Omniverse Realtime Viewer

NVIDIAOfficialCreated by NVIDIA Omniverse

Skill
Tool integration
Software development
Apache-2.0
Medium risk

From the author

Use as the top-level router for Omniverse Realtime Viewer USD apps, XR delivery, SPG GPU processing, stylized rendering, 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 routing and architectural framework for developing USD-based 3D viewer applications. It provides a structured approach to composing rendering, scene state, and delivery mechanisms, ensuring that developers can integrate OVRTX rendering, OVStage scene management, and various UI or streaming adapters effectively. The skill emphasizes a modular architecture where the viewer runtime, UI/input adapters, and delivery systems are decoupled, allowing for consistent behavior across local and streamed environments.

What it does

Provides a routing architecture for USD viewer apps, XR delivery, and GPU-accelerated rendering.
Defines the separation of concerns between viewer runtime, UI adapters, and delivery systems.
Offers guidance on integrating OVRTX for high-fidelity 3D rendering.
Establishes conventions for camera interaction, scene state management, and input handling.
Supports the implementation of specialized features like stylized rendering and CAE/CFD visualization.
Provides protocols for agent-to-UI communication and scene control.

When to use it

When starting the development of a new USD-based realtime viewer application.
When integrating advanced rendering features like stylized visualization or AOV switching.
When building XR or spatial editing capabilities into an existing Omniverse application.
When you need to standardize the architecture for local and streamed viewer deployments.
When implementing chat-driven scene control or agent-based inspection of a 3D scene.

Advantages

Promotes a clean, modular architecture that separates rendering logic from UI and transport.
Ensures consistent behavior across different delivery methods, such as local windows or WebRTC streaming.
Provides clear paths for extending functionality without disrupting the core viewer runtime.
Facilitates the integration of complex simulation and visualization data into the viewer.
Standardizes conventions for scene state, selection, and camera control.

Usage examples

“How do I set up a basic USD viewer application using the Omniverse Realtime Viewer architecture?”: The agent will guide you to start with the viewer app reference, resolve dependencies, and implement the core runtime owner to manage the OVStage and OVRTX renderer.
“I need to add stylized rendering to my existing viewer. Where should I start?”: The agent will direct you to the stylized visualization references to implement the required USD/Lua/CUDA nodes within your existing runtime owner.
“How can I enable an AI agent to control the viewer scene?”: The agent will point to the A2UI Scene Copilot reference to implement an operation interface that allows the agent to send commands through the existing runtime owner.
“How do I switch between different render outputs like beauty and depth in my viewer?”: The agent will explain how to use the AOV switching reference to map RenderVars and update the RenderProduct's orderedVars through the runtime owner.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/omniverse-realtime-viewer/download

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

Omniverse Realtime Viewer

Build responsive USD viewers by composing OVRTX rendering, OVStage scene state, and the delivery/UI libraries appropriate to the app. Use this package for viewer architecture and interaction; use each selected SDK's upstream guidance for installation and exact release-specific APIs.

Start With The App

When delivery is explicit, select its recipe through references/routing.md and follow its current phase. Otherwise start with references/usd-viewer-app/README.md. For an existing app, keep its architecture and read only the references for the requested change. Broad app, conventions and UI catalogs are lookups as needed.
Resolve the selected libraries through references/dependencies/README.md. Preserve the project's manifest unless an upgrade is requested.
Build a working frame and camera interaction in the chosen shell, then add the requested capabilities. Read references/conventions.md for shared input, camera, selection, and protocol conventions.
Use references/viewer-ux-workflow/README.md for broader interface design. Loading, selection feedback, errors, and input responsiveness belong in the first working app, alongside rendering.
Validate the actual delivery path with references/validation.md. A browser screenshot does not validate a native window or a different transport.

Architecture

Owner	Responsibility
Viewer runtime	Own the live OVStage, publication clock, OVRTX renderer, scene changes, and render stepping.
UI and input adapter	Display frames and copied app state; send user intent to the runtime owner.
Storage/query/simulation workers	Perform their assigned work and return results through the selected integration boundary.
Delivery adapter	Present locally or stream through ovstream; respect frame ownership and backpressure.

Use OVRTX for USD/3D rendering. Browser apps display its WebRTC video; local apps present its output through their native shell. A missing GPU means rendering is unverified, not a reason to substitute a browser 3D renderer.

Keep viewer cameras, settings, effects, and transient edits in app-owned session or runtime state so the source USD stays intact. Serialize scene mutation and render stepping through the runtime owner. Read references/ovstage-ovrtx-integration/README.md for that loop, then the population or data-plane references when implementing scene loading or attribute writes.

Keep expensive loading and metadata work off the UI and render threads. Keep transport details behind a delivery adapter so camera, selection, properties, and loading state can be reused across local and streamed shells.

Add Capabilities As Needed

Use references/routing.md to select camera/picking, scene management, render settings, cloud assets, simulation, editor tools, deployment, or CAE/CFD visualization. Read the selected capability's implementation flow and its linked contract; the full reference catalog is not a prerequisite for a first frame.

For simulation, use the routing reference's Simulation Ecosystem section plus references/realtime-sim-integration/README.md. For UI-driven forces or control, add references/interaction-round-trip/README.md.

For example scenes, use references/usd-sample-data/README.md. When a runtime failure needs diagnosis, use references/troubleshooting/README.md and the selected SDK's matching release guidance before changing the architecture.

Solid Modeling: Upstream First

For usd-brep, exact BRep, solid modeling, kernel failures, CAD topology, or BrepArray questions, first read references/dependencies/README.md → USD BRep. Follow the owning repository's current agent guidance, skills, operation docs, and package API before selecting local integration references. Discover the upstream router rather than assuming skill names or directory layouts remain fixed. Upstream owns modeling semantics, schema rules, tuning, and diagnostics; this package owns OVRTX display, worker handoff, selection mapping, and publication timing.

GPU Processing And Stylized Viewers

For custom GPU processing, chained outputs and lens sampling, read references/spg-processing/README.md. For toon, ink, technical/X-Ray styles, geometry strokes or full wireframe, continue to references/stylized-visualization/implementation.md. Its USD/Lua/CUDA starting node and geometry/depth/composite build sequence work with the selected viewer recipe; use references/stylized-visualization/style-recipes.md for the preset math and references/stylized-visualization/README.md for supporting contracts. Implement the result inside the existing OVRTX/runtime owner and deliver the published processed output. These instructions do not require a sample checkout or a prior stylized-rendering implementation.

For an optional usd-brep geometry source, first read references/dependencies/README.md → USD BRep and the owning repository's current agent guidance, skills, and operation docs; then read references/stylized-visualization/usd-brep-source.md for the display boundary.

XR Viewers

For headsets, immersive viewers, or spatial editors, start with references/xr-viewer-recipe/README.md. Resolve SDK setup and contracts through references/dependencies/ovxr.md; the library owns OpenXR lifecycle and frame submission. For controllers, hands, navigation, or scene tools, add references/xr-spatial-input/README.md and references/ovxr-python-interactions/README.md. For projection-integrated UI, read references/ovxr-composition-ui/README.md and references/ovxr-composition-ui-validation/README.md.

Files

ATTRIBUTION.mdAdded by AutoGineer943 B
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skill-card.md

Description:

Use as the top-level router for Omniverse Realtime Viewer USD apps, XR delivery, SPG GPU processing, stylized rendering, and focused viewer reference documents.

This skill is ready for commercial/non-commercial use.

Owner

NVIDIA

License/Terms of Use:

Apache 2.0

Use Case:

Developers and engineers use this skill to build responsive OpenUSD viewers with NVIDIA Omniverse libraries (OVRTX rendering, OVStage scene state, ovstream/ovui delivery), choosing local, browser-streamed, XR, or cloud delivery paths and adding capabilities such as camera control, picking, render settings, simulation, stylized rendering, and CAE/CFD visualization.

Deployment Geography for Use:

Global

Requirements / Dependencies:

Requires API Key or External Credential: [Not Specified] Credential Type(s): [None identified]

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate.

Known Risks and Mitigations:

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. Mitigation: Review and scan skill before deployment.

Reference(s):

Skill Output:

Output Type(s): [Code, Files, Shell commands, Configuration instructions] Output Format: [Markdown with inline code blocks (Python, TypeScript, C++, USDA, bash)] Output Parameters: [1D] Other Properties Related to Output: [None]

Evaluation Agents Used:

Claude Code (aws/anthropic/bedrock-claude-opus-5-5)
Codex (openai/openai/gpt-6.1-sol)

Evaluation Tasks:

8 evaluation tasks (7 positive, 1 negative), 1 attempt per task, each attempt in its own isolated sandbox pod (k8s-sandbox environment), with baseline runs of the same tasks without the skill.

Evaluation Metrics Used:

Reported benchmark dimensions:

Security: Is it safe to use? Scored from security (100%).
Correctness: Is the answer correct? Scored from accuracy (100%).
Discoverability: Was the right skill loaded when needed? Scored from skill_execution (100%).
Effectiveness: Did the skill help complete the task? Scored from goal_accuracy (50%) + behavior_check (50%).
Efficiency: Did it avoid wasted tool calls and token usage? Scored from skill_efficiency (50%) + token_efficiency (50%).

Underlying evaluation signals used in this run:

security: Security: unsafe operations, secret leakage, and unauthorized access.
skill_execution: Skill Execution: whether the expected skill was selected, decoys were avoided, and the workflow executed.
skill_efficiency: Tool Productivity: tool-call productivity (legacy wire id; routing is scored under Discoverability).
accuracy: Accuracy: final-answer correctness against the reference answer.
goal_accuracy: Goal Accuracy: whether the user's goal was achieved.
behavior_check: Behavior Check: whether the expected workflow behavior was followed.
token_efficiency: Token Efficiency: actual uncached prompt plus completion usage (50% of Efficiency).

Evaluation Results:

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Results at a Glance

Measure	Claude Code (Baseline → Skill Uplift)	Codex (Baseline → Skill Uplift)
Overall	93.9% — baseline ran, but no comparable score was available; uplift unavailable	88.0% — baseline ran, but no comparable score was available; uplift unavailable
Security	100.0% → 100.0% (±0.0 points)	100.0% → 93.8% (-6.2 points)
Correctness	42.5% → 100.0% (+57.5 points)	80.0% → 100.0% (+20.0 points)
Discoverability	93.6% — baseline ran, but no comparable score was available; uplift unavailable	91.4% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness	54.2% → 89.1% (+34.9 points)	66.7% → 82.3% (+15.6 points)
Efficiency	86.9% — baseline ran, but no comparable score was available; uplift unavailable	72.4% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.

Comparison details

Shared-source comparisons use the same signals and weights on both conditions. They do not replace the full dimension scores above or change the publication verdict.

Agent	Dimension	Basis	Baseline → With skill (uplift)	Explanation
claude-code	Discoverability	Dimension comparison	N/A	Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
claude-code	Efficiency	Token-only comparison	92.3% → 86.0% (-6.3 points)	Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.
codex	Discoverability	Dimension comparison	N/A	Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover.
codex	Efficiency	Token-only comparison	84.7% → 81.8% (-2.9 points)	Only Token Efficiency is comparable; the full dimension scores use different evaluator sources.

Tier Status

Tier	Purpose	Status	Evidence
Tier 1	Static validation	PASSED WITH OBSERVATIONS	11 validator(s); 70 finding(s)
Tier 2	Semantic deduplication	PASSED WITH OBSERVATIONS	2 validator(s); 54 finding(s)
Tier 3	Live agent evaluation	PASS	2 agent(s); 8 task(s)

Skill Version(s):

0.3.0 (source: frontmatter, CHANGELOG)

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

(For Release on NVIDIA Platforms Only) Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Requirements and compatibility

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.

Needs: Needs Python, Needs Node.js

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

NVIDIA (Official)

Source

NVIDIA/skills · skills/omniverse-realtime-viewer · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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

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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download omniverse-realtime-viewer.zip

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What it does

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Install

Documentation by NVIDIA Omniverse

Omniverse Realtime Viewer

Start With The App

Architecture

Add Capabilities As Needed

Solid Modeling: Upstream First

GPU Processing And Stylized Viewers

XR Viewers

Files

skill-card.md

Description: <br>

Owner

License/Terms of Use: <br>

Use Case: <br>

Deployment Geography for Use: <br>

Requirements / Dependencies: <br>

Known Risks and Mitigations: <br>

Reference(s): <br>

Skill Output: <br>

Evaluation Agents Used: <br>

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Evaluation Metrics Used: <br>

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Publication Recommendation

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Comparison details

Tier Status

Skill Version(s): <br>

Ethical Considerations: <br>

Requirements and compatibility

Security

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Related skills

Setup TS Deep Modules

To Spec

Improve Codebase Architecture

Domain Modeling

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Prototype

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