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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

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/cloud-deployment/README.md

Cloud Deployment

Triggers

Use this skill for requests mentioning deploy, OKAS 1, cloud deployment, session APIs, Docker, health checks, Brev, launchables, or remote deployment.

Use the permanent omniverse-realtime-viewer launchable: https://brev.nvidia.com/launchable/deploy?launchableID=env-3EHjQXkUNYv2pOa3idjBeJOauvH

Instance type: g5.xlarge (A10G, 23 GB VRAM).

Prerequisites
  • Brev account with an omniverse-realtime-viewer-launchable instance (A10G or better)

  • TCP/UDP Port Rules open: 80, 1024, 47998, 49100

  • SSH access configured (uses Brev SSH key, typically ~/.brev/brev.pem; run brev refresh to regenerate)

Architecture

Two access modes are supported:

Option A: HTTPS via Brev domain (port 80 + Caddy WSS proxy)

Brev exposes port 80 as https://frontend-<id>.brevlab.com with TLS termination at their edge. Browsers enforce secure WebSocket (wss://) — plain ws:// is blocked as mixed-content. Caddy on port 80 serves both the frontend AND proxies the internal @nvidia/ov-web-rtc Direct signaling endpoint used by standalone ovstream:

Browser → https://frontend-<id>.brevlab.com (Brev TLS edge)
  └── port 80 → Caddy
        ├── /sign_in* → reverse_proxy localhost:49100 (ovstream WebSocket signaling)
        ├── /*        → file_server (pre-built frontend)
        └── UDP media → <PUBLIC_IP>:47998 (direct, no proxy)

The frontend is built with VITE_SIGNALING_PORT=443 so @nvidia/ov-web-rtc Direct mode connects to wss://frontend-<id>.brevlab.com:443/sign_in (same origin as the page). Brev routes this → port 80 → Caddy → localhost:49100.

This is the exposed route for standalone ovstream Direct signaling. The deployment layer may provide auth, launch, routing, and lifecycle management, but the browser WebRTC config still uses @nvidia/ov-web-rtc Direct mode with the exposed signaling endpoint. Do not replace it with a Kit, OVC, NVCF, or GFN client connection profile.

Option B: Direct IP access (port 1024 + nginx)

For internal testing where TLS isn't needed, access the viewer directly via http://<PUBLIC_IP>:1024. nginx on port 1024 proxies both the frontend and signaling:

Browser → http://<PUBLIC_IP>:1024/
  ├── nginx (port 1024) → /            → Vite dev server or static files (port 5173/3000)
  │                     → /sign_in     → ovstream signaling server (port 49100)
  └── UDP media → <PUBLIC_IP>:47998

No TLS, no mixed-content issues (both page and WebSocket are plain HTTP). Frontend uses default VITE_SIGNALING_PORT=1024 (same port as page).

Note: Do not use Brev's Cloudflare secure link (https://) with Option B. NVST extracts the client IP from getpeername() on the TCP socket — Cloudflare in the middle causes NAT hole-punch failure.

Critical Configuration
Option A: Caddyfile (port 80 — frontend + WSS proxy)
{
    auto_https off
}

:80 {
    handle /sign_in* {
        reverse_proxy localhost:49100
    }
    handle {
        root * /opt/ov-viewer/clients/webrtc-browser/dist
        file_server
    }
}

Install Caddy:

curl -o /tmp/caddy.tar.gz -sL "https://github.com/caddyserver/caddy/releases/download/v2.8.4/caddy_2.8.4_linux_amd64.tar.gz"
tar -xzf /tmp/caddy.tar.gz -C /tmp caddy
sudo mv /tmp/caddy /usr/local/bin/caddy
Option B: nginx (port 1024 — direct access)
server {
    listen 1024;

    location / {
        proxy_pass http://localhost:3000/;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "Upgrade";
    }

    location /sign_in {
        proxy_pass http://localhost:49100/sign_in;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "Upgrade";
    }
}
Server (ov_web_viewer_server.py)

The server binds signaling on port 49100 and media on port 47998:

python3 ov_web_viewer_server.py --port 49100 --public-ip "$PUBLIC_IP" \
    --stage /opt/ov-viewer/samples_data/stage01.usd
  • --public-ip sets the ICE candidate IP in SDP. Required for NAT traversal.

  • Media defaults to UDP :47998 — must match Brev port rule.

Frontend build (bake signaling port)

The frontend reads VITE_SIGNALING_PORT at build time:

cd clients/webrtc-browser

# Option A (HTTPS via Brev domain):
VITE_SIGNALING_PORT=443 npx vite build

# Option B (direct IP on port 1024):
VITE_SIGNALING_PORT=1024 npx vite build

This makes the SDK connect to the matching port for signaling. With Option A, Caddy proxies the WebSocket transparently. With Option B, nginx proxies it on the same port.

Deployment Steps
  1. Build frontend locally:

    export VIEWER_ROOT=/path/to/generated-viewer
    cd "$VIEWER_ROOT/clients/webrtc-browser"
    npm install --ignore-scripts
    # Choose one:
    VITE_SIGNALING_PORT=443 npx vite build   # Option A (HTTPS)
    VITE_SIGNALING_PORT=1024 npx vite build  # Option B (direct)
  2. Refresh SSH and rsync payload:

    brev refresh
    INSTANCE="omniverse-realtime-viewer-launchable-XXXXXX"
    
    # Wheels (~2.5GB ovrtx + ovstream)
    rsync -az "$VIEWER_ROOT/deps/wheels/" $INSTANCE:/tmp/ov-deploy/
    
    # Server + samples + frontend
    rsync -az "$VIEWER_ROOT/server" $INSTANCE:/tmp/ov-deploy/
    rsync -az "$VIEWER_ROOT/samples_data" $INSTANCE:/tmp/ov-deploy/
    rsync -az "$VIEWER_ROOT/clients/webrtc-browser/dist" $INSTANCE:/tmp/ov-deploy/frontend-dist
  3. Remote setup (SSH into instance):

    # System deps
    sudo apt-get update -qq
    sudo apt-get install -y -qq python3-pip python3-venv python3-dev \
        libgomp1 libatomic1 libgl1 libglx0 libx11-6 libxau6 libxdmcp6 \
        libxcb1 libbsd0 libmd0 libegl1 libglib2.0-0
    
    # Deploy directory
    sudo mkdir -p /opt/ov-viewer && sudo chown $(whoami) /opt/ov-viewer
    cp -r /tmp/ov-deploy/server /opt/ov-viewer/
    cp -r /tmp/ov-deploy/samples_data /opt/ov-viewer/
    mkdir -p /opt/ov-viewer/clients/webrtc-browser
    cp -r /tmp/ov-deploy/frontend-dist /opt/ov-viewer/clients/webrtc-browser/dist
    
    # Python venv + wheels
    cd /opt/ov-viewer/server
    python3 -m venv .venv
    source .venv/bin/activate
    pip install --upgrade pip -q
    pip install /tmp/ov-deploy/*.whl -q
    pip install numpy warp-lang "usd-core==24.11" -q
    python3 -c "import ovstream, ovrtx; print('OK')"
    deactivate
    
    # Install Caddy (Option A only)
    curl -o /tmp/caddy.tar.gz -sL "https://github.com/caddyserver/caddy/releases/download/v2.8.4/caddy_2.8.4_linux_amd64.tar.gz"
    tar -xzf /tmp/caddy.tar.gz -C /tmp caddy && sudo mv /tmp/caddy /usr/local/bin/caddy
  4. Start server:

    cd /opt/ov-viewer/server
    source .venv/bin/activate
    export OVRTX_SKIP_USD_CHECK=1
    OVSTREAM_DIR=$(python3 -c "import ovstream, os; print(os.path.dirname(ovstream.__file__))")
    OVRTX_BIN=$(python3 -c "import ovrtx, os; print(os.path.join(os.path.dirname(ovrtx.__file__), 'bin'))")
    export LD_LIBRARY_PATH="${OVSTREAM_DIR}:${OVRTX_BIN}:${LD_LIBRARY_PATH:-}"
    PUBLIC_IP=$(curl -sf ifconfig.me)
    
    nohup python3 ov_web_viewer_server.py --port 49100 --public-ip "$PUBLIC_IP" \
        --stage /opt/ov-viewer/samples_data/stage01.usd > /tmp/server.log 2>&1 &
  5. Wait for shader warmup (~5-10 min cold on A10G):

    # Monitor: port 49100 appears when ready
    watch -n5 'ss -tlnp | grep 49100 && echo READY || echo WAITING'
  6. Start reverse proxy:

    # Option A: Caddy on port 80
    sudo caddy run --config /path/to/Caddyfile &
    
    # Option B: nginx on port 1024 (install + configure per above)
    sudo apt-get install -y nginx
    # Add server block to /etc/nginx/sites-enabled/default, then:
    sudo nginx -s reload
  7. Access:

    • Option A: https://frontend-<id>.brevlab.com/

    • Option B: http://<PUBLIC_IP>:1024/

Gotchas & Troubleshooting
SymptomCauseFix
Mixed Content: ws:// blockedPage loaded over HTTPS, SDK uses plain WSUse Option A (Caddy + VITE_SIGNALING_PORT=443) or Option B (direct IP, no TLS)
"connection attempts failed, retrying"Proxy not forwarding the internal Direct signaling endpointVerify Caddy/nginx config proxies /sign_in to 49100
WebSocket to wss:// fails on direct IPSDK tries WSS on HTTPS pageUse Option B with http:// access (no TLS = no mixed-content)
Black screen, input worksNVENC encoder state corruptionKill and restart server clean
NattHolePunch: Address ... is not validSignaling through Cloudflare hides client IPUse Option B (direct IP) or Option A (Caddy, doesn't affect UDP)
Port 49100 not listening after 10 minShader warmup still runningCheck nvidia-smi — GPU at 0% is normal during compilation
GPU device ID 8759 not white-listedA10G not in NVST allowlistWarning only; NVENC works fine
Shader compilation takes 5-10 minCold start on A10G (no shader cache)Wait; GPU util jumps to 50%+ then drops when done
Safari shows black videoMissing autoplay playsinline muted on <video>Add these attributes to HTML
404 on assets after rebuildBrowser cached old index.html with stale hashHard refresh (Ctrl+Shift+R)
NVST_R_BUSYSecond WebRTC client connectedOnly 1 peer at a time; restart server
Why Two Options

Option A (Caddy + HTTPS) is required for demos and external access. Brev's HTTPS proxy terminates TLS at the edge and forwards to port 80. Browsers enforce wss:// from HTTPS pages — Caddy solves this by serving both frontend and signaling on the same origin.

Option B (nginx + direct IP) is simpler for internal dev/testing. No TLS means no mixed-content issues. Access via http://<PUBLIC_IP>:1024 bypasses Brev's Cloudflare tunnel entirely. However, NVST cannot determine the client IP through Cloudflare, so never use the Brev https:// URL with Option B.

Port 1024 and 80 are opened via Brev's "TCP/UDP Port Rules" (actual AWS security group entries), NOT via "Secure Links" (which only proxy TCP through Cloudflare).


Docker Container Deployment

For containerized deployments without a full orchestrator, build a standalone Docker image that bundles the ovrtx server, ovstream, sample data, and a pre-built frontend.

Base Image And System Dependencies

Use nvidia/cuda:12.6.3-base-ubuntu22.04 as the base. This provides CUDA runtime libraries without the full toolkit overhead.

Required apt packages for ovrtx rendering and ovstream:

# syntax=docker/dockerfile:1
FROM nvidia/cuda:12.6.3-base-ubuntu22.04

ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3 python3-pip python3-dev \
    libgomp1 libatomic1 \
    libgl1 libglx0 libegl1 libopengl0 \
    libx11-6 libxau6 libxdmcp6 libxcb1 libbsd0 libmd0 \
    libglib2.0-0 \
    curl ca-certificates \
  && rm -rf /var/lib/apt/lists/*

The # syntax=docker/dockerfile:1 directive at the top of the Dockerfile is required for BuildKit features like bind mounts.

Installing Large Python Wheels Efficiently

ovrtx and ovstream wheels can be hundreds of megabytes. Using a regular COPY + pip install doubles image size because layers retain both the wheel and the installed package. Use BuildKit bind mounts instead:

# Place wheels in deps/wheels/ relative to build context
RUN --mount=type=bind,source=deps/wheels,target=/tmp/wheels \
    pip install --no-cache-dir /tmp/wheels/*.whl

This mounts the wheels at build time without copying them into a layer. The final image only contains the installed packages.

.dockerignore

A .dockerignore file is critical. Without it, COPY . /app sends node_modules/ to the Docker daemon and can inject platform-incompatible native binaries into the image:

**/node_modules
**/.git
**/__pycache__
*.egg-info
Runtime Requirements

The container must be started with GPU access and X11 display forwarding for ovrtx headless rendering:

docker run --gpus all \
  -e DISPLAY=:99 \
  -e PUBLIC_IP=<reachable-ip> \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  -p 49100:49100 \
  -p 47998:47998/udp \
  -p 8081:8081 \
  ovrtx-viewer:latest
Env VarPurpose
DISPLAYX11 display for GPU rendering (use Xvfb :99 for headless)
PUBLIC_IPWebRTC ICE candidate IP advertised to clients
VolumePurpose
/tmp/.X11-unixX11 socket mount (from host Xvfb or display server)
PortProtocolPurpose
49100TCPWebRTC signaling (WebSocket)
47998UDPWebRTC media
8081TCPHealth endpoint (/healthz)
Shader Compilation Cold Start

After a fresh container start, the first scene load triggers GPU shader compilation. Expected times:

GPUApproximate shader compilation time
L40 / L40S~90 seconds
A10G~240 seconds
H100 / A100 (non-graphics)Not supported for rendering

Do not connect clients or mark the service as ready until /healthz returns 200. The health endpoint gates on the first successfully rendered and converted frame, which occurs after shader compilation completes; it should not require an attached browser client.

Entrypoint Pattern
COPY entrypoint.sh /entrypoint.sh
RUN chmod +x /entrypoint.sh
ENTRYPOINT ["/entrypoint.sh"]
#!/bin/bash
set -e

# Start Xvfb if no display is available
if ! xdpyinfo -display "${DISPLAY:-:99}" >/dev/null 2>&1; then
  Xvfb ${DISPLAY:-:99} -screen 0 1920x1080x24 &
  sleep 1
fi

export OVRTX_SKIP_USD_CHECK=1
exec python3 /app/server/ov_web_viewer_server.py \
  --port "${PORT:-49100}" \
  --health-port "${HEALTH_PORT:-8081}" \
  --public-ip "${PUBLIC_IP:-$(curl -s ifconfig.me)}" \
  --stage "${STAGE_PATH:-/app/samples_data/stage01.usd}"
Sample Dockerfile (Complete)
# syntax=docker/dockerfile:1
FROM nvidia/cuda:12.6.3-base-ubuntu22.04

ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
    python3 python3-pip python3-dev \
    libgomp1 libatomic1 \
    libgl1 libglx0 libegl1 libopengl0 \
    libx11-6 libxau6 libxdmcp6 libxcb1 libbsd0 libmd0 \
    libglib2.0-0 \
    xvfb \
    curl ca-certificates \
  && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Install Python wheels without layer bloat
RUN --mount=type=bind,source=deps/wheels,target=/tmp/wheels \
    pip install --no-cache-dir /tmp/wheels/*.whl

# Copy server code and sample data
COPY server/ /app/server/
COPY samples_data/ /app/samples_data/

# Copy pre-built frontend (optional, for self-contained image)
COPY frontend/dist/ /app/frontend/dist/

COPY entrypoint.sh /entrypoint.sh
RUN chmod +x /entrypoint.sh

EXPOSE 49100 47998/udp 8081
ENTRYPOINT ["/entrypoint.sh"]
Build And Run
# Build (requires BuildKit)
DOCKER_BUILDKIT=1 docker build -t ovrtx-viewer:latest .

# Run
docker run --gpus all \
  -e PUBLIC_IP=$(curl -s ifconfig.me) \
  -p 49100:49100 \
  -p 47998:47998/udp \
  -p 8081:8081 \
  ovrtx-viewer:latest

If the host already has an X11 display or Xvfb running, mount the socket and set DISPLAY accordingly. Otherwise the entrypoint starts its own Xvfb instance.


OKAS 1 / Generic Session Orchestration

For production-style deployments, keep the Omniverse Realtime Viewer contract portable. Use OKAS 1 or a generic container/session orchestrator that can start one GPU container per Omniverse Realtime Viewer session, expose WebRTC signaling and media ports, route the browser to the frontend, and terminate the container when the session ends.

OKAS is orchestration/session management, not a different WebRTC client profile. It may allocate GPU resources, start the container, inject environment/config, publish routes, and manage session lifecycle. After OKAS resolves a session endpoint, the frontend uses standalone ovstream Direct config: server and signalingPort point at the exposed signaling endpoint, while media remains negotiated by WebRTC.

Registration Contract

Register the Omniverse Realtime Viewer with portable metadata:

{
  "id": "ovrtx-viewer",
  "name": "Omniverse Realtime Viewer",
  "image": "ovrtx-viewer:0.2.0",
  "description": "Omniverse Realtime Viewer using ovrtx rendering with ovstream WebRTC delivery",
  "gpuRequired": true,
  "ports": {
    "signaling": 49100,
    "mediaUdp": 47998,
    "health": 8081
  }
}

Keep deployment recipes portable. Do not bind generated apps to app registries, session-manager paths, sidecars, or caching services unless the selected deployment target explicitly provides them.

Launch Contract

A session launcher should run the same server command the Brev path uses:

export OVRTX_SKIP_USD_CHECK=1
python3 server/ov_web_viewer_server.py \
  --port "${PORT:-49100}" \
  --health-port "${HEALTH_PORT:-8081}" \
  --public-ip "${PUBLIC_IP}" \
  --stage "${STAGE_PATH:-samples/samples_data/stage01.usd}"
Health And Ports
PortProtocolPurpose
49100WebSocket/TCPWebRTC signaling
47998 or 47999UDPWebRTC media (deployment-dependent)
8081HTTPhealth endpoint (/healthz)
Docker
cd deploy
docker build -t ovrtx-viewer:0.2.0 .

deploy/entrypoint.sh launches the server with $PORT and $STAGE_PATH.

VarDefaultPurpose
PORT49100signaling port
PUBLIC_IPauto-detect via ifconfig.meWebRTC candidate IP
STAGE_PATHsamples/samples_data/stage01.usdinitial stage
HEALTH_PORT8081health endpoint
Session Lifecycle
POST /sessions {application:"ovrtx-viewer"}
  → spawn one GPU process/container
  → poll GET :8081/healthz
  → mark ready
  → browser connects to frontend and internal Direct signaling
  → WebRTC media flows
  → DELETE /sessions/{id}
  → SIGTERM
  • streaming-server for ovstream ServerConfig details and frame handling.

  • streaming-client for frontend WebRTC SDK usage.

  • streaming-lifecycle for connection/reconnection behavior.

  • cloud-assets when deployed sessions load stages from S3/MinIO.

  • OKAS 1 or your orchestrator documentation for portal/session APIs.

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

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