pre-converted dataset (physical-ai-datasets). - Train the reconstruction — feed NCore V4 to NRE; out comes a
USDZ (
nre). - Render new views — render images, videos, or LiDAR sweeps from
the USDZ (
nre).
Projects that just want to use an existing NVIDIA-published scene skip step 2.
Pick a skill
Match the user's goal in the left column and open the named upstream skill on the right. Arrows mean "do these in order".
| I want to… | Upstream skill |
|---|---|
| Find or download a NuRec dataset NVIDIA has published | physical-ai-datasets |
| Convert my own camera / LiDAR / radar / depth / stereo recording into NCore V4 | ncore |
| Write a new converter for an unsupported sensor setup (drone, RGB-D, ROS 2 bag, COLMAP, ScanNet++) | ncore |
| Train a 3D reconstruction from an NCore clip | ncore → nre |
| Generate the extra inputs NRE needs (segmentation masks, depth, ego mask) | nre (uses the nre-tools container) |
| Render a USDZ along the original camera positions | nre |
| Render at full resolution / highest quality | nre (see "Quality presets") |
| Render along a shifted trajectory (e.g. car moved 3 m left) | nre |
| Render through a server so CARLA / Isaac Sim / AlpaSim / a custom simulator can ask for frames | nre (serve-grpc) |
| Render the same USDZ many times back-to-back from Python with minimal per-call latency | nre (warm serve-grpc + thin Python client / batch_render_rgb) |
| Render LiDAR sweeps (point clouds) from a USDZ | nre (render-grpc --lidar) |
| Skip training and just render a NuRec scene NVIDIA already built | physical-ai-datasets → nre |
| Extract individual 3D objects (cars, pedestrians) from a driving clip | asset-harvester |
| Add, remove, or replace cars / pedestrians in a NuRec scene | asset-harvester → nre |
| Clean up or harmonize rendered frames (ghosting, floaters, flicker, lighting/shadows) | nurec-fixer, or --enable-difix inside nre for inline rendering |
| Export the scene as a PLY, mesh, depth maps, ego mask, etc. | nre |
| Upgrade an old USDZ so newer NRE versions load it faster | nre (upgrade-artifact) |
| Open a USDZ or PLY in a browser viewer | nre (viewer / ply_viewer) |
| Measure rendering quality (PSNR, SSIM, LPIPS) against ground truth | nre (eval-rendering-metrics) |
| Benchmark different reconstruction methods on the same scenes | physical-ai-datasets (PhysicalAI-NuRec-PPISP) → nre |
| Train on multiple GPUs or on SLURM | nre (Workflow D) |
Common workflows
Six end-to-end workflows are documented in
references/workflows.md:
- A. Make a NuRec scene from your own recording.
- B. Use a NuRec scene NVIDIA has already trained.
- C. Add, remove, or replace 3D objects in a scene.
- D. Clean up rendered frames.
- E. Benchmark reconstruction quality.
- F. Connect NuRec to a simulator.
Open that file when the user's task spans more than one sibling skill.
Sibling skills (upstream)
| Name | Upstream folder | What it does |
|---|---|---|
physical-ai-datasets | .agents/skills/physical-ai-datasets/ | Catalog and download recipes for every NVIDIA Physical AI dataset on Hugging Face (driving, robotics, manipulation, NuRec scenes, benchmarks). |
ncore | .agents/skills/ncore/ | Converts any sensor recording to NCore V4 (the format NRE needs). Also covers writing a new converter. |
nre | .agents/skills/nre/ | The Neural Reconstruction Engine itself. Trains, renders (locally, via warm serve-grpc + thin Python client / batch_render_rgb, or to an external simulator), exports meshes / point clouds / depth, edits actors, evaluates quality. |
asset-harvester | .agents/skills/asset-harvester/ | Open-source Apache-2.0 pipeline that extracts individual 3D objects from sparse views in a driving clip and saves them as .ply Gaussian splats with metadata. |
nurec-fixer | .agents/skills/nurec-fixer/ | Standalone NVIDIA DiffusionHarmonizer workflow — public successor to the older Fixer / Difix3D+ recipes — that cleans rendered frames, harmonizes inserted actors, evaluates PSNR/LPIPS, and optionally fine-tunes the model. |
For naming overlaps (NRE vs Fixer, ncore vs nre, AV-NuRec vs
Cosmos-Drive-Dreams, NuRec vs SimReady) see
references/mix-ups.md.
Locate and fetch the upstream skills
Quick recipe (full version in
references/upstream-fetch.md):
UPSTREAM_ROOT="${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}"
mkdir -p "$UPSTREAM_ROOT"
if [ -d "$UPSTREAM_ROOT/nurec-skills/.git" ]; then
git -C "$UPSTREAM_ROOT/nurec-skills" fetch --tags
git -C "$UPSTREAM_ROOT/nurec-skills" checkout main
git -C "$UPSTREAM_ROOT/nurec-skills" pull --ff-only
else
git clone --depth 1 https://github.com/NVIDIA/nurec-skills.git \
"$UPSTREAM_ROOT/nurec-skills"
fi
test -f "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md"
Then read the upstream skill before running any mutating command:
cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/SKILL.md" # router
cat "$UPSTREAM_ROOT/nurec-skills/.agents/skills/<folder>/SKILL.md" # sibling
Local lookup order (try in order before the upstream clone):
.agents/skills/<name>/SKILL.md(Cursor, Codex, NemoClaw).claude/skills/<name>/SKILL.md(Claude Code).cursor/skills/<name>/SKILL.md(project-scoped)~/.cursor/skills/<name>/SKILL.md(personal skills)
Hard Rules
- Router only — do not duplicate upstream NuRec recipes here. Read the upstream sibling skill body before running any mutating command.
- Refer to sibling skills by their
name:(e.g.nre), not by repo path. Folder layouts can change; the name is portable. - Clone or refresh
https://github.com/NVIDIA/nurec-skillsunder the shared upstream root (${NUREC_SKILLS_UPSTREAM_ROOT:-${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams}}/nurec-skills). Do not scan broad developer workspaces such as~/Codesor reuse unrelated old clones. physical-ai-datasetscovers gated Hugging Face datasets. Do not bypass dataset license terms; the user must accept thePhysicalAI-*gated licenses on Hugging Face and provide a token before downloading.- Asset Harvester runs before packaging into a USDZ. Do not call
nre'sexport-external-assetson hand-rolled.plyfiles unless the user explicitly asks to skip Asset Harvester. - For artifact cleanup, prefer the built-in
--enable-difixpath innre. Route to the standalonenurec-fixeronly when the user needs the public code/model card, paired evaluation, fine-tuning, or fixes on previously rendered frames. - Do not invent NRE / NCore / DiffusionHarmonizer commands from
memory. Re-read the upstream sibling skill — versions move fast
(NRE
release_26.04is the current pinned tag). - This router does not deploy infrastructure. Route AKS / OSMO /
NIM Operator setup to
physical-ai-infrastructure-setup-and-resilient-scaling.
Limitations
- Router only. This skill never executes mutating NuRec commands. All training, rendering, conversion, and harmonization happens in upstream sibling skills.
- Upstream-pinned. Recipes live in
https://github.com/NVIDIA/nurec-skills, which evolves outside this repo. Stale clones can drift; alwaysgit pullthe upstream before relying on a sibling skill. - Gated content.
nvidia/PhysicalAI-*,nvidia/DiffusionHarmonizer, andnvidia/asset-harvesterrequire the user to accept license terms on Hugging Face first. The router cannot bypass this. - Heavy footprint. A complete NuRec workflow can leave 150 GB+
on disk. See
references/teardown.md. - NVIDIA-only stack. Requires an NVIDIA GPU plus the NVIDIA Container Toolkit. AMD / Intel / Apple Silicon are not supported.
- Not a SimReady pipeline. NuRec produces a renderable USDZ from
a recording; SimReady packaging of CAD or source meshes is a
different pipeline (see
omniverse-cad-to-simready).
Troubleshooting
| Error / symptom | Likely cause | Solution |
|---|---|---|
nurec-skills clone missing or empty | Upstream not fetched yet | Run the clone block in Locate and fetch the upstream skills |
403/401 pulling nvidia/PhysicalAI-* from HF | Gated license not accepted, or HF_TOKEN unset / wrong scope | Accept the gated license on Hugging Face, then hf auth login with a token that has read access |
denied: requested access to the resource is denied from nvcr.io/nvidia/nre/* | Missing or expired NGC_API_KEY | docker login nvcr.io with $oauthtoken / NGC_API_KEY; rotate the key at org.ngc.nvidia.com/setup/api-key if needed |
| NRE refuses to load a clip ("not valid NCore V4") | Recording was not converted | Run the ncore skill before invoking nre |
serve-grpc cold-start latency dominates a Python loop | One-shot Docker invocation per render | Use the nre warm serve-grpc + thin Python client (batch_render_rgb) recipe |
Output files are owned by root after a docker run | -u $(id -u):$(id -g) was missing | sudo chown -R "$(id -u):$(id -g)" <output_dir>; add the -u flag next time |
| Frames have ghosting / floaters / flicker after rendering | Inline cleanup not enabled | Re-render with nre --enable-difix, or post-process with nurec-fixer (DiffusionHarmonizer) |
Stale skill names (ncore-data-conversion, old nvidia/Fixer) in agent output | Out-of-date cached skill | Update references to ncore and nurec-fixer (DiffusionHarmonizer); see references/maintenance.md |
Bash anti-pattern ${HF_TOKEN:+yes}${HF_TOKEN:-no} echoed token value | Misuse of bash parameter expansion | Rotate the token; use hf auth whoami or length-only checks (see references/secrets-handling.md) |
Cross-skill teardown
A complete NuRec workflow can leave 150 GB+ on disk between
container images, model weights, code clones, conda envs, and output
directories. Each sibling skill has its own dedicated Teardown
section — read them in the order documented in
references/teardown.md when the user no
longer needs the workflow.
Keeping this router up to date
Procedure for adding new sibling skills, renames, or upstream URL
changes lives in references/maintenance.md.
Treat the upstream nurec-index at
https://github.com/NVIDIA/nurec-skills/blob/main/.agents/skills/SKILL.md
as authoritative; this skill mirrors only the picker tables, the
workflow ordering, and the upstream fetch recipe.
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