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/README.md

Convert to USD

When to Use

Use this reference as the conversion router. Ask the converter references which source they support, choose the highest-priority supported reference, preserve conversion metadata, and hand off the result to minimum USD validation before deeper omni-asset-validate, omni-asset-validate-geometry, omni-asset-validate-physics, and simready-validate checks.

Do not perform detailed format-specific conversion here when a specialized converter reference exists.

For NVIDIA-backed conversion references, preserve the upstream tool names. The router does not maintain a mesh/CAD/Gaussian-splat extension table; it calls each reference's probe mode and lets that reference read its upstream capability source.

Prerequisites

Python 3.12 and uv (per repo README.md).
Prefer a ready PHYSICAL_AI_PREFLIGHT_MANIFEST from the preflight reference. NVIDIA-backed converter references consume prepared upstream roots and executables from that manifest before falling back to direct legacy discovery. When PHYSICAL_AI_REQUIRE_PREFLIGHT=1 is set, missing converter readiness blocks at the preflight guardrail.
The selected converter backend must be installed and reachable before conversion runs.
CAD routes require the NVIDIA Omniverse usd-convert-cad checkout, omniverse-kit, supported CAD core extensions, and CAD Converter licensing on supported architectures. On Linux arm64 only, CAD routes use the usd-convert-cad reference's Kit App Template CAD Converter fallback.
First CAD runs need network access to download Kit extensions from the NVIDIA registry.

Routing

Do not classify NVIDIA-backed inputs from a router-owned extension table. Existing USD files are detected locally, then the router queries converter references in priority order:

Probe source	Route
URDF reference probe	`urdf-usd-converter`
MuJoCo reference probe, including XML `<mujoco>` root inspection	`mujoco-usd-converter`
Upstream `usd-convert-gsplat` CLI source inspected by the `usd-convert-gsplat` reference	`usd-convert-gsplat`
Upstream `usd-convert-cad` `src/usd_convert_cad/formats.py` inspected by the `usd-convert-cad` reference; on Linux arm64, after dedicated references such as URDF and MuJoCo decline the source, the Kit App Template CAD Converter fallback reports support and lets the installed Kit runtime determine whether conversion can succeed	`usd-convert-cad`; NVIDIA-backed source conversion delegates to upstream `usd-convert-cad` on supported architectures and to the Kit App Template CAD Converter fallback on Linux arm64 only
Existing OpenUSD layer or package signature	Skip conversion and route to `validate-usd-minimum`

If more than one converter reference reports support, the router selects by converter-reference priority and records a warning. If no reference reports support, return an unsupported report rather than guessing.

For ambiguous mesh-like suffixes such as STL, rely on upstream converter capability from each converter reference probe instead of hard-coding a local route in the router.

USD Exchange Backing

Most conversion routes eventually hand off to a usdex consumer:

Conversion path	Underlying consumer
`urdf-usd-converter`	`urdf_usd_converter`, direct `usdex.core`
`mujoco-usd-converter`	`mujoco_usd_converter`, direct `usdex.core`
`usd-convert-cad` JT route	`omni.kit.converter.jt_core`, linked with `libusdex_core` and `libusdex_rtx`
`usd-convert-cad` DGN route	`omni.kit.converter.dgn_core`, linked with `libusdex_core` and `libusdex_rtx`
`usd-convert-cad` HOOPS route	`omni.kit.converter.hoops_core`, linked with `libusdex_core` and `libusdex_rtx`

usd-convert-asset is intentionally not an active route until a public PyPI package is available. Downstream validation still follows the OpenUSD Exchange SDK 2.3 stage and metadata contract where applicable; use the OpenUSD Exchange SDK / usdex reference for repo authoring rules and omni-asset-validate for the hub validation wrapper.

Instructions

Locate the source asset and relevant sidecar files.
Check whether the input is already OpenUSD.
Identify required asset roots such as mesh folders, texture folders, ROS package roots, or MJCF asset directories.
Run each converter reference probe and select the first supported reference in router priority order.
Confirm the installed reference script and selected converter dependency are available before running conversion.
For CAD inputs, let the usd-convert-cad reference run upstream validate.py as a delegated readiness gate before it invokes upstream conversion. On Linux arm64, let that reference start the Kit App Template CAD Converter fallback. Do not import or start either runtime directly from this router.
Convert to a dedicated output directory with convert-to-usd, not next to source files unless the user requested that location.
Record a conversion report with source, converter, output, warnings, and next validation step.
Hand off the generated USD artifact to validate-usd-minimum.

CLI Pattern

Prefer the installed reference-local script instead of assembling ad hoc commands:

python3 scripts/run.py /path/to/source_asset /path/to/output_dir --report /path/to/conversion_report.json

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

python3 /path/to/skills/omniverse-cad-to-simready/references/convert-to-usd/scripts/run.py /path/to/source_asset /path/to/output_dir --report /path/to/conversion_report.json

Check router dependencies with:

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

Output Format

Every conversion path should produce or report:

Field	Meaning
`source_asset_path`	Original input file or directory.
`source_format`	Detected format, such as `urdf`, `mjcf`, `gsplat`, `cad`, or `usd`.
`converter_reference`	Reference selected for conversion.
`converter_tool`	Library, CLI, or application used by the converter reference.
`output_directory`	Directory containing the generated USD artifact and sidecar output.
`output_usd_path`	Primary USD layer or package generated by conversion.
`sidecar_inputs`	Meshes, textures, package roots, or extra files needed to convert.
`warnings`	Non-fatal issues, assumptions, and missing optional information.
`errors`	Blocking failures or unsupported features.
`next_step`	Usually `validate-usd-minimum`.

Markdown is acceptable for the first report. JSON can be added later when schemas exist.

Scripts

Script	Purpose	Usage
`scripts/run.py`	Query converter reference probes, route to the selected reference, and produce a normalized conversion report.	Execute: `python3 scripts/run.py <source_asset> <output_dir> --report <conversion_report.json>`. Calls external converters via subprocess (network on first CAD run).
`scripts/check_dependencies.py`	Verify the converter dependencies referenced by `run.py` are reachable.	Execute: `python3 scripts/check_dependencies.py --report <dependency_report.json>`. Read-only; no network.
`scripts/report_schema.json`	JSON Schema for the conversion report shape.	Reference: read for expected report structure.

Limitations

This router does not perform detailed format-specific conversion when a specialized converter reference exists.
This router does not own NVIDIA-backed source-extension tables. Update the upstream converter reference or upstream repo when format capability changes.
NVIDIA-backed source conversion must delegate to the usd-convert-cad reference. That reference uses upstream usd-convert-cad except for its Linux arm64 Kit App Template CAD Converter fallback. Do not switch to any other converter or substitute tooling when the selected runtime is unavailable.
Ambiguous source types are only routed when a converter reference probe reports support.

Troubleshooting

Symptom	Cause	Fix
NVIDIA-backed conversion is blocked	`USD_CONVERT_CAD_ROOT`, the upstream checkout, Python 3.12, `omniverse-kit`, the required converter extension, registry access, platform support, or converter licensing is unavailable; on Linux arm64, `KIT_APP_TEMPLATE_ROOT`, the Kit App Template build, Kit executable, or CAD Converter extension is unavailable	Return a `blocked` conversion report with the specific readiness or conversion dependency. Do not switch to another converter or substitute tooling. NVIDIA-backed source conversion must delegate to the `usd-convert-cad` reference.
Kit registry access is denied	Upstream `usd-convert-cad validate.py` cannot pull its Kit extensions from the extension registry/CDN	Return the structured `kit_registry_access_denied` diagnostic, including the extension, URL host, exit code, and recovery hint. Fix Horde node egress, proxy, or credentials, or pre-populate/reuse the upstream Kit extension cache, then rerun `OMNI_KIT_ACCEPT_EULA=yes python validate.py` in the upstream checkout.
A source routes to an unexpected converter	More than one reference reported support, or an upstream capability source changed	Inspect the report warnings. The router records the priority-selected reference and warns when multiple probes report support.
First CAD conversion runs slowly	Kit downloads converter extensions from the registry on first run	Expected on first run only. Subsequent runs use the cached extensions.

Unsupported Cases

Do not pretend a conversion succeeded if a converter is unavailable or cannot parse the source. Produce a clear blocked report that includes:

detected format
missing converter or dependency
source files inspected
expected converter reference
recommended next action

NVIDIA-backed source conversion must delegate to the usd-convert-cad reference, including source types listed by upstream src/usd_convert_cad/formats.py. The upstream route uses converter extensions such as omni.kit.converter.hoops_core, omni.kit.converter.dgn_core, and omni.kit.converter.jt_core. On Linux arm64 only, when the router reaches the usd-convert-cad reference after higher-priority dedicated references decline the source, that CAD reference uses the Kit App Template CAD Converter fallback and lets the installed Kit runtime decide whether the input can be converted. If USD_CONVERT_CAD_ROOT, the upstream checkout setup, Python 3.12, omniverse-kit, the required converter extension, platform support, licensing, Kit App Template fallback runtime, or conversion support is unavailable, return a blocked conversion report rather than switching to any other converter or substitute tooling. The higher-level router must not override converter capability with its own source-extension list.

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/README.md

Convert to USD

When to Use

Prerequisites

Routing

USD Exchange Backing

Instructions

CLI Pattern

Output Format

Scripts

Limitations

Troubleshooting

Unsupported Cases

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