[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-simready-foundation-conform-fet-007-nonvisual-materials":3,"mdc-idut2s-key":34,"related-repo-nvidia-simready-foundation-conform-fet-007-nonvisual-materials":1733,"related-org-nvidia-simready-foundation-conform-fet-007-nonvisual-materials":1819},{"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},"simready-foundation-conform-fet-007-nonvisual-materials","repair SimReady nonvisual material attributes","Use for repairing SimReady nonvisual sensor material attributes on bound USD materials.",{"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},"Physics","physics",52,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fsimready-foundation","2026-07-14T05:33:59.248079","Apache-2.0",8,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"SimReady Foundation is a central repository for defining simulation content specifications based on various runtime use cases. ","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fsimready-foundation\u002Ftree\u002FHEAD\u002Fskills\u002Fsimready-foundation-conform-fet-007-nonvisual-materials","---\nname: simready-foundation-conform-fet-007-nonvisual-materials\ndescription: \"Use for repairing SimReady nonvisual sensor material attributes on bound USD materials.\"\nlicense: Apache-2.0\nmetadata:\n  author: \"Shaad Boochoon \u003Csboochoon@nvidia.com>\"\n  tags:\n    - simready\n    - conformance\n    - materials\n---\n\n\n# SimReady Conform FET-007 Nonvisual Materials\n\n## Purpose\nUse this workflow skill when the selected profile, validation report, or user request targets `FET007_BASE_NEUTRAL` non-visual sensor material attributes. FET007 authors and repairs sensor-facing material classification on bound `UsdShade.Material` prims so radar, lidar, thermal, and related RTX sensor simulations can reason about surfaces beyond visible shader appearance.\n\nThis is a material-semantics repair skill, not a shader-generation skill. Work on a staged copy under the requested output directory, preserve existing visual material bindings, and stop at the first FET007 issue that needs material identity or user judgement.\n\nFET007 is not currently used by the default prop robotics profiles in `profiles.toml`. When a profile does not include FET007, run this skill only when the user explicitly asks for non-visual sensor material conformance or supplies a profile\u002Fworkflow that includes `FET007_BASE_NEUTRAL`.\n\n## Prerequisites\n\nRead the source-of-truth files named below before editing. Work on staged outputs where the skill requires them, and keep validation evidence with the result.\n\n## Source of Truth\n\nBefore changing an asset, load the selected FET007 manifest and requirement docs:\n\n- `nv_core\u002Fsr_specs\u002Fdocs\u002Ffeatures\u002FFET_007_base_neutral-0.2.0-nonvisual_materials.json`\n- `nv_core\u002Fsr_specs\u002Fdocs\u002Ffeatures\u002FFET_007-nonvisual_materials.md`\n- `nv_core\u002Fsr_specs\u002Fdocs\u002Fcapabilities\u002Fnonvisual_sensors\u002Fnonvisual_materials\u002Fcapability-nonvisual_materials.md`\n- `nv_core\u002Fsr_specs\u002Fdocs\u002Fcapabilities\u002Fnonvisual_sensors\u002Fnonvisual_materials\u002Frequirements.md`\n- `nv_core\u002Fsr_specs\u002Fdocs\u002Fcapabilities\u002Fnonvisual_sensors\u002Fnonvisual_materials\u002Fvalidation.py`\n\nTreat the selected JSON manifest as authoritative for the required IDs. If the markdown, manifest, validator, or report disagree on allowed values or requirement behavior, follow the current validation report and call out the mismatch in the stage summary.\n\nFor per-requirement repair details, read `references\u002Ffet007-requirements.md` when a FET007 validation report or inspection identifies matching failures.\n\n## Inputs\n\nCollect these before editing:\n\n| Input | Requirement |\n|---|---|\n| `usd_asset` | Required `.usd`, `.usda`, `.usdc`, or unpacked USD-family asset to repair. |\n| `output_root` | Required or inferred folder for staged assets and reports. |\n| `simready_profile` | Profile being validated, if one includes FET007. |\n| `profile_version` | Profile version, if supplied by the user or validation command. |\n| `validation_report` | Preferred JSON or markdown report from the failing profile or feature validation gate. |\n| `fet007_variant` | Selected feature ID and version, currently `FET007_BASE_NEUTRAL@0.2.0`. |\n| `material_evidence` | Source DCC material metadata, visual material names, shader parameters, property reports, render review, or user-supplied material classifications. |\n| `classification_policy` | User-approved defaults or mappings for unknown materials, coatings, and special attributes. |\n\n## Instructions\n\nUse this checklist when changing the repository:\n\n1. Confirm the input asset exists and determine whether FET007 is actually selected by the profile or explicitly requested by the user.\n2. Parse the validation report and filter to FET007\u002FNVM failures. Do not repair visual shader, texture, rigid-body, physics-material, or grasp issues in this skill.\n3. Load the selected FET007 manifest version and the requirement repair map.\n4. Create a staged output folder under `output_root`; do not mutate the source unless the user explicitly asks for in-place repair.\n5. Inspect the stage before editing:\n   - default prim and composed payload\u002Freference boundaries\n   - renderable `UsdGeom.Gprim` and `UsdGeom.Subset` prims with computed purpose `default` or `render`\n   - computed full-purpose material bindings for those prims\n   - bound and unbound `UsdShade.Material` prims\n   - existing `omni:simready:nonvisual:base`, `omni:simready:nonvisual:coating`, and `omni:simready:nonvisual:attributes`\n   - time samples on those attributes\n   - visual material evidence such as shader metallic, opacity, diffuse color, material names, texture names, and source DCC metadata\n6. Decide the non-visual material classification before authoring:\n   - Use explicit source or user-supplied material labels first.\n   - Use strong visual-material evidence only when the mapping is obvious and allowed by the current validator.\n   - Use user-approved defaults for generic classes such as `plastic`, `steel`, `rubber`, `wood`, `fabric`, or `clear_glass`.\n   - Block when the real material is not representable by the allowed values, or when multiple classes are plausible.\n7. Apply repairs in this order:\n   - Remove, move, or bind orphaned non-visual material attributes only when the material-binding intent is clear.\n   - Add or fix `omni:simready:nonvisual:base` from explicit material evidence.\n   - Add or fix `omni:simready:nonvisual:coating`, defaulting to `none` only when no coating evidence exists and that policy is approved.\n   - Add or fix `omni:simready:nonvisual:attributes`, using `[\"none\"]` or an empty token array only when no special sensor attributes are intended by policy.\n   - Remove time samples from non-visual attributes only after preserving one intended default value.\n   - Re-check consistency with the visual material semantics.\n8. Rerun the same validation gate when a profile includes FET007, or the narrowest available Foundation\u002FOAV capability check for non-visual materials. If no executable FET007 gate is available, report the USD inspection results and the validation gap.\n9. Summarize the stage as passed, failed, skipped, or blocked. Stop when FET007 passes or the next FET007 failure requires material identity, sensor-response policy, visual review, or source-material data.\n\n## Examples\n\nExample request:\n\n```text\nRepair FET007 nonvisual sensor material attributes on a USD asset.\n```\n\nExpected result summary:\n\n```text\nstaged_asset: repaired copy or output directory\nvalidation: selected feature\u002Fprofile gate and report path\nremaining_failures: next failing requirement IDs, if any\n```\n\n## Repair Policy\n\nMake automatic repairs only when the intended result is mechanical and locally verifiable:\n\n- Fix attribute type names to `token` or `token[]` when the value is already valid.\n- Replace invalid token values with a clear approved mapping from source material evidence.\n- Remove time samples and author a default value when one sampled value is clearly the intended static material label.\n- Move non-visual attributes from an unbound duplicate material to the bound material only when the duplicate relationship is obvious.\n- Remove non-visual attributes from a clearly orphaned scratch material only when that material is not referenced or bound.\n\nBlock and report instead of guessing when:\n\n- A material has no reliable source classification.\n- A visual material name or shader color is too generic to identify a sensor material class.\n- The asset material is outside the current allowed FET007 value list.\n- The validator and docs disagree and the repair would risk making sensor semantics less truthful.\n- A material appears visually inconsistent with the proposed non-visual class.\n- The staged asset has no bound visual materials and the user did not ask to create visual materials first.\n\n## Classification Guidance\n\nFET007 values are semantic labels, not visual decoration. Do not choose them just to satisfy the validator.\n\nUse the current validator value list for pass\u002Ffail decisions. Notable current values include:\n\n- Base examples: `aluminum`, `steel`, `oxidized_steel`, `iron`, `plastic`, `rubber`, `wood`, `fabric`, `leather`, `clear_glass`, `frosted_glass`, `concrete`, `brick`, `stone`, `water`, `snow`, `ice`, `calibration_lambertian`\n- Coating values: `none`, `paint`, `clearcoat`, `paint_clearcoat`\n- Attribute values: `none`, `emissive`, `retroreflective`, `single_sided`, `visually_transparent`\n\nCommon consistency checks:\n\n- Metallic visual materials should map to a metal base such as `steel`, `aluminum`, `iron`, `brass`, or `bronze`.\n- Transparent visual materials should usually use a glass-like base and include `visually_transparent`.\n- Painted materials should use a base material plus `paint` or `paint_clearcoat`.\n- Rubber-like dark flexible surfaces should use `rubber`, not generic `plastic`, when evidence supports it.\n- Emissive visual materials should include `emissive`; road reflectors and similar objects may include `retroreflective`.\n\n## Validation Handoff\n\nPreserve reports under the staged output directory. If a selected profile includes FET007 and the Physical AI Skill Hub validation commands are available, use the same profile gate that exposed the failure:\n\n```bash\nuv run --python 3.12 validate-simready-profile \u003Cstaged-usd> \\\n  --profile \u003Cprofile> \\\n  --profile-version \u003Cversion> \\\n  --foundation-root \u003Csimready-foundation-root> \\\n  --foundation-spec-root \u003Csimready-foundation-root>\u002Fnv_core\u002Fsr_specs\u002Fdocs \\\n  --report \u003Coutput-root>\u002Fsimready-profile-after-fet007.json\n```\n\nIf no selected profile includes FET007, do not mutate `profiles.toml` just to test the skill. Run the available non-visual materials capability validator directly if the local environment exposes it; otherwise report the deterministic USD inspection and note that profile validation cannot exercise FET007 until a profile selects it.\n\n## Limitations\n\n- Do not silently mutate the source asset; work on the requested staged output.\n- Do not hide later profile failures after the selected feature gate passes or fails.\n- Do not invent geometry, metadata, or runtime behavior that conflicts with the asset intent.\n\n## Troubleshooting\n\n- Error: validation tooling is unavailable. Solution: run the narrowest available USD or static check and report the gap.\n- Error: a repair would change asset intent. Solution: stop and ask for direction or stage the smallest reversible edit.\n- Error: later profile gates still fail. Solution: report the next failing feature and hand off to the matching conformance skill.\n\n## Resources\n\n- `assets\u002Fopenai.yaml` preserves optional UI metadata for clients that read skill display hints. It is not required for the workflow.\n- `references\u002F` contains detailed requirement notes; load only the files needed for the active validation failure.\n\n## Summary Format\n\nReport:\n\n| Field | Meaning |\n|---|---|\n| `input_usd_path` | Original USD path. |\n| `output_usd_path` | Latest staged\u002Frepaired USD path. |\n| `profile` and `profile_version` | Validation target, if any. |\n| `fet007_variant` | Selected FET007 feature ID and version. |\n| `material_evidence` | Source used for non-visual classification. |\n| `classified_materials` | Bound material prims and authored base\u002Fcoating\u002Fattribute values. |\n| `unbound_material_repairs` | Orphaned attribute fixes or bindings changed for NVM.004. |\n| `time_sample_repairs` | Attributes changed for NVM.006. |\n| `requirements_repaired` | Requirement IDs changed by this skill. |\n| `requirements_blocked` | Requirement IDs that need material identity, source data, or user judgement. |\n| `validation_report` | Path to the rerun validation or inspection report. |\n| `next_step` | Usually run FET007-capable validation or collect missing material evidence. |\n\nKeep the user-facing summary short: which materials were classified, what evidence justified the values, what validation could run, and what remains blocked.\n",{"data":35,"body":42},{"name":4,"description":6,"license":26,"metadata":36},{"author":37,"tags":38},"Shaad Boochoon \u003Csboochoon@nvidia.com>",[39,40,41],"simready","conformance","materials",{"type":43,"children":44},"root",[45,54,61,84,89,109,115,120,126,131,181,186,199,205,210,404,410,415,701,707,712,724,729,738,744,749,792,797,830,836,841,846,1042,1047,1152,1158,1163,1391,1403,1409,1427,1433,1451,1457,1482,1488,1493,1722,1727],{"type":46,"tag":47,"props":48,"children":50},"element","h1",{"id":49},"simready-conform-fet-007-nonvisual-materials",[51],{"type":52,"value":53},"text","SimReady Conform FET-007 Nonvisual Materials",{"type":46,"tag":55,"props":56,"children":58},"h2",{"id":57},"purpose",[59],{"type":52,"value":60},"Purpose",{"type":46,"tag":62,"props":63,"children":64},"p",{},[65,67,74,76,82],{"type":52,"value":66},"Use this workflow skill when the selected profile, validation report, or user request targets ",{"type":46,"tag":68,"props":69,"children":71},"code",{"className":70},[],[72],{"type":52,"value":73},"FET007_BASE_NEUTRAL",{"type":52,"value":75}," non-visual sensor material attributes. FET007 authors and repairs sensor-facing material classification on bound ",{"type":46,"tag":68,"props":77,"children":79},{"className":78},[],[80],{"type":52,"value":81},"UsdShade.Material",{"type":52,"value":83}," prims so radar, lidar, thermal, and related RTX sensor simulations can reason about surfaces beyond visible shader appearance.",{"type":46,"tag":62,"props":85,"children":86},{},[87],{"type":52,"value":88},"This is a material-semantics repair skill, not a shader-generation skill. Work on a staged copy under the requested output directory, preserve existing visual material bindings, and stop at the first FET007 issue that needs material identity or user judgement.",{"type":46,"tag":62,"props":90,"children":91},{},[92,94,100,102,107],{"type":52,"value":93},"FET007 is not currently used by the default prop robotics profiles in ",{"type":46,"tag":68,"props":95,"children":97},{"className":96},[],[98],{"type":52,"value":99},"profiles.toml",{"type":52,"value":101},". When a profile does not include FET007, run this skill only when the user explicitly asks for non-visual sensor material conformance or supplies a profile\u002Fworkflow that includes ",{"type":46,"tag":68,"props":103,"children":105},{"className":104},[],[106],{"type":52,"value":73},{"type":52,"value":108},".",{"type":46,"tag":55,"props":110,"children":112},{"id":111},"prerequisites",[113],{"type":52,"value":114},"Prerequisites",{"type":46,"tag":62,"props":116,"children":117},{},[118],{"type":52,"value":119},"Read the source-of-truth files named below before editing. 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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},[1861,1864],{"name":1862,"slug":1863,"type":15},"CI\u002FCD","ci-cd",{"name":1847,"slug":1848,"type":15},"2026-07-14T05:25:59.97109",{"slug":1867,"name":1867,"fn":1868,"description":1869,"org":1870,"tags":1871,"stars":1852,"repoUrl":1853,"updatedAt":1877},"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},[1872,1873,1874],{"name":1862,"slug":1863,"type":15},{"name":1847,"slug":1848,"type":15},{"name":1875,"slug":1876,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1879,"name":1879,"fn":1880,"description":1881,"org":1882,"tags":1883,"stars":1852,"repoUrl":1853,"updatedAt":1891},"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},[1884,1887,1888],{"name":1885,"slug":1886,"type":15},"Debugging","debugging",{"name":1875,"slug":1876,"type":15},{"name":1889,"slug":1890,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1893,"name":1893,"fn":1894,"description":1895,"org":1896,"tags":1897,"stars":1852,"repoUrl":1853,"updatedAt":1904},"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},[1898,1901],{"name":1899,"slug":1900,"type":15},"Best Practices","best-practices",{"name":1902,"slug":1903,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1906,"name":1906,"fn":1907,"description":1908,"org":1909,"tags":1910,"stars":1852,"repoUrl":1853,"updatedAt":1918},"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},[1911,1914,1917],{"name":1912,"slug":1913,"type":15},"Machine Learning","machine-learning",{"name":1915,"slug":1916,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1920,"name":1920,"fn":1921,"description":1922,"org":1923,"tags":1924,"stars":1852,"repoUrl":1853,"updatedAt":1929},"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},[1925,1928],{"name":1926,"slug":1927,"type":15},"QA","qa",{"name":1803,"slug":1804,"type":15},"2026-07-14T05:25:53.673039",{"slug":1931,"name":1931,"fn":1932,"description":1933,"org":1934,"tags":1935,"stars":1852,"repoUrl":1853,"updatedAt":1940},"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},[1936,1937],{"name":1847,"slug":1848,"type":15},{"name":1938,"slug":1939,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":1942,"name":1942,"fn":1943,"description":1944,"org":1945,"tags":1946,"stars":1852,"repoUrl":1853,"updatedAt":1954},"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},[1947,1950,1951],{"name":1948,"slug":1949,"type":15},"Code Review","code-review",{"name":1875,"slug":1876,"type":15},{"name":1952,"slug":1953,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1956,"name":1956,"fn":1957,"description":1958,"org":1959,"tags":1960,"stars":1852,"repoUrl":1853,"updatedAt":1963},"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},[1961,1962],{"name":1926,"slug":1927,"type":15},{"name":1803,"slug":1804,"type":15},"2026-07-14T05:25:54.928983",{"slug":1965,"name":1965,"fn":1966,"description":1967,"org":1968,"tags":1969,"stars":1852,"repoUrl":1853,"updatedAt":1974},"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},[1970,1973],{"name":1971,"slug":1972,"type":15},"Automation","automation",{"name":1862,"slug":1863,"type":15},"2026-07-30T05:29:03.275638",496]