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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/camera-auto-select/README.md

Camera Auto-Select

Purpose

When a user says "here's my stage" the viewer should open to a meaningful camera on the very first frame — not to an arbitrary default position. This skill inspects the stage for authored cameras and picks the best one, or computes a fit-all fallback.

Triggers

Use when:

  • Loading a user-provided USD stage for the first time.

  • Building a viewer application that must "just work" with arbitrary stages.

  • The default camera position is wrong or unhelpful for a given scene.

  • A build pipeline needs to determine the hero camera before deployment.

Priority Heuristic

Evaluate cameras in this order. Stop at the first match.

PriorityConditionRationale
1Stage metadata has defaultCameraAuthor explicitly chose one.
2Camera prim named *Main*, *Hero*, *Default*, *Persp* (case-insensitive)Common naming convention.
3Exactly one camera in the stageNo ambiguity — use it.
4Camera with widest FOV (lowest focalLength) that is NOT top-down (X-rotation ≈ 90°)Likely the overview/hero shot.
5First camera in scene traversal orderDeterministic fallback.
6Compute bbox-fit cameraStage has no authored cameras at all.

Implementation

Stage Introspection (Python / pxr)
import math
from pxr import Usd, UsdGeom, Gf

HERO_NAME_PATTERNS = ["main", "hero", "default", "persp", "perspective"]


def find_best_camera(stage: Usd.Stage) -> str | None:
    """Return the prim path of the best camera, or None if bbox-fit needed."""

    # Priority 1: explicit defaultCamera in layer metadata
    root_layer = stage.GetRootLayer()
    default_cam = root_layer.customLayerData.get("defaultCamera")
    if default_cam:
        prim = stage.GetPrimAtPath(default_cam)
        if prim and prim.IsA(UsdGeom.Camera):
            return str(prim.GetPath())

    # Collect all cameras
    cameras = []
    for prim in stage.Traverse():
        if prim.IsA(UsdGeom.Camera):
            cameras.append(prim)

    if not cameras:
        return None  # caller should use bbox-fit

    # Priority 2: name matching
    for cam in cameras:
        name_lower = cam.GetName().lower()
        for pattern in HERO_NAME_PATTERNS:
            if pattern in name_lower:
                return str(cam.GetPath())

    # Priority 3: single camera
    if len(cameras) == 1:
        return str(cameras[0].GetPath())

    # Priority 4: widest FOV, skip top-down
    best_cam = None
    lowest_focal = float("inf")
    for cam in cameras:
        focal = cam.GetAttribute("focalLength").Get() or 50.0
        if _is_top_down(stage, cam):
            continue
        if focal < lowest_focal:
            lowest_focal = focal
            best_cam = cam

    if best_cam:
        return str(best_cam.GetPath())

    # Priority 5: first in traversal
    return str(cameras[0].GetPath())


def _is_top_down(stage: Usd.Stage, cam_prim) -> bool:
    """Heuristic: camera looking straight down (X rotation ~90°)."""
    xformable = UsdGeom.Xformable(cam_prim)
    xform = xformable.ComputeLocalToWorldTransform(Usd.TimeCode.Default())
    # Extract the forward vector (negative Z in camera space)
    forward = xform.TransformDir(Gf.Vec3d(0, 0, -1))
    up_axis = UsdGeom.GetStageUpAxis(stage)
    if up_axis == UsdGeom.Tokens.z:
        world_down = Gf.Vec3d(0, 0, -1)
    else:
        world_down = Gf.Vec3d(0, -1, 0)
    # If forward is within ~10° of straight down, it's top-down
    dot = Gf.Dot(forward.GetNormalized(), world_down)
    return dot > math.cos(math.radians(10))
Bbox-Fit Fallback

When no authored camera exists, compute a fit-all orbit:

def compute_bbox_fit_camera(stage: Usd.Stage, fov_deg: float = 60.0):
    """Return (target, distance, elevation, azimuth) for an OrbitCamera."""
    bbox_cache = UsdGeom.BBoxCache(Usd.TimeCode.Default(), ["default", "render"])
    world_bbox = bbox_cache.ComputeWorldBound(stage.GetPseudoRoot())
    bbox_range = world_bbox.ComputeAlignedBox()

    center = (bbox_range.GetMin() + bbox_range.GetMax()) / 2.0
    size = bbox_range.GetMax() - bbox_range.GetMin()
    max_dim = max(size[0], size[1], size[2])

    # Distance to fit the bounding sphere in view
    half_fov = math.radians(fov_deg / 2.0)
    distance = (max_dim / 2.0) / math.tan(half_fov) * 1.2  # 20% padding

    # Default orbit angles: slight elevation, 3/4 azimuth
    elevation = math.radians(25.0)
    azimuth = math.radians(-45.0)

    return {
        "target": [center[0], center[1], center[2]],
        "distance": distance,
        "elevation": elevation,
        "azimuth": azimuth,
    }
Emitting camera_config.json

During app build or stage load, write a config the frontend can consume:

import json

def emit_camera_config(stage: Usd.Stage, output_path: str = "camera_config.json"):
    """Write camera config for the frontend."""
    cameras = []
    for prim in stage.Traverse():
        if prim.IsA(UsdGeom.Camera):
            cam = UsdGeom.Camera(prim)
            cameras.append({
                "path": str(prim.GetPath()),
                "name": prim.GetName(),
                "focalLength": cam.GetFocalLengthAttr().Get() or 50.0,
            })

    best = find_best_camera(stage)
    config = {
        "cameras": cameras,
        "defaultCamera": best,
        "hasBboxFallback": best is None,
    }

    if best is None:
        config["bboxFit"] = compute_bbox_fit_camera(stage)

    with open(output_path, "w") as f:
        json.dump(config, f, indent=2)

    return config

Integration Points

Server (stage-loading)

Call find_best_camera() immediately after Usd.Stage.Open(). If a camera is found, set it as the active render camera for the first frame:

stage = Usd.Stage.Open(stage_path)
best_camera = find_best_camera(stage)
if best_camera:
    # Point the render product at the authored camera
    renderer.set_active_camera(best_camera)
else:
    # Use bbox-fit orbit as the session camera
    fit = compute_bbox_fit_camera(stage)
    orbit_camera.target = fit["target"]
    orbit_camera.distance = fit["distance"]
    orbit_camera.elevation = fit["elevation"]
    orbit_camera.azimuth = fit["azimuth"]
Frontend (streaming-client)

On stage load response, read the camera list and set the initial view. If the app includes a camera picker (see camera-picker skill), populate it from the same data.

Build Pipeline

For pre-built/deployed apps where the stage is known at build time, run emit_camera_config() during the build step and bundle the JSON with the app assets. The frontend reads it at startup without needing a round-trip to the server.

Gotchas

  • defaultCamera in layer metadata is a custom field — not all stages set it. The heuristic handles this gracefully.

  • Some stages define cameras inside referenced assets (props with internal cameras). Filter to cameras under /World/Cameras or at the root level to avoid picking internal asset cameras.

  • Top-down cameras are useful for plan views but make poor defaults for first impressions. The heuristic deprioritizes them.

  • For multi-GPU or multi-viewport setups, each viewport can have its own camera. This skill picks the initial default only.

See Also

  • camera-controls — orbit, pan, zoom, and fly input handling.

  • camera-picker — UI dropdown for switching between stage cameras.

  • stage-loading — stage open and session setup.

  • stage-hierarchy — traversal and bbox computation.

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)
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