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Omniverse Realtime Viewer

OpenAIOfficialCreated by NVIDIA OmniverseDistributed by OpenAI

  • Skill
  • Meta (authoring, orchestration, planning, memory)
  • Software development
  • Apache-2.0
  • Medium risk
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From the author

Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.
— the description by NVIDIA Omniverse

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Omniverse Realtime Viewer skill acts as a central orchestrator for developing USD-based viewer applications. It provides a structured approach to selecting implementation recipes, enforcing architectural conventions, and managing dependencies. By utilizing a set of focused reference documents, it guides developers through the lifecycle of building, configuring, and validating viewer applications, ensuring consistent behavior across different delivery paths such as local workstations or streaming environments.

What it does

  • Routes development requests to specific implementation recipes based on the target delivery path.
  • Enforces architectural rules for USD rendering, stage management, and UI integration.
  • Provides guidance on camera selection, input handling, and viewport interaction.
  • Standardizes the integration of AOV switching and render product configuration.
  • Facilitates the capture of validation evidence for viewer readiness.
  • Maintains separation between user-provided USD files and application-specific session state.

When to use it

  • When initiating the development of a new USD viewer application within the Omniverse ecosystem.
  • When determining the appropriate delivery path for a viewer, such as local native or streaming.
  • When needing to implement consistent camera, selection, or UI behavior across viewer projects.
  • When integrating advanced rendering features like AOV switching into an existing viewer.

Advantages

  • Ensures architectural consistency across diverse viewer implementations.
  • Provides a clear, modular path for adding specific capabilities like input routing or data display.
  • Promotes clean separation of concerns between stage data and viewer-owned state.
  • Standardizes the validation process for consistent delivery.

Usage examples

“I need to build a browser-based viewer for USD files.”
The agent will route you to the streaming viewer recipe, providing guidance on WebRTC stream setup, UI integration, and viewport interaction.
“How do I implement AOV switching in my local USD viewer?”
The agent will provide the AOV switching reference, detailing how to map render vars and update the display state on the server.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/openai/omniverse-realtime-viewer/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip omniverse-realtime-viewer.zip -d .agents/skills

Documentation by NVIDIA Omniverse

<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->

<!-- SPDX-License-Identifier: Apache-2.0 -->

Omniverse Realtime Viewer

This is the top-level entry point for the Omniverse Realtime Viewer skill package. It is self-contained: all required routing, conventions, and validation guidance live in the selected references.

Use the focused reference documents as implementation recipes. This file chooses the right recipes and preserves the architectural rules that must hold across all generated viewer apps.

Instructions

Start by classifying the requested viewer, then read only the references needed for that delivery path and feature set. Implement the render path first, layer interaction and UI behavior on top of it, and finish by capturing validation evidence from references/validation.md.

Read Order

  1. Read references/routing.md to choose the delivery path and focused references.

  2. Read references/conventions.md before implementing camera, input, selection, viewport, streaming protocol, scene loading, or environment behavior.

  3. For broad viewer requests, read references/usd-viewer-app/README.md.

  4. If the delivery path is unclear, read references/streaming-vs-local/README.md.

  5. If the prompt includes layout, panels, controls, inspectors, status, or UX, read references/viewer-ux-workflow/README.md and then the focused viewer UI references. This applies to React/WebRTC, Tauri, Electron, ovui, ovwidgets, and Dear ImGui apps; "frontend" means user-facing UI, not only browser UI.

  6. For viewport interaction, read references/viewer-input-routing/README.md before references/camera-controls/README.md, references/native-picking-selection/README.md, or references/object-selection/README.md.

  7. Read only the focused capability references needed for the requested app.

  8. Use references/validation.md to capture review evidence before handoff.

Non-Negotiables

  • Use ovrtx for all USD and 3D rendering.

  • Browser apps display an ovstream WebRTC video stream plus UI. The browser does not render USD geometry.

  • Do not substitute WebGL, Three.js, Babylon.js, PlayCanvas, A-Frame, model-viewer, react-three-fiber, glTF browser viewers, or other client-side 3D renderers.

  • If local validation cannot run because the GPU/runtime environment is absent, scaffold the ovrtx path and document the runtime requirement. Do not add a browser-renderer fallback.

  • Keep user USD files unmodified. Viewer cameras, render products, render vars, settings, selection metadata, and runtime state belong in session/composite layers or app state.

  • Keep one owner for renderer.step(), stage mutation, native picking, selection writes, and live attribute writes.

  • Keep dependency acquisition in references/dependencies/README.md and deployment choices in references/cloud-deployment/README.md; do not duplicate package locations or deployment setup.

Focused Reference Families

  • Entry points and recipes: references/usd-viewer-app/README.md, references/streaming-viewer-recipe/README.md, references/ovui-local-viewer-recipe/README.md, references/streaming-vs-local/README.md, references/electron-shm-viewer/README.md, references/ovwidgets-editor-shell/README.md.

  • Rendering and stage: references/ovrtx-rendering/README.md, references/stage-loading/README.md, references/stage-management/README.md, references/render-settings/README.md, references/aov-switching/README.md, references/stage-hierarchy/README.md, references/stage-queries/README.md, references/stage-attribute-reads/README.md, references/prim-transform-safety/README.md, references/usd-sample-data/README.md.

  • Delivery and runtime: references/streaming-server/README.md, references/streaming-client/README.md, references/streaming-messages/README.md, references/streaming-lifecycle/README.md, references/local-viewer/README.md, references/tauri-local-viewer/README.md, references/cpp-native-viewer/README.md, references/headless-shm-cli/README.md, references/viewer-backend-interface/README.md, references/webgl-shm-transport/README.md.

  • Viewer UI/UX: references/viewer-ux-workflow/README.md, references/viewer-layout-patterns/README.md, references/viewer-control-patterns/README.md, references/viewer-data-view-patterns/README.md, references/viewer-feedback-status/README.md.

  • Interaction: references/viewer-input-routing/README.md, references/camera-controls/README.md, references/object-selection/README.md, references/native-picking-selection/README.md, references/selection-feedback/README.md, references/selection-animation/README.md, references/transform-manipulator/README.md, references/gl-viewport-overlay/README.md, references/ovui-library/README.md, references/prim-pick-effects/README.md, references/prim-info-display/README.md, references/viewport-overlays/README.md.

  • Infrastructure: references/dependencies/README.md, references/windows-native-setup/README.md, references/cloud-assets/README.md, references/cloud-deployment/README.md, references/troubleshooting/README.md.

Build Workflow

  1. Classify the prompt by delivery path, target user, required capabilities, runtime environment, validation needs, and explicit constraints.

  2. Select a small reference set. Start with the recipe or routing reference, then add focused capabilities such as camera, picking, hierarchy, properties, render settings, transform tools, cloud assets, or deployment.

  3. Read selected references before writing app code. Follow their build order, import order, data-channel contracts, and renderer ownership rules.

  4. Implement the core render path first, then input routing and camera, then selection and data panels, then scene/settings features, then packaging or deployment.

  5. Treat the selected references as the behavior contract for API shape, compatibility, and generated project structure.

  6. Capture validation evidence before calling the viewer ready.

Examples

  • For a browser viewer request, use the streaming recipe references plus camera, picking, hierarchy, properties, render settings, and stream-status references.

  • For a local workstation viewer request, use the local or native delivery references plus renderer setup, stage loading, viewport input, and validation.

Completion Checklist

  • Selected references match the user's intent and delivery path.

  • No code path uses a browser-side 3D renderer for USD.

  • The generated app has one clear owner for render stepping and stage mutation.

  • User USD files remain untouched by viewer-owned session data.

  • Camera, input, selection, scene loading, and stream behavior follow references/conventions.md.

  • Setup/build/run results and visual interaction evidence are captured with references/validation.md.

Files

references/huggingface-usd/README.md

Hugging Face USD Asset Acquisition

Prerequisites

Install the Hugging Face SDK (provides both Python API and hf CLI):

pip install -U "huggingface_hub[cli]"

Authentication

ScenarioAuth needed?
Public datasets (e.g., NVIDIA SimReady)No
Gated datasetsAccept terms on HF website + token
Private datasetsAccess granted + token
Higher rate limitsToken recommended

Setup:

# Interactive (stores at ~/.cache/huggingface/token)
hf auth login

# Or via environment
export HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxx"

# Or inline
hf download <dataset> --token hf_xxx ...

Create tokens at: https://huggingface.co/settings/tokens — Read scope is sufficient.

Downloading Assets

Single file
hf download <org>/<dataset> <path/to/file.usd> --repo-type dataset --local-dir assets
Entire asset folder (includes dependencies like textures/sublayers)
hf download <org>/<dataset> --repo-type dataset --local-dir assets --include "<folder>/*"
Multiple patterns (batch)
hf download <org>/<dataset> --repo-type dataset --local-dir assets \
  --include "Props/assembly/Pallets_*/*" \
  --include "Props/general/Warehouse/Forklift*/*"
Full dataset
hf download <org>/<dataset> --repo-type dataset --local-dir assets

Always use --repo-type dataset — HF defaults to model repos otherwise.

Hero Stage Discovery

When a user provides a Hugging Face dataset URL and wants "the USD to open", use these heuristics to identify the correct entry-point file (the "hero stage"):

Decision process

  1. List root-level files in the repository (not in subdirectories)

  2. Filter for USD files (.usd, .usda, .usdc)

  3. If exactly one root-level USD exists — that's almost certainly the hero

  4. If multiple exist, score them using the signals below

Scoring signals (strongest first)
SignalConfidenceHow to check
Has a .thumbs/256x256/<filename>.png thumbnailVery highCheck if .thumbs/ folder contains a matching PNG
Has a defaultPrim setVery highOpen with pxr → stage.GetDefaultPrim() is valid
References sublayersHighstage.GetRootLayer().subLayerPaths is non-empty
Name matches dataset nameHighe.g., physical_ai_simready_warehouse_01.usd in dataset PhysicalAI-SimReady-Warehouse-01
Larger file size than siblingsMediumScenes are bigger than individual props
Name contains "scene", "stage", "world", "main"MediumCommon naming conventions
A SubLayers/ folder exists in the repoSupportingConfirms the root USD is a composed scene
What is NOT the hero

  • Files inside Props/ — these are individual assets/components

  • Files inside SubLayers/ — these are scene partitions referenced by the hero

  • Files inside Materials/ or Textures/ — supporting data

  • Files with _physics suffix — physics layer overrides, not visual entry points

Without pxr available

If you can't inspect USD structure programmatically, rely on:

  1. Position (root-level, not in a subdirectory)

  2. Naming (matches dataset name)

  3. Thumbnail presence (.thumbs/256x256/)

  4. Context (does a SubLayers/ folder exist?)

These four signals together are sufficient to identify the hero in all known NVIDIA SimReady datasets.

Dependency Resolution

Once you identify the hero stage, determine what else to download:

Hero characteristicsWhat to download
No sublayers, no external referencesJust the hero file — self-contained
Has sublayers onlyHero + SubLayers/*
Has sublayers + payload references to Props/Full dataset for complete rendering
Unknown/complexDownload everything to be safe

For a quick structural preview (walls/floor render, some props missing):

hf download <org>/<dataset> --repo-type dataset --local-dir assets \
  --include "<hero>.usd" --include "SubLayers/*" \
  --include "Props/assembly/*" --include "Props/modular/*"

For full fidelity (all props, textures, materials resolve):

hf download <org>/<dataset> --repo-type dataset --local-dir assets

Catalog Discovery

Many NVIDIA datasets include a CSV catalog at the repo root. Download it to browse available assets:

hf download <org>/<dataset> <catalog>.csv --repo-type dataset --local-dir .

For nvidia/PhysicalAI-SimReady-Warehouse-01, the catalog is physical_ai_simready_warehouse_01.csv with columns:

  • asset_name — human-friendly name

  • relative_path — path within the repo (use for download commands)

  • classification — category (e.g., "Prop general hand manipulation")

  • label — object type (e.g., "bottle", "Forklift", "Cardboard Box")

Parse it, filter by label or classification, then feed relative_path values into hf download.

End-to-End Example

Developer: "I want to use the warehouse stage at huggingface.co/datasets/nvidia/PhysicalAI-SimReady-Warehouse-01 as my test stage"

  1. Parse URL → dataset is nvidia/PhysicalAI-SimReady-Warehouse-01

  2. List root files → one USD: physical_ai_simready_warehouse_01.usd (27 KB)

  3. Check signals: ✓ root-level, ✓ name matches dataset, ✓ has thumbnail, ✓ SubLayers/ exists

  4. Conclusion: that's the hero stage

  5. Inspect (if pxr available): defaultPrim=/World, upAxis=Z, metersPerUnit=1.0, 6 sublayers

  6. Download for development:

# Structural preview (~24 MB)
hf download nvidia/PhysicalAI-SimReady-Warehouse-01 --repo-type dataset --local-dir ./test-assets \
  --include "physical_ai_simready_warehouse_01.usd" \
  --include "SubLayers/*" \
  --include "Props/assembly/*" \
  --include "Props/modular/*"

# Open: ./test-assets/physical_ai_simready_warehouse_01.usd
# Default prim: /World | Up axis: Z | Meters per unit: 1.0

Curl Fallback (No Python Needed)

For environments where installing packages isn't practical:

# Public dataset file
curl -L --fail -o asset.usd \
  "https://huggingface.co/datasets/<org>/<dataset>/resolve/main/<path>?download=true"

# Authenticated (gated/private)
curl -L --fail -H "Authorization: Bearer $HF_TOKEN" \
  -o asset.usd \
  "https://huggingface.co/datasets/<org>/<dataset>/resolve/main/<path>?download=true"

Verify you got real data: head -c 8 asset.usd should show PXR-USDC (binary) or #usda 1.0 (ASCII), not a git-lfs pointer.

Important Notes

  • huggingface_hub handles Xet-backed repos transparently (many newer HF repos use Xet storage)

  • Downloads cache in ~/.cache/huggingface/ — use --local-dir to place files where you need them

  • Large downloads resume automatically on retry

  • A single .usd may reference textures/sublayers via relative paths — preserve directory structure

  • Individual SimReady props are 1–100 KB; full datasets can be 10+ GB — be selective with --include

Viewer Integration

After downloading the hero stage:

  1. Ensure directory structure is preserved (hero USD uses relative paths like ./SubLayers/...)

  2. Open the hero .usd file — it will resolve references to sublayers and props

  3. Key metadata for viewer configuration:

    • defaultPrim — root prim to load (usually /World)

    • upAxis — coordinate system (Z-up for SimReady)

    • metersPerUnit — scale factor (1.0 = real-world meters)

Known NVIDIA SimReady Datasets on HF

  • nvidia/PhysicalAI-SimReady-Warehouse-01 — 753 warehouse/manipulation props (~14.4 GB total)

    • Hero: physical_ai_simready_warehouse_01.usd

    • 6 sublayers (loading zone, sorting area, unloading/staging, metro racks, floorplan, transporter area)

    • 85 asset labels (Cardboard Box ×151, Warehouse ×43, Pallet ×28, Forklift ×5, etc.)

See also: usd-sample-data, stage-loading, stage-management, cloud-assets.

Requirements and compatibility

Written for: OpenAI Codex

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

The author says: Orchestrator skill. Downstream focused references may require NVIDIA GPUs, ovrtx, ovstream, ovui, OpenUSD, Python, Node/React, Tauri, Electron, C++, or cloud GPU deployment access depending on the selected viewer path.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Omniverse
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/nvidia/skills/omniverse-realtime-viewer · commit d420d13
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA Omniverse or OpenAI.

Download ZIP

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