Omniverse Cad To Simready

OpenAIOfficialCreated by OmniverseDistributed by OpenAI

Skill
Workflow
Software development
Apache-2.0
Medium risk
Has scripts

From the author

Coordinate the end-to-end CAD/source-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/physics 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.

— the description by Omniverse

From the skill’s files

Taken from the skill’s own files (its description, section headings and stated usage). Not an AutoGineer summary, and not written by the author as one.

Coordinate the end-to-end CAD/source-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/physics 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. Use this workflow skill when the user wants an end-to-end pipeline from a source asset to a SimReady asset or package. This skill coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly. Do not replace the workflow with a single monolithic runner command.

What it does

When to Use
Prerequisites
Minimum Viable Scope
First Action
Instructions
Output Format

When to use it

Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and…
Use this workflow skill when the user wants an end-to-end pipeline from a source asset to a SimReady asset or package.
This skill coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly.
This skill is documentation-driven and does not ship scripts/run.py.
It should not depend on a repository checkout.
When a stage needs deterministic execution, run the portable script from that stage reference's installed directory.

Advantages

The skill’s files do not state this.

Usage examples

The skill’s files do not state this.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/openai/omniverse-cad-to-simready/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip omniverse-cad-to-simready.zip -d .agents/skills

Documentation by Omniverse

CAD to SimReady

When to Use

Use this workflow skill when the user wants an end-to-end pipeline from a source asset to a SimReady asset or package. This skill coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly. Do not replace the workflow with a single monolithic runner command.

This skill is documentation-driven and does not ship scripts/run.py. It should not depend on a repository checkout. When a stage needs deterministic execution, run the portable script from that stage reference's installed directory. Shell is declared because this workflow invokes installed stage reference scripts directly; it still must not grow a monolithic runner.

Prerequisites

Prefer running the preflight reference first for deterministic setup. It installs or verifies local upstream checkouts, writes a cad-to-simready-preflight.json manifest, and exports PHYSICAL_AI_PREFLIGHT_MANIFEST plus PHYSICAL_AI_REQUIRE_PREFLIGHT=1 for downstream references.
Python 3.12 and uv (per repo README.md).
NVIDIA_API_KEY from https://build.nvidia.com when local Content Agents deployment will run. Already-running endpoints may instead use explicit endpoint variables plus usage tokens such as NGC_API_KEY, NVCF_API_KEY, or CONTENT_AGENTS_*_TOKEN.
Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and OVRTX stages.
Local upstream checkouts under ${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams} when a downstream stage needs upstream scripts or specs.

Minimum Viable Scope

Conversion-only is a valid workflow request. When the user asks only to convert or smoke-test source asset conversion, set property_assignment_intent=skip, do not deploy Content Agents, run convert-to-usd, then run validate-usd-minimum on the generated USD if conversion succeeds.

Do not imply that uv sync installs every source converter runtime. URDF, MuJoCo/MJCF, and the repo Python dependencies are handled by the project environment, but NVIDIA-backed source conversion requires an installed and validated NVIDIA-Omniverse/usd-convert-cad checkout. If that runtime is missing or does not support the source, preserve the blocked conversion report and its install_hint instead of attempting an unrequested local build or substituting another converter.

First Action

For any broad CAD/source-asset to SimReady request, assume property_assignment_intent=run unless the user explicitly asks for conversion-only, validation-only, or no material/physics assignment.

Before invoking converter, validation, Content Agents, OVRTX, packaging, or FET helper scripts, run the preflight reference or verify an existing PHYSICAL_AI_PREFLIGHT_MANIFEST. Treat preflight as the mandatory dependency bootstrap step, not as workflow routing. If the user explicitly asks not to deploy services or asks for conversion-only/validation-only, use --skip-content-agents. When PHYSICAL_AI_REQUIRE_PREFLIGHT=1 is set and a required component is not ready in the manifest, downstream references must block with the preflight guardrail instead of rediscovering upstreams or services directly.

When property_assignment_intent=run, the first operational action after confirming the source path and resolving intent is to verify or deploy Content Agents services. Do this before asset-context inspection, converter dependency checks, conversion, validation, conformance, rendering, packaging, or upstream source builds.

Use healthy existing endpoints when available. If OVRTX, Material, or Physics endpoints are missing or unhealthy, run deploy-content-agents first and do not continue until the shared standalone OVRTX renderer plus independent Material and Physics service containers are healthy and exported through CONTENT_AGENTS_*_BASE_URL. Deploy the Texture Agent too when texture generation is requested.

If required deployment authentication is missing, ask the user for NVIDIA_API_KEY and wait. If a provided endpoint requires usage auth, ask for the appropriate usage token instead. If deployment cannot produce healthy services, report Content Agents readiness as blocked instead of proceeding to conversion.

Instructions

Confirm the source asset path exists, resolve output_root, and classify the request as end-to-end, conversion-only, validation-only, or packaging.
Resolve property_assignment_intent before running any asset inspection, converter probe, conversion, validation, conformance, rendering, or packaging step.
Run preflight for the selected workflow targets, unless a ready PHYSICAL_AI_PREFLIGHT_MANIFEST is already configured. Source the generated env file before running downstream scripts. Treat preflight as dependency setup only: it may use a provided --source-asset, --source-format, or --conversion-tools value to scope dependency checks, but convert-to-usd and the upstream converter references still decide actual conversion support.
Verify or deploy Content Agents services first when property_assignment_intent=run; block on missing authentication or unhealthy services instead of continuing.
Read references/workflow.md and references/commands.md, then run only the stage references needed for the current request.
Run identify-asset-context on the original source asset when web search is available or property assignment will run.
Route the source through convert-to-usd, or skip conversion for existing USD input and treat the source path as the current USD path.
Run validate-usd-minimum before expensive downstream work. Treat this as a viability gate only: record unit/profile issues such as metersPerUnit != 1.0, but do not run simready-conform-profile, FET001, or any other FET repair before Content Agents assignment when property assignment will run.
Run Content Agents material, physics, and optional texture assignment on the converted/minimum-valid USD when requested or required.
Run simready-conform-profile on the latest simulation USD path after property assignment and preserve every selected FET repair report.
Run validation gates in order: omni-asset-validate, omni-asset-validate-geometry, omni-asset-validate-physics, and simready-validate.
Rerun simready-conform-profile when simready-validate reports a repairable requirement, then rerun profile validation on the newest authored USD.
Run ovrtx-render-service when preview, thumbnail, or inspection images are requested. When package outputs are requested, run assemble-package-source next to create the clean deliverable/ package source from the final USD and thumbnail, then run nv-core-package-sample and nv-core-package-sample-validation on that deliverable folder only.
Emit the consolidated workflow report with the final USD path, all stage reports, validation findings, rerun reasons, and next work.

Use the simready-conform-profile reference only after property assignment when property_assignment_intent=run. It routes feature repair to upstream SimReady Foundation FET skills such as simready-foundation-conform-fet-000-core, simready-foundation-conform-fet-001-minimal, simready-foundation-conform-fet-004-simulate-multi-body-physics, and simready-foundation-conform-fet-005-simulate-grasp-physics from branch main.

If simready-validate reports a repairable requirement after the first conformance pass, feed the structured requirement IDs back into the simready-conform-profile reference before writing the final result. In particular, GSP.001 is owned by upstream simready-foundation-conform-fet-005-simulate-grasp-physics; run that skill when a vision-capable agent can inspect visual evidence or explicit grasp points were provided, otherwise record the FET005 step as blocked by missing vision/points instead of treating it as an optional preview task. For RB.MB.001, route the failure to upstream simready-foundation-conform-fet-004-simulate-multi-body-physics. Do not assume multiple visual prims are multiple rigid bodies; inspect UsdPhysics.RigidBodyAPI applications. When the Physics Agent report shows composed topology optimization or the USD has existing component colliders/part roots and the profile validator reports FET004/RB.MB.001, FET004 should promote those existing components into rigid bodies without creating geometry. Do not mark the gate not applicable until after confirming there are fewer than two reusable body candidates.

Output Format

Emit a consolidated workflow report in Markdown, and include JSON when the workflow writes structured artifacts. The report must include:

Overall status: passed, blocked, failed, or needs_rerun.
Request summary: source asset path, detected source format, output root, selected SimReady profile/version, and property assignment intent.
Ordered stage results: stage reference, input artifact, output USD or USDZ path, report path, status, blocker reason, and rerun reason when applicable.
Content Agents readiness and property assignment results with service URLs, tokens, and credentials redacted.
Conformance and validation findings grouped by gate, requirement ID, selected FET repair reference, repair-loop attempt, and final disposition.
Final artifacts: final reported USD path, render preview path when requested, package root and package validation report when packaging ran, Markdown report path, JSON report path when present, and recommended next work.

Detailed References

Read only the references needed for the current request:

references/preflight/README.md: deterministic local setup, manifest/env contract, Linux and Windows wrappers, Content Agents deployment opt-out, and guardrail behavior.
references/workflow.md: inputs, source routing, detailed workflow, validation policy, output report fields, approval points, and next steps.
references/commands.md: concrete portable script command patterns for each stage.
references/assemble-package-source/README.md: two-zone package source assembly, canonical root USD naming, thumbnail placement, and self-contained deliverable checks.

Publishing Layout Notes

Use skills/omniverse-cad-to-simready/ as the source of truth for this product repo's skill. The .agents/skills symlink is a compatibility alias for local agentskills.io-style discovery, and .codex/skills and .claude/skills are agent-specific compatibility aliases.

Frontmatter keeps version and tools at top level for agentskills.io runtime compatibility. NVCARPS discoverability fields live under metadata.

The nested references/ tree is intentional. It keeps one public catalog skill while retaining script-bearing atomic stage references, upstream handoff notes, and router documentation under the workflow. Do not flatten those references or promote nested README references to sibling SKILL.md files unless the repo's publishing model changes.

Limitations

This workflow coordinates existing conversion, property assignment, conformance, validation, rendering, and packaging skills; it does not replace them with a single monolithic runner command.
Stop at the first failing deployment, conversion, property-assignment, or conformance authoring gate unless the user explicitly asks for best-effort continuation.
Upstream simready-foundation-conform-fet-005-simulate-grasp-physics needs visual review or explicit grasp points before it can author a meaningful grasp vector.

Troubleshooting

Symptom	Cause	Fix
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/service is not `ready`	Run `preflight/scripts/preflight.py`, source the generated env file, or explicitly disable service deployment with `--skip-content-agents` only when Content Agents are out of scope.
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.
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.
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.
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/scene state consumed by the service	Rerun assignment from the converted/minimum-valid USD: Material Agent first, then Physics Agent, then run `simready-conform-profile` and FET repairs on the latest service-authored USD.
Material or Physics Agent local optimized path reports `Permission denied: '/app/.build-resources/scene_optimizer_core/python'`	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 /app/.build-resources/scene_optimizer_core` or `docker exec --user root content-physics-agent-service chmod -R a+rX /app/.build-resources/scene_optimizer_core`, then rerun the same optimized agent command. Do not treat the no-optimizer fallback as the root cause for instanced/prototype assets.
`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/part roots when the active profile reports FET004/RB.MB.001 and no geometry must be invented.

Hard Rules

Prefer the preflight manifest for local upstream roots, converter executables, SimReady validation runtime, OVRTX endpoint, and Content Agents service URLs. When PHYSICAL_AI_REQUIRE_PREFLIGHT=1 is set, do not bypass the manifest with direct upstream discovery.
Do not run asset inspection, converter probes, local upstream builds, conversion, validation, conformance, rendering, or packaging before Content Agents readiness when property assignment will run.
Use stage-specific installed reference scripts directly. Do not add or call a single omniverse-cad-to-simready runner command.
For source conversion, delegate to the convert-to-usd reference; do not substitute another converter for CAD or mesh formats.
For property assignment, use Content Agents references as separate atomic steps: material first, then physics, then texture only when requested.
When property assignment will run, do not run simready-conform-profile or any FET helper before Content Agents. Validate minimum USD first, then run Content Agents on that converted/minimum-valid USD, then apply FET repairs to the latest service-authored USD.
When property assignment will run, do not run simready-validate or any SimReady profile validation before Content Agents. The only validation gate allowed before service calls is validate-usd-minimum, which is a basic USD viability check.
Stop at the first failing deployment, conversion, property-assignment, or conformance authoring gate unless the user explicitly asks for best-effort continuation.
Do not stop at validation findings after a meaningful USD artifact exists. Continue remaining diagnostic gates and mark the result needs_rerun.
Do not leave a GSP.001 profile failure as an unclassified final finding. Route it to upstream simready-foundation-conform-fet-005-simulate-grasp-physics; if the current agent cannot inspect renders or no explicit grasp points are available, report a blocked FET005 repair with the visual evidence path or missing input reason.
Preserve every stage report and pass the concrete output USD path from each report into the next stage.

Files

references/convert-to-usd/references/usd-convert-cad/README.md

Convert CAD to USD

When to Use

Use this reference for NVIDIA-backed source conversion. On supported architectures, conversion delegates to upstream usd-convert-cad, a headless Omniverse Kit Python wrapper that installs omniverse-kit, fetches converter core extensions from the Kit registry, routes supported source formats through its format metadata, and writes its own JSON conversion report.

Guardrail: upstream usd-convert-cad is the default converter backend for this reference's NVIDIA-backed source conversion. The only local fallback is Linux arm64, where upstream usd-convert-cad is not available yet; that path uses a private NVIDIA Kit App Template application with CAD Converter extensions. Do not fall back to usd-convert-asset, hand-authored USD, mesh converters, or other substitute CAD converters.

Upstream Reference

NVIDIA Omniverse usd-convert-cad repository: https://github.com/NVIDIA-Omniverse/usd-convert-cad
Upstream CAD conversion skill: https://github.com/NVIDIA-Omniverse/usd-convert-cad/blob/main/.agents/skills/usd-convert-cad/SKILL.md
Linux arm64 fallback: NVIDIA Kit App Template repository: https://github.com/NVIDIA-Omniverse/kit-app-template
Linux arm64 fallback docs: https://docs.omniverse.nvidia.com/kit/docs/kit-app-template/latest/
NVIDIA Omniverse CAD Converter extension docs: https://docs.omniverse.nvidia.com/kit/docs/omni.kit.converter.cad/latest/
NVIDIA HOOPS CAD core extension docs: https://docs.omniverse.nvidia.com/kit/docs/omni.kit.converter.hoops_core/latest/Overview.html
NVIDIA DGN CAD core extension docs: https://docs.omniverse.nvidia.com/kit/docs/omni.kit.converter.dgn_core/latest/Overview.html
NVIDIA JT CAD core extension docs: https://docs.omniverse.nvidia.com/kit/docs/omni.kit.converter.jt_core/latest/Overview.html
Linux arm64 optional service mode docs: https://docs.omniverse.nvidia.com/kit/docs/omni.services.convert.cad/latest/Usage.html

Browser, raw-file fetches, or unauthenticated GitHub access can fail depending on access level. If that happens, use an authenticated local clone of https://github.com/NVIDIA-Omniverse/usd-convert-cad and read the referenced paths from that checkout.

Use $HOME/.physical-ai-skill-hub/upstreams/usd-convert-cad as the default stable upstream checkout path. Set PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT to move the shared upstream root, or set USD_CONVERT_CAD_ROOT / --usd-convert-cad-root for this converter only. An existing legacy $HOME/.usd-convert-cad checkout is still accepted when no shared root is configured, but new setup should use the shared upstream root. Do not use /tmp as the runtime checkout location for conversions. A /tmp clone is acceptable only for short-lived inspection.

On Linux arm64 only, the wrapper does not require USD_CONVERT_CAD_ROOT. Instead it locates Kit App Template from KIT_APP_TEMPLATE_ROOT or $HOME/.kit-app-template, the build directory from KIT_APP_TEMPLATE_BUILD_DIR or _build/<platform>/release, and the Kit executable from KIT_APP_TEMPLATE_KIT_EXECUTABLE, KIT_EXECUTABLE, or <kit-build-dir>/kit/kit.

Inputs

Collect a source file, output directory, and optional --usd-convert-cad-root. Supported source suffixes and converter routing belong to upstream SUPPORTED_FORMATS / format metadata in src/usd_convert_cad/formats.py and .agents/skills/usd-convert-cad/SKILL.md. This reference reads the upstream formats table from the configured checkout when it is available and keeps only a fallback snapshot for blocked reports when the checkout is missing. Examples such as .stp, .step, .igs, .iges, .dgn, .ifc, .ifczip, .jt, and proprietary CAD files route to usd-convert-cad, never to a substitute converter. Mesh/scene formats also route here when upstream usd-convert-cad lists them as supported; otherwise they are reported unsupported rather than sent to usd-convert-asset. Do not choose jt_core, dgn_core, or hoops_core in this wrapper; upstream usd-convert-cad selects the converter from its supported-format metadata. Legacy backend-selection arguments are accepted for compatibility, but the value is ignored by this wrapper and is never forwarded to upstream convert.py.

On Linux arm64, probe support does not use a local source-format allowlist because the Kit App Template fallback is the scoped architecture workaround. The fallback reports support and lets the installed Kit CAD Converter runtime determine whether the input can be converted. The fallback accepts Kit App Template options such as --kit-app-template-root, --kit-build-dir, --kit-executable, --execution-mode, --config-path, --fine, and --coarse; these options are ignored by the upstream path on other architectures.

Dependency Check

Require:

usd-convert-cad from this repo.
A local NVIDIA-Omniverse/usd-convert-cad checkout from https://github.com/NVIDIA-Omniverse/usd-convert-cad, preferably at $HOME/.physical-ai-skill-hub/upstreams/usd-convert-cad.
Python 3.12 available for upstream setup.
Upstream setup completed with the upstream runtime Python, for example .venv/bin/python install.py from the upstream checkout after the venv is created.
Upstream environment validated with python validate.py.
Network access to the Kit extension registry on first run.
Accepted Omniverse terms for non-interactive runs. The wrappers set or expect OMNI_KIT_ACCEPT_EULA=yes.

For Linux arm64 fallback, require:

Local NVIDIA-Omniverse/kit-app-template checkout from https://github.com/NVIDIA-Omniverse/kit-app-template, preferably at $HOME/.kit-app-template.
A built Kit App Template app whose dependencies include omni.kit.converter.cad or the specific CAD core extension required by the input: omni.kit.converter.hoops_core, omni.kit.converter.dgn_core, or omni.kit.converter.jt_core.
Built Kit executable under _build/<platform>/release/kit/kit or an explicit --kit-executable.
For optional --execution-mode service, omni.services.convert.cad-* in _build/<platform>/release/extscache, including omni/services/convert/cad/services/process/{hoops,dgn,jt}_main.py.
Accepted Omniverse terms for non-interactive runs. The fallback sets ACCEPT_EULA=Y and OMNI_KIT_ACCEPT_EULA=yes.

Do not silently install or build missing dependencies. If the checkout, .venv, omniverse-kit, converter core extension, platform support, Kit App Template build, or CAD Converter license is unavailable, run the wrapper and preserve its blocked conversion report. This reference may invoke upstream validate.py to verify readiness on supported architectures. On Linux arm64 it may start the local Kit App Template runtime because that is the scoped fallback path.

Conversion Workflow

Confirm the source asset exists.
On Linux arm64, when this CAD reference is selected by the higher-level router, use the Kit App Template CAD Converter fallback and let the installed Kit runtime determine conversion support.
On other architectures, confirm upstream usd-convert-cad lists the source suffix as supported.
Locate the upstream checkout from --usd-convert-cad-root, USD_CONVERT_CAD_ROOT, $PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT/usd-convert-cad, or $HOME/.physical-ai-skill-hub/upstreams/usd-convert-cad; on Linux arm64, locate Kit App Template from KIT_APP_TEMPLATE_ROOT, KIT_APP_TEMPLATE_BUILD_DIR, or KIT_APP_TEMPLATE_KIT_EXECUTABLE.
If setup state is unknown, follow the selected runtime's install/validate guidance.
Run this installed reference's portable script; before upstream conversion it delegates readiness to upstream validate.py, then calls upstream python "$USD_CONVERT_CAD_ROOT/convert.py" ... --quiet --report .... On Linux arm64, it starts the built Kit executable with the selected CAD core extension and a generated runner/config sidecar.
Preserve both reports on the upstream path: this repo's normalized conversion report and the upstream *_usd_convert_cad_status.json sidecar. Preserve generated Kit fallback sidecars on the Linux arm64 path.
If USD is generated, hand it to validate-usd-minimum.
If blocked, report the exact upstream readiness or fallback runtime failure, such as a missing checkout, stale setup, Python 3.12 issue, omniverse-kit issue, missing Kit App Template build, required CAD core extension, platform issue, registry download failure, conversion failure, or CAD license dependency.

CLI Pattern

Default STEP conversion:

python3 scripts/run.py asset.step output_dir \
  --report output_dir/conversion.json

Explicit upstream checkout:

python3 scripts/run.py asset.jt output_dir \
  --usd-convert-cad-root /path/to/usd-convert-cad \
  --report output_dir/conversion.json

Linux arm64 fallback with explicit Kit App Template build:

python3 scripts/run.py asset.step output_dir \
  --kit-build-dir /path/to/kit-app-template/_build/linux-aarch64/release \
  --report output_dir/conversion.json

When running from outside the reference directory, use the installed reference path:

python3 /path/to/skills/omniverse-cad-to-simready/references/convert-to-usd/references/usd-convert-cad/scripts/run.py asset.step output_dir --report output_dir/conversion.json

Check dependencies with:

python3 scripts/check_dependencies.py --report dependency-check.json

The dependency check delegates to upstream validate.py when the checkout, convert.py, and validate.py are present. It can start the upstream runtime and access the extension registry; use it as the CAD readiness gate before batching per-asset conversions. On Linux arm64, the dependency check reports Kit App Template root, build directory, and Kit executable readiness instead.

Output Format

This repo normalizes the upstream status into the shared conversion report contract and includes:

source_asset_path
source_format: cad
converter_skill: usd-convert-cad
converter_tool: usd-convert-cad on the upstream path, or NVIDIA Kit App Template CAD Converter on Linux arm64 fallback
converter_command, the upstream convert.py invocation with explicit output USD and report paths, or the Kit executable command that enables the selected CAD core fallback extension
output_directory
output_usd_path
generated_files
sidecar_inputs, including the upstream checkout, upstream JSON report, and upstream log when available, or the Kit App Template checkout/build/config/runner sidecars for Linux arm64 fallback
warnings, including the selected runtime and converter core
errors
next_step: validate-usd-minimum

The upstream sidecar report includes the selected converter extension, converter module, converter options, elapsed time, pass/fail status, and upstream warnings/errors.

Known Caveats

Upstream usd-convert-cad is still Omniverse Kit and CAD Converter based; the Linux arm64 fallback uses Kit App Template and CAD Converter directly until upstream supports that architecture.
Python 3.12 is required by upstream setup.
The first conversion can take longer because Kit downloads converter extensions from the registry.
If validate.py reports Result.ERROR_ACCESS_DENIED while pulling a Kit extension, treat it as an upstream Kit registry/CDN access problem, not a routing problem. The portable scripts report kind: kit_registry_access_denied with the extension, URL host, exit code, and a recovery hint. Fix Horde node egress, proxy, or credentials, or pre-populate and reuse the upstream Kit extension cache, then rerun OMNI_KIT_ACCEPT_EULA=yes python validate.py.
Proprietary CAD formats can require CAD Converter licensing.
Detailed converter option names must come from the upstream skill, Kit App Template docs, or installed extension docs.
A successful CAD conversion does not imply simulation readiness.

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.

Needs: Needs an API key, Needs Python

The author says: 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/content-agents on branch main. Reused/provided endpoints may instead use explicit endpoint and usage-token environment variables. Linux/macOS only.

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs
Instructions that use the network
Downloads piped into a shell
Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

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

Omniverse

Publisher

OpenAI (Official)

Distributed by

OpenAI

Source

openai/plugins · plugins/nvidia/skills/omniverse-cad-to-simready · commit d420d13

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (written by AutoGineer)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by Omniverse or OpenAI.

Download ZIP

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

From the author

From the skill’s files

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Omniverse

CAD to SimReady

When to Use

Prerequisites

Minimum Viable Scope

First Action

Instructions

Output Format

Detailed References

Publishing Layout Notes

Limitations

Troubleshooting

Hard Rules

Files

references/convert-to-usd/references/usd-convert-cad/README.md

Convert CAD to USD

When to Use

Upstream Reference

Inputs

Dependency Check

Conversion Workflow

CLI Pattern

Output Format

Known Caveats

Requirements and compatibility

Security

Attribution and licence

Related skills

Setup TS Deep Modules

To Spec

Improve Codebase Architecture

Domain Modeling

Codebase Design

Prototype

Download ZIP