[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-omniverse-realtime-viewer":3,"mdc--edurwv-key":34,"related-repo-nvidia-omniverse-realtime-viewer":805,"related-org-nvidia-omniverse-realtime-viewer":911},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"omniverse-realtime-viewer","view Omniverse USD applications","Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,17,20],{"name":13,"slug":14,"type":15},"3D","3d","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Visualization","visualization",{"name":21,"slug":22,"type":15},"Design","design",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:30:10.813242","Apache-2.0",281,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fomniverse-realtime-viewer","---\nname: omniverse-realtime-viewer\ndescription: \"Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.\"\nversion: \"0.1.0\"\nlicense: Apache-2.0\ntools:\n  - Read\n  - Shell\n  - Write\ncompatibility: >\n  Orchestrator skill. Downstream focused references may require NVIDIA GPUs, ovrtx,\n  ovstream, ovui, OpenUSD, Python, Node\u002FReact, Tauri, Electron, C++, or cloud\n  GPU deployment access depending on the selected viewer path.\nmetadata:\n  author: NVIDIA Omniverse\n  tags:\n    - omniverse\n    - usd\n    - viewer\n    - workflow\n  domain: ai-ml\n  languages:\n    - python\n    - typescript\n    - cpp\n---\n\n\u003C!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->\n\u003C!-- SPDX-License-Identifier: Apache-2.0 -->\n\n# Omniverse Realtime Viewer\n\nThis is the top-level entry point for the Omniverse Realtime Viewer skill package.\nIt is self-contained: all required routing, conventions, and validation\nguidance live in the selected references.\n\nUse the focused reference documents as implementation recipes. This file chooses the\nright recipes and preserves the architectural rules that must hold across all\ngenerated viewer apps.\n\n## Instructions\n\nStart by classifying the requested viewer, then read only the references needed\nfor that delivery path and feature set. Implement the render path first, layer\ninteraction and UI behavior on top of it, and finish by capturing validation\nevidence from `references\u002Fvalidation.md`.\n\n## Read Order\n\n1. Read `references\u002Frouting.md` to choose the delivery path and focused references.\n2. Read `references\u002Fconventions.md` before implementing camera, input,\n   selection, viewport, streaming protocol, scene loading, or environment\n   behavior.\n3. For broad viewer requests, read `references\u002Fusd-viewer-app\u002FREADME.md`.\n4. If the delivery path is unclear, read `references\u002Fstreaming-vs-local\u002FREADME.md`.\n5. If the prompt includes layout, panels, controls, inspectors, status, or UX,\n   read `references\u002Fviewer-ux-workflow\u002FREADME.md` and then the focused viewer UI references.\n   This applies to React\u002FWebRTC, Tauri, Electron, `ovui`, `ovwidgets`, and Dear\n   ImGui apps; \"frontend\" means user-facing UI, not only browser UI.\n6. For viewport interaction, read `references\u002Fviewer-input-routing\u002FREADME.md` before\n   `references\u002Fcamera-controls\u002FREADME.md`, `references\u002Fnative-picking-selection\u002FREADME.md`, or `references\u002Fobject-selection\u002FREADME.md`.\n7. Read only the focused capability references needed for the requested app.\n8. Use `references\u002Fvalidation.md` to capture review evidence before handoff.\n\n## Non-Negotiables\n\n- Use `ovrtx` for all USD and 3D rendering.\n- Browser apps display an `ovstream` WebRTC video stream plus UI. The browser\n  does not render USD geometry.\n- Do not substitute WebGL, Three.js, Babylon.js, PlayCanvas, A-Frame,\n  model-viewer, react-three-fiber, glTF browser viewers, or other client-side\n  3D renderers.\n- If local validation cannot run because the GPU\u002Fruntime environment is absent,\n  scaffold the `ovrtx` path and document the runtime requirement. Do not add a\n  browser-renderer fallback.\n- Keep user USD files unmodified. Viewer cameras, render products, render vars,\n  settings, selection metadata, and runtime state belong in session\u002Fcomposite\n  layers or app state.\n- Keep one owner for `renderer.step()`, stage mutation, native picking,\n  selection writes, and live attribute writes.\n- Keep dependency acquisition in `references\u002Fdependencies\u002FREADME.md` and deployment choices in\n  `references\u002Fcloud-deployment\u002FREADME.md`; do not duplicate package locations or deployment setup.\n\n## Focused Reference Families\n\n- Entry points and recipes: `references\u002Fusd-viewer-app\u002FREADME.md`, `references\u002Fstreaming-viewer-recipe\u002FREADME.md`,\n  `references\u002Fovui-local-viewer-recipe\u002FREADME.md`, `references\u002Fstreaming-vs-local\u002FREADME.md`, `references\u002Felectron-shm-viewer\u002FREADME.md`,\n  `references\u002Fovwidgets-editor-shell\u002FREADME.md`.\n- Rendering and stage: `references\u002Fovrtx-rendering\u002FREADME.md`, `references\u002Fstage-loading\u002FREADME.md`, `references\u002Fstage-management\u002FREADME.md`,\n  `references\u002Frender-settings\u002FREADME.md`, `references\u002Faov-switching\u002FREADME.md`, `references\u002Fstage-hierarchy\u002FREADME.md`, `references\u002Fstage-queries\u002FREADME.md`,\n  `references\u002Fstage-attribute-reads\u002FREADME.md`, `references\u002Fprim-transform-safety\u002FREADME.md`, `references\u002Fusd-sample-data\u002FREADME.md`.\n- Delivery and runtime: `references\u002Fstreaming-server\u002FREADME.md`, `references\u002Fstreaming-client\u002FREADME.md`,\n  `references\u002Fstreaming-messages\u002FREADME.md`, `references\u002Fstreaming-lifecycle\u002FREADME.md`, `references\u002Flocal-viewer\u002FREADME.md`,\n  `references\u002Ftauri-local-viewer\u002FREADME.md`, `references\u002Fcpp-native-viewer\u002FREADME.md`, `references\u002Fheadless-shm-cli\u002FREADME.md`,\n  `references\u002Fviewer-backend-interface\u002FREADME.md`, `references\u002Fwebgl-shm-transport\u002FREADME.md`.\n- Viewer UI\u002FUX: `references\u002Fviewer-ux-workflow\u002FREADME.md`, `references\u002Fviewer-layout-patterns\u002FREADME.md`,\n  `references\u002Fviewer-control-patterns\u002FREADME.md`, `references\u002Fviewer-data-view-patterns\u002FREADME.md`,\n  `references\u002Fviewer-feedback-status\u002FREADME.md`.\n- Interaction: `references\u002Fviewer-input-routing\u002FREADME.md`, `references\u002Fcamera-controls\u002FREADME.md`,\n  `references\u002Fobject-selection\u002FREADME.md`, `references\u002Fnative-picking-selection\u002FREADME.md`, `references\u002Fselection-feedback\u002FREADME.md`,\n  `references\u002Fselection-animation\u002FREADME.md`, `references\u002Ftransform-manipulator\u002FREADME.md`, `references\u002Fgl-viewport-overlay\u002FREADME.md`,\n  `references\u002Fovui-library\u002FREADME.md`, `references\u002Fprim-pick-effects\u002FREADME.md`, `references\u002Fprim-info-display\u002FREADME.md`,\n  `references\u002Fviewport-overlays\u002FREADME.md`.\n- Infrastructure: `references\u002Fdependencies\u002FREADME.md`, `references\u002Fwindows-native-setup\u002FREADME.md`, `references\u002Fcloud-assets\u002FREADME.md`,\n  `references\u002Fcloud-deployment\u002FREADME.md`, `references\u002Ftroubleshooting\u002FREADME.md`.\n\n## Build Workflow\n\n1. Classify the prompt by delivery path, target user, required capabilities,\n   runtime environment, validation needs, and explicit constraints.\n2. Select a small reference set. Start with the recipe or routing reference, then add\n   focused capabilities such as camera, picking, hierarchy, properties, render\n   settings, transform tools, cloud assets, or deployment.\n3. Read selected references before writing app code. Follow their build order,\n   import order, data-channel contracts, and renderer ownership rules.\n4. Implement the core render path first, then input routing and camera, then\n   selection and data panels, then scene\u002Fsettings features, then packaging or\n   deployment.\n5. Treat the selected references as the behavior contract for API shape,\n   compatibility, and generated project structure.\n6. Capture validation evidence before calling the viewer ready.\n\n## Examples\n\n- For a browser viewer request, use the streaming recipe references plus camera,\n  picking, hierarchy, properties, render settings, and stream-status references.\n- For a local workstation viewer request, use the local or native delivery\n  references plus renderer setup, stage loading, viewport input, and validation.\n\n## Completion Checklist\n\n- Selected references match the user's intent and delivery path.\n- No code path uses a browser-side 3D renderer for USD.\n- The generated app has one clear owner for render stepping and stage mutation.\n- User USD files remain untouched by viewer-owned session data.\n- Camera, input, selection, scene loading, and stream behavior follow\n  `references\u002Fconventions.md`.\n- Setup\u002Fbuild\u002Frun results and visual interaction evidence are captured with\n  `references\u002Fvalidation.md`.\n",{"data":35,"body":54},{"name":4,"description":6,"version":36,"license":26,"tools":37,"compatibility":41,"metadata":42},"0.1.0",[38,39,40],"Read","Shell","Write","Orchestrator skill. 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The browser\ndoes not render USD geometry.",{"type":58,"tag":108,"props":274,"children":275},{},[276],{"type":63,"value":277},"Do not substitute WebGL, Three.js, Babylon.js, PlayCanvas, A-Frame,\nmodel-viewer, react-three-fiber, glTF browser viewers, or other client-side\n3D renderers.",{"type":58,"tag":108,"props":279,"children":280},{},[281,283,288],{"type":63,"value":282},"If local validation cannot run because the GPU\u002Fruntime environment is absent,\nscaffold the ",{"type":58,"tag":89,"props":284,"children":286},{"className":285},[],[287],{"type":63,"value":257},{"type":63,"value":289}," path and document the runtime requirement. Do not add a\nbrowser-renderer fallback.",{"type":58,"tag":108,"props":291,"children":292},{},[293],{"type":63,"value":294},"Keep user USD files unmodified. 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",{"type":58,"tag":89,"props":664,"children":666},{"className":665},[],[667],{"type":63,"value":318},{"type":63,"value":177},{"type":58,"tag":89,"props":670,"children":672},{"className":671},[],[673],{"type":63,"value":674},"references\u002Fwindows-native-setup\u002FREADME.md",{"type":63,"value":177},{"type":58,"tag":89,"props":677,"children":679},{"className":678},[],[680],{"type":63,"value":681},"references\u002Fcloud-assets\u002FREADME.md",{"type":63,"value":356},{"type":58,"tag":89,"props":684,"children":686},{"className":685},[],[687],{"type":63,"value":326},{"type":63,"value":177},{"type":58,"tag":89,"props":690,"children":692},{"className":691},[],[693],{"type":63,"value":694},"references\u002Ftroubleshooting\u002FREADME.md",{"type":63,"value":96},{"type":58,"tag":77,"props":697,"children":699},{"id":698},"build-workflow",[700],{"type":63,"value":701},"Build 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For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[870,873,876,877],{"name":871,"slug":872,"type":15},"Automation","automation",{"name":874,"slug":875,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":878,"slug":879,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":882,"name":882,"fn":883,"description":884,"org":885,"tags":886,"stars":23,"repoUrl":24,"updatedAt":895},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[887,888,891,892],{"name":831,"slug":832,"type":15},{"name":889,"slug":890,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":893,"slug":894,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":897,"name":897,"fn":898,"description":899,"org":900,"tags":901,"stars":23,"repoUrl":24,"updatedAt":909},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[902,903,906],{"name":9,"slug":8,"type":15},{"name":904,"slug":905,"type":15},"Quantum Computing","quantum-computing",{"name":907,"slug":908,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305,{"items":912,"total":1062},[913,931,946,957,969,983,996,1010,1021,1030,1044,1053],{"slug":914,"name":914,"fn":915,"description":916,"org":917,"tags":918,"stars":928,"repoUrl":929,"updatedAt":930},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[919,922,925],{"name":920,"slug":921,"type":15},"Documentation","documentation",{"name":923,"slug":924,"type":15},"MCP","mcp",{"name":926,"slug":927,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":932,"name":932,"fn":933,"description":934,"org":935,"tags":936,"stars":943,"repoUrl":944,"updatedAt":945},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[937,940,941],{"name":938,"slug":939,"type":15},"Containers","containers",{"name":831,"slug":832,"type":15},{"name":942,"slug":51,"type":15},"Python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":947,"name":947,"fn":948,"description":949,"org":950,"tags":951,"stars":943,"repoUrl":944,"updatedAt":956},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[952,955],{"name":953,"slug":954,"type":15},"CI\u002FCD","ci-cd",{"name":831,"slug":832,"type":15},"2026-07-14T05:25:59.97109",{"slug":958,"name":958,"fn":959,"description":960,"org":961,"tags":962,"stars":943,"repoUrl":944,"updatedAt":968},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[963,964,965],{"name":953,"slug":954,"type":15},{"name":831,"slug":832,"type":15},{"name":966,"slug":967,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":970,"name":970,"fn":971,"description":972,"org":973,"tags":974,"stars":943,"repoUrl":944,"updatedAt":982},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[975,978,979],{"name":976,"slug":977,"type":15},"Debugging","debugging",{"name":966,"slug":967,"type":15},{"name":980,"slug":981,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":984,"name":984,"fn":985,"description":986,"org":987,"tags":988,"stars":943,"repoUrl":944,"updatedAt":995},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[989,992],{"name":990,"slug":991,"type":15},"Best Practices","best-practices",{"name":993,"slug":994,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":997,"name":997,"fn":998,"description":999,"org":1000,"tags":1001,"stars":943,"repoUrl":944,"updatedAt":1009},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1002,1005,1008],{"name":1003,"slug":1004,"type":15},"Machine Learning","machine-learning",{"name":1006,"slug":1007,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1011,"name":1011,"fn":1012,"description":1013,"org":1014,"tags":1015,"stars":943,"repoUrl":944,"updatedAt":1020},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1016,1019],{"name":1017,"slug":1018,"type":15},"QA","qa",{"name":861,"slug":862,"type":15},"2026-07-14T05:25:53.673039",{"slug":1022,"name":1022,"fn":1023,"description":1024,"org":1025,"tags":1026,"stars":943,"repoUrl":944,"updatedAt":1029},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. 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Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1050,1051],{"name":1017,"slug":1018,"type":15},{"name":861,"slug":862,"type":15},"2026-07-14T05:25:54.928983",{"slug":1054,"name":1054,"fn":1055,"description":1056,"org":1057,"tags":1058,"stars":943,"repoUrl":944,"updatedAt":1061},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1059,1060],{"name":871,"slug":872,"type":15},{"name":953,"slug":954,"type":15},"2026-07-30T05:29:03.275638",496]