[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-omniverse-cad-to-simready":3,"mdc--37h8g8-key":34,"related-repo-nvidia-omniverse-cad-to-simready":1439,"related-org-nvidia-omniverse-cad-to-simready":1543},{"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-cad-to-simready","convert CAD assets to SimReady USD","Coordinate the end-to-end CAD\u002Fsource-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material\u002Fphysics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.",{"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},"Simulation","simulation",{"name":21,"slug":22,"type":15},"Design","design",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:28:51.808286","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-cad-to-simready","---\nname: omniverse-cad-to-simready\ndescription: \"Coordinate the end-to-end CAD\u002Fsource-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material\u002Fphysics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.\"\nversion: \"0.1.0\"\nlicense: Apache-2.0\ntools:\n  - Read\n  - Shell\ncompatibility: >\n  Orchestrator skill. Managed Content Agents deployment requires NVIDIA_API_KEY\n  (build.nvidia.com), Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and\n  an upstream checkout of nvidia-omniverse\u002Fcontent-agents on branch\n  main. Reused\u002Fprovided endpoints may instead use explicit endpoint and\n  usage-token environment variables. Linux\u002FmacOS only.\nmetadata:\n  author: Omniverse\n  tags:\n    - physical-ai\n    - simready\n    - workflow\n    - cad\n    - conversion\n  domain: ai-ml\n  languages:\n    - python\n---\n\n# CAD to SimReady\n\n## When to Use\n\nUse this workflow skill when the user wants an end-to-end pipeline from a\nsource asset to a SimReady asset or package. This skill coordinates existing\nconversion, authoring, validation, conformance, rendering, and packaging\nreferences directly. Do not replace the workflow with a single monolithic\nrunner command.\n\nThis skill is documentation-driven and does not ship `scripts\u002Frun.py`. It\nshould not depend on a repository checkout. When a stage needs deterministic\nexecution, run the portable script from that stage reference's installed\ndirectory. `Shell` is declared because this workflow invokes installed stage\nreference scripts directly; it still must not grow a monolithic runner.\n\n## Prerequisites\n\n- Prefer running the `preflight` reference first for deterministic setup. It\n  installs or verifies local upstream checkouts, writes a\n  `cad-to-simready-preflight.json` manifest, and exports\n  `PHYSICAL_AI_PREFLIGHT_MANIFEST` plus `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` for\n  downstream references.\n- Python 3.12 and `uv` (per repo `README.md`).\n- NVIDIA_API_KEY from `https:\u002F\u002Fbuild.nvidia.com` when local Content Agents\n  deployment will run. Already-running endpoints may instead use explicit\n  endpoint variables plus usage tokens such as `NGC_API_KEY`, `NVCF_API_KEY`,\n  or `CONTENT_AGENTS_*_TOKEN`.\n- Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and\n  OVRTX stages.\n- Local upstream checkouts under\n  `${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME\u002F.physical-ai-skill-hub\u002Fupstreams}`\n  when a downstream stage needs upstream scripts or specs.\n\n## Minimum Viable Scope\n\nConversion-only is a valid workflow request. When the user asks only to convert\nor smoke-test source asset conversion, set `property_assignment_intent=skip`,\ndo not deploy Content Agents, run `convert-to-usd`, then run\n`validate-usd-minimum` on the generated USD if conversion succeeds.\n\nDo not imply that `uv sync` installs every source converter runtime. URDF,\nMuJoCo\u002FMJCF, and the repo Python dependencies are handled by the project\nenvironment, but NVIDIA-backed source conversion requires an installed and\nvalidated `NVIDIA-Omniverse\u002Fusd-convert-cad` checkout. If that runtime is\nmissing or does not support the source, preserve the blocked conversion report\nand its `install_hint` instead of attempting an unrequested local build or\nsubstituting another converter.\n\n## First Action\n\nFor any broad CAD\u002Fsource-asset to SimReady request, assume\n`property_assignment_intent=run` unless the user explicitly asks for\nconversion-only, validation-only, or no material\u002Fphysics assignment.\n\nBefore invoking converter, validation, Content Agents, OVRTX, packaging, or FET\nhelper scripts, run the `preflight` reference or verify an existing\n`PHYSICAL_AI_PREFLIGHT_MANIFEST`. Treat preflight as the mandatory dependency\nbootstrap step, not as workflow routing. If the user explicitly asks not to\ndeploy services or asks for conversion-only\u002Fvalidation-only, use\n`--skip-content-agents`.\nWhen `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` is set and a required component is not\nready in the manifest, downstream references must block with the preflight\nguardrail instead of rediscovering upstreams or services directly.\n\nWhen `property_assignment_intent=run`, the first operational action after\nconfirming the source path and resolving intent is to verify or deploy Content\nAgents services. Do this before asset-context inspection, converter dependency\nchecks, conversion, validation, conformance, rendering, packaging, or upstream\nsource builds.\n\nUse healthy existing endpoints when available. If OVRTX, Material, or Physics\nendpoints are missing or unhealthy, run `deploy-content-agents`\nfirst and do not continue until the shared standalone OVRTX renderer plus\nindependent Material and Physics service containers are healthy and exported\nthrough `CONTENT_AGENTS_*_BASE_URL`. Deploy the Texture Agent too when texture\ngeneration is requested.\n\nIf required deployment authentication is missing, ask the user for\n`NVIDIA_API_KEY` and wait. If a provided endpoint requires usage auth, ask for\nthe appropriate usage token instead. If deployment cannot produce healthy\nservices, report Content Agents readiness as blocked instead of proceeding to\nconversion.\n\n## Instructions\n\n1. Confirm the source asset path exists, resolve `output_root`, and classify\n   the request as end-to-end, conversion-only, validation-only, or packaging.\n2. Resolve `property_assignment_intent` before running any asset inspection,\n   converter probe, conversion, validation, conformance, rendering, or\n   packaging step.\n3. Run `preflight` for the selected workflow targets, unless a ready\n   `PHYSICAL_AI_PREFLIGHT_MANIFEST` is already configured. Source the generated\n   env file before running downstream scripts. Treat preflight as dependency\n   setup only: it may use a provided `--source-asset`, `--source-format`, or\n   `--conversion-tools` value to scope dependency checks, but `convert-to-usd`\n   and the upstream converter references still decide actual conversion support.\n4. Verify or deploy Content Agents services first when\n   `property_assignment_intent=run`; block on missing authentication or\n   unhealthy services instead of continuing.\n5. Read `references\u002Fworkflow.md` and `references\u002Fcommands.md`, then run only\n   the stage references needed for the current request.\n6. Run `identify-asset-context` on the original source asset when web search is\n   available or property assignment will run.\n7. Route the source through `convert-to-usd`, or skip conversion for existing\n   USD input and treat the source path as the current USD path.\n8. Run `validate-usd-minimum` before expensive downstream work. Treat this as a\n   viability gate only: record unit\u002Fprofile issues such as `metersPerUnit !=\n   1.0`, but do not run `simready-conform-profile`, FET001, or any other FET\n   repair before Content Agents assignment when property assignment will run.\n9. Run Content Agents material, physics, and optional texture assignment on the\n   converted\u002Fminimum-valid USD when requested or required.\n10. Run `simready-conform-profile` on the latest simulation USD path after\n   property assignment and preserve every selected FET repair report.\n11. Run validation gates in order: `omni-asset-validate`,\n   `omni-asset-validate-geometry`, `omni-asset-validate-physics`, and\n   `simready-validate`.\n12. Rerun `simready-conform-profile` when `simready-validate` reports a\n    repairable requirement, then rerun profile validation on the newest authored\n    USD.\n13. Run `ovrtx-render-service` when preview, thumbnail, or inspection images\n    are requested. When package outputs are requested, run\n    `assemble-package-source` next to create the clean `deliverable\u002F` package\n    source from the final USD and thumbnail, then run `nv-core-package-sample`\n    and `nv-core-package-sample-validation` on that deliverable folder only.\n14. Emit the consolidated workflow report with the final USD path, all stage\n    reports, validation findings, rerun reasons, and next work.\n\nUse the `simready-conform-profile` reference only after property assignment\nwhen `property_assignment_intent=run`. It routes feature repair to upstream\nSimReady Foundation FET skills such\nas `simready-foundation-conform-fet-000-core`,\n`simready-foundation-conform-fet-001-minimal`,\n`simready-foundation-conform-fet-004-simulate-multi-body-physics`, and\n`simready-foundation-conform-fet-005-simulate-grasp-physics` from branch\n`main`.\n\nIf `simready-validate` reports a repairable requirement after the first\nconformance pass, feed the structured requirement IDs back into\nthe `simready-conform-profile` reference before writing the final result. In\nparticular, `GSP.001` is owned by upstream\n`simready-foundation-conform-fet-005-simulate-grasp-physics`; run that skill when a\nvision-capable agent can inspect visual evidence or explicit grasp points were\nprovided, otherwise record the FET005 step as blocked by missing vision\u002Fpoints\ninstead of treating it as an optional preview task.\nFor `RB.MB.001`, route the failure to\nupstream `simready-foundation-conform-fet-004-simulate-multi-body-physics`. Do not assume\nmultiple visual prims are multiple rigid bodies; inspect\n`UsdPhysics.RigidBodyAPI` applications. When the Physics Agent report shows\ncomposed topology optimization or the USD has existing component colliders\u002Fpart\nroots and the profile validator reports FET004\u002FRB.MB.001, FET004 should promote\nthose existing components into rigid bodies without creating geometry. Do not\nmark the gate not applicable until after confirming there are fewer than two\nreusable body candidates.\n\n## Output Format\n\nEmit a consolidated workflow report in Markdown, and include JSON when the\nworkflow writes structured artifacts. The report must include:\n\n- Overall status: `passed`, `blocked`, `failed`, or `needs_rerun`.\n- Request summary: source asset path, detected source format, output root,\n  selected SimReady profile\u002Fversion, and property assignment intent.\n- Ordered stage results: stage reference, input artifact, output USD or USDZ\n  path, report path, status, blocker reason, and rerun reason when applicable.\n- Content Agents readiness and property assignment results with service URLs,\n  tokens, and credentials redacted.\n- Conformance and validation findings grouped by gate, requirement ID, selected\n  FET repair reference, repair-loop attempt, and final disposition.\n- Final artifacts: final reported USD path, render preview path when requested,\n  package root and package validation report when packaging ran, Markdown report\n  path, JSON report path when present, and recommended next work.\n\n## Detailed References\n\nRead only the references needed for the current request:\n\n- `references\u002Fpreflight\u002FREADME.md`: deterministic local setup, manifest\u002Fenv\n  contract, Linux and Windows wrappers, Content Agents deployment opt-out, and\n  guardrail behavior.\n- `references\u002Fworkflow.md`: inputs, source routing, detailed workflow,\n  validation policy, output report fields, approval points, and next steps.\n- `references\u002Fcommands.md`: concrete portable script command patterns for each\n  stage.\n- `references\u002Fassemble-package-source\u002FREADME.md`: two-zone package source\n  assembly, canonical root USD naming, thumbnail placement, and self-contained\n  deliverable checks.\n\n## Publishing Layout Notes\n\nUse `skills\u002Fomniverse-cad-to-simready\u002F` as the source of truth for this product\nrepo's skill. The `.agents\u002Fskills` symlink is a compatibility alias for local\nagentskills.io-style discovery, and `.codex\u002Fskills` and `.claude\u002Fskills` are\nagent-specific compatibility aliases.\n\nFrontmatter keeps `version` and `tools` at top level for agentskills.io runtime\ncompatibility. NVCARPS discoverability fields live under `metadata`.\n\nThe nested `references\u002F` tree is intentional. It keeps one public catalog skill\nwhile retaining script-bearing atomic stage references, upstream handoff notes,\nand router documentation under the workflow. Do not flatten those references or\npromote nested README references to sibling `SKILL.md` files unless the repo's\npublishing model changes.\n\n## Limitations\n\n- This workflow coordinates existing conversion, property assignment,\n  conformance, validation, rendering, and packaging skills; it does not replace\n  them with a single monolithic runner command.\n- Stop at the first failing deployment, conversion, property-assignment, or\n  conformance authoring gate unless the user explicitly asks for best-effort\n  continuation.\n- Upstream `simready-foundation-conform-fet-005-simulate-grasp-physics` needs visual\n  review or explicit grasp points before it can author a meaningful grasp\n  vector.\n\n## Troubleshooting\n\n| Symptom | Cause | Fix |\n|---------|-------|-----|\n| Downstream reference reports that cad-to-simready preflight has not prepared a component | `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` is set, but the manifest is missing or the required runtime\u002Fservice is not `ready` | Run `preflight\u002Fscripts\u002Fpreflight.py`, source the generated env file, or explicitly disable service deployment with `--skip-content-agents` only when Content Agents are out of scope. |\n| Workflow stops on `GSP.001` and reports the failure as unclassified | Visual evidence or explicit grasp points were not provided to FET005 | Run upstream `simready-foundation-conform-fet-005-simulate-grasp-physics` only after a vision-capable agent has reviewed the asset, or pass explicit grasp points. Otherwise report the FET005 step as `blocked`, not failed. |\n| Validation fails after a meaningful USD artifact already exists | Workflow stopped at the first validation finding | Continue remaining diagnostic gates and mark the result `needs_rerun`. Do not stop at validation findings once a USD artifact has been produced. |\n| Property-assignment stage fails with a missing service endpoint | Content Agents service was not deployed before conversion | Run `deploy-content-agents` first. Do not start asset inspection, conversion, validation, conformance, rendering, or packaging before Content Agents readiness when property assignment will run. |\n| Material Agent reports that rendering produced `0 images` after unit or profile repair | A FET repair, commonly FET001 unit normalization, was applied before Material Agent and changed the USD layering\u002Fscene state consumed by the service | Rerun assignment from the converted\u002Fminimum-valid USD: Material Agent first, then Physics Agent, then run `simready-conform-profile` and FET repairs on the latest service-authored USD. |\n| Material or Physics Agent local optimized path reports `Permission denied: '\u002Fapp\u002F.build-resources\u002Fscene_optimizer_core\u002Fpython'` | Local Docker Scene Optimizer bundle permissions prevent the non-root service user from reading the packaged SO runtime | Repair the relevant local container with `docker exec --user root content-material-agent-service chmod -R a+rX \u002Fapp\u002F.build-resources\u002Fscene_optimizer_core` or `docker exec --user root content-physics-agent-service chmod -R a+rX \u002Fapp\u002F.build-resources\u002Fscene_optimizer_core`, then rerun the same optimized agent command. Do not treat the no-optimizer fallback as the root cause for instanced\u002Fprototype assets. |\n| `RB.MB.001` fails even though the asset has many prims | The profile counts `UsdPhysics.RigidBodyAPI` prims, not visual or collider prims; Physics Agent may author one root rigid body | Route to upstream `simready-foundation-conform-fet-004-simulate-multi-body-physics`. First ensure Physics Agent used composed-topology optimization when applicable, then promote existing component colliders\u002Fpart roots when the active profile reports FET004\u002FRB.MB.001 and no geometry must be invented. |\n\n## Hard Rules\n\n- Prefer the preflight manifest for local upstream roots, converter\n  executables, SimReady validation runtime, OVRTX endpoint, and Content Agents\n  service URLs. When `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` is set, do not bypass the\n  manifest with direct upstream discovery.\n- Do not run asset inspection, converter probes, local upstream builds,\n  conversion, validation, conformance, rendering, or packaging before Content\n  Agents readiness when property assignment will run.\n- Use stage-specific installed reference scripts directly. Do not add or call a\n  single `omniverse-cad-to-simready` runner command.\n- For source conversion, delegate to the `convert-to-usd` reference; do not\n  substitute another converter for CAD or mesh formats.\n- For property assignment, use Content Agents references as separate atomic steps:\n  material first, then physics, then texture only when requested.\n- When property assignment will run, do not run `simready-conform-profile` or\n  any FET helper before Content Agents. Validate minimum USD first, then run\n  Content Agents on that converted\u002Fminimum-valid USD, then apply FET repairs to\n  the latest service-authored USD.\n- When property assignment will run, do not run `simready-validate` or any\n  SimReady profile validation before Content Agents. The only validation gate\n  allowed before service calls is `validate-usd-minimum`, which is a basic USD\n  viability check.\n- Stop at the first failing deployment, conversion, property-assignment, or\n  conformance authoring gate unless the user explicitly asks for best-effort\n  continuation.\n- Do not stop at validation findings after a meaningful USD artifact exists.\n  Continue remaining diagnostic gates and mark the result `needs_rerun`.\n- Do not leave a `GSP.001` profile failure as an unclassified final finding.\n  Route it to upstream `simready-foundation-conform-fet-005-simulate-grasp-physics`; if\n  the current agent cannot inspect renders or no explicit grasp points are\n  available, report a blocked FET005 repair with the visual evidence path or\n  missing input reason.\n- Preserve every stage report and pass the concrete output USD path from each\n  report into the next stage.\n",{"data":35,"body":52},{"name":4,"description":6,"version":36,"license":26,"tools":37,"compatibility":40,"metadata":41},"0.1.0",[38,39],"Read","Shell","Orchestrator skill. Managed Content Agents deployment requires NVIDIA_API_KEY (build.nvidia.com), Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse\u002Fcontent-agents on branch main. Reused\u002Fprovided endpoints may instead use explicit endpoint and usage-token environment variables. Linux\u002FmacOS only.\n",{"author":42,"tags":43,"domain":49,"languages":50},"Omniverse",[44,45,46,47,48],"physical-ai","simready","workflow","cad","conversion","ai-ml",[51],"python",{"type":53,"children":54},"root",[55,64,71,77,98,104,222,228,257,286,292,305,339,351,372,385,391,673,728,785,791,796,858,864,869,914,920,956,983,1004,1010,1035,1041,1315,1321],{"type":56,"tag":57,"props":58,"children":60},"element","h1",{"id":59},"cad-to-simready",[61],{"type":62,"value":63},"text","CAD to SimReady",{"type":56,"tag":65,"props":66,"children":68},"h2",{"id":67},"when-to-use",[69],{"type":62,"value":70},"When to Use",{"type":56,"tag":72,"props":73,"children":74},"p",{},[75],{"type":62,"value":76},"Use this workflow skill when the user wants an end-to-end pipeline from a\nsource asset to a SimReady asset or package. This skill coordinates existing\nconversion, authoring, validation, conformance, rendering, and packaging\nreferences directly. Do not replace the workflow with a single monolithic\nrunner command.",{"type":56,"tag":72,"props":78,"children":79},{},[80,82,89,91,96],{"type":62,"value":81},"This skill is documentation-driven and does not ship ",{"type":56,"tag":83,"props":84,"children":86},"code",{"className":85},[],[87],{"type":62,"value":88},"scripts\u002Frun.py",{"type":62,"value":90},". It\nshould not depend on a repository checkout. When a stage needs deterministic\nexecution, run the portable script from that stage reference's installed\ndirectory. ",{"type":56,"tag":83,"props":92,"children":94},{"className":93},[],[95],{"type":62,"value":39},{"type":62,"value":97}," is declared because this workflow invokes installed stage\nreference scripts directly; it still must not grow a monolithic runner.",{"type":56,"tag":65,"props":99,"children":101},{"id":100},"prerequisites",[102],{"type":62,"value":103},"Prerequisites",{"type":56,"tag":105,"props":106,"children":107},"ul",{},[108,146,167,204,209],{"type":56,"tag":109,"props":110,"children":111},"li",{},[112,114,120,122,128,130,136,138,144],{"type":62,"value":113},"Prefer running the ",{"type":56,"tag":83,"props":115,"children":117},{"className":116},[],[118],{"type":62,"value":119},"preflight",{"type":62,"value":121}," reference first for deterministic setup. It\ninstalls or verifies local upstream checkouts, writes a\n",{"type":56,"tag":83,"props":123,"children":125},{"className":124},[],[126],{"type":62,"value":127},"cad-to-simready-preflight.json",{"type":62,"value":129}," manifest, and exports\n",{"type":56,"tag":83,"props":131,"children":133},{"className":132},[],[134],{"type":62,"value":135},"PHYSICAL_AI_PREFLIGHT_MANIFEST",{"type":62,"value":137}," plus ",{"type":56,"tag":83,"props":139,"children":141},{"className":140},[],[142],{"type":62,"value":143},"PHYSICAL_AI_REQUIRE_PREFLIGHT=1",{"type":62,"value":145}," for\ndownstream references.",{"type":56,"tag":109,"props":147,"children":148},{},[149,151,157,159,165],{"type":62,"value":150},"Python 3.12 and ",{"type":56,"tag":83,"props":152,"children":154},{"className":153},[],[155],{"type":62,"value":156},"uv",{"type":62,"value":158}," (per repo ",{"type":56,"tag":83,"props":160,"children":162},{"className":161},[],[163],{"type":62,"value":164},"README.md",{"type":62,"value":166},").",{"type":56,"tag":109,"props":168,"children":169},{},[170,172,178,180,186,188,194,196,202],{"type":62,"value":171},"NVIDIA_API_KEY from ",{"type":56,"tag":83,"props":173,"children":175},{"className":174},[],[176],{"type":62,"value":177},"https:\u002F\u002Fbuild.nvidia.com",{"type":62,"value":179}," when local Content Agents\ndeployment will run. Already-running endpoints may instead use explicit\nendpoint variables plus usage tokens such as ",{"type":56,"tag":83,"props":181,"children":183},{"className":182},[],[184],{"type":62,"value":185},"NGC_API_KEY",{"type":62,"value":187},", ",{"type":56,"tag":83,"props":189,"children":191},{"className":190},[],[192],{"type":62,"value":193},"NVCF_API_KEY",{"type":62,"value":195},",\nor ",{"type":56,"tag":83,"props":197,"children":199},{"className":198},[],[200],{"type":62,"value":201},"CONTENT_AGENTS_*_TOKEN",{"type":62,"value":203},".",{"type":56,"tag":109,"props":205,"children":206},{},[207],{"type":62,"value":208},"Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and\nOVRTX stages.",{"type":56,"tag":109,"props":210,"children":211},{},[212,214,220],{"type":62,"value":213},"Local upstream checkouts under\n",{"type":56,"tag":83,"props":215,"children":217},{"className":216},[],[218],{"type":62,"value":219},"${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME\u002F.physical-ai-skill-hub\u002Fupstreams}",{"type":62,"value":221},"\nwhen a downstream stage needs upstream scripts or specs.",{"type":56,"tag":65,"props":223,"children":225},{"id":224},"minimum-viable-scope",[226],{"type":62,"value":227},"Minimum Viable Scope",{"type":56,"tag":72,"props":229,"children":230},{},[231,233,239,241,247,249,255],{"type":62,"value":232},"Conversion-only is a valid workflow request. When the user asks only to convert\nor smoke-test source asset conversion, set ",{"type":56,"tag":83,"props":234,"children":236},{"className":235},[],[237],{"type":62,"value":238},"property_assignment_intent=skip",{"type":62,"value":240},",\ndo not deploy Content Agents, run ",{"type":56,"tag":83,"props":242,"children":244},{"className":243},[],[245],{"type":62,"value":246},"convert-to-usd",{"type":62,"value":248},", then run\n",{"type":56,"tag":83,"props":250,"children":252},{"className":251},[],[253],{"type":62,"value":254},"validate-usd-minimum",{"type":62,"value":256}," on the generated USD if conversion succeeds.",{"type":56,"tag":72,"props":258,"children":259},{},[260,262,268,270,276,278,284],{"type":62,"value":261},"Do not imply that ",{"type":56,"tag":83,"props":263,"children":265},{"className":264},[],[266],{"type":62,"value":267},"uv sync",{"type":62,"value":269}," installs every source converter runtime. URDF,\nMuJoCo\u002FMJCF, and the repo Python dependencies are handled by the project\nenvironment, but NVIDIA-backed source conversion requires an installed and\nvalidated ",{"type":56,"tag":83,"props":271,"children":273},{"className":272},[],[274],{"type":62,"value":275},"NVIDIA-Omniverse\u002Fusd-convert-cad",{"type":62,"value":277}," checkout. 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Validate minimum USD first, then run\nContent Agents on that converted\u002Fminimum-valid USD, then apply FET repairs to\nthe latest service-authored USD.",{"type":56,"tag":109,"props":1383,"children":1384},{},[1385,1386,1391,1393,1398],{"type":62,"value":1374},{"type":56,"tag":83,"props":1387,"children":1389},{"className":1388},[],[1390],{"type":62,"value":603},{"type":62,"value":1392}," or any\nSimReady profile validation before Content Agents. 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Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1569,1572,1573],{"name":1570,"slug":1571,"type":15},"Containers","containers",{"name":1465,"slug":1466,"type":15},{"name":1574,"slug":51,"type":15},"Python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1579,"name":1579,"fn":1580,"description":1581,"org":1582,"tags":1583,"stars":1575,"repoUrl":1576,"updatedAt":1588},"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},[1584,1587],{"name":1585,"slug":1586,"type":15},"CI\u002FCD","ci-cd",{"name":1465,"slug":1466,"type":15},"2026-07-14T05:25:59.97109",{"slug":1590,"name":1590,"fn":1591,"description":1592,"org":1593,"tags":1594,"stars":1575,"repoUrl":1576,"updatedAt":1600},"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},[1595,1596,1597],{"name":1585,"slug":1586,"type":15},{"name":1465,"slug":1466,"type":15},{"name":1598,"slug":1599,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1602,"name":1602,"fn":1603,"description":1604,"org":1605,"tags":1606,"stars":1575,"repoUrl":1576,"updatedAt":1614},"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},[1607,1610,1611],{"name":1608,"slug":1609,"type":15},"Debugging","debugging",{"name":1598,"slug":1599,"type":15},{"name":1612,"slug":1613,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1616,"name":1616,"fn":1617,"description":1618,"org":1619,"tags":1620,"stars":1575,"repoUrl":1576,"updatedAt":1627},"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},[1621,1624],{"name":1622,"slug":1623,"type":15},"Best Practices","best-practices",{"name":1625,"slug":1626,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1629,"name":1629,"fn":1630,"description":1631,"org":1632,"tags":1633,"stars":1575,"repoUrl":1576,"updatedAt":1641},"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},[1634,1637,1640],{"name":1635,"slug":1636,"type":15},"Machine Learning","machine-learning",{"name":1638,"slug":1639,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1643,"name":1643,"fn":1644,"description":1645,"org":1646,"tags":1647,"stars":1575,"repoUrl":1576,"updatedAt":1652},"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},[1648,1651],{"name":1649,"slug":1650,"type":15},"QA","qa",{"name":1495,"slug":1496,"type":15},"2026-07-14T05:25:53.673039",{"slug":1654,"name":1654,"fn":1655,"description":1656,"org":1657,"tags":1658,"stars":1575,"repoUrl":1576,"updatedAt":1661},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1659,1660],{"name":1465,"slug":1466,"type":15},{"name":1468,"slug":1469,"type":15},"2026-07-14T05:25:49.362534",{"slug":1663,"name":1663,"fn":1664,"description":1665,"org":1666,"tags":1667,"stars":1575,"repoUrl":1576,"updatedAt":1675},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1668,1671,1672],{"name":1669,"slug":1670,"type":15},"Code Review","code-review",{"name":1598,"slug":1599,"type":15},{"name":1673,"slug":1674,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1677,"name":1677,"fn":1678,"description":1679,"org":1680,"tags":1681,"stars":1575,"repoUrl":1576,"updatedAt":1684},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. 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},[1682,1683],{"name":1649,"slug":1650,"type":15},{"name":1495,"slug":1496,"type":15},"2026-07-14T05:25:54.928983",{"slug":1686,"name":1686,"fn":1687,"description":1688,"org":1689,"tags":1690,"stars":1575,"repoUrl":1576,"updatedAt":1693},"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},[1691,1692],{"name":1505,"slug":1506,"type":15},{"name":1585,"slug":1586,"type":15},"2026-07-30T05:29:03.275638",496]