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://mp-staging.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/preflight/README.md

CAD to SimReady Preflight

When to Use

Use this reference before an omniverse-cad-to-simready workflow when the host should have deterministic local dependencies instead of each downstream reference discovering upstream checkouts independently. It prepares local upstream checkouts, validates runtime entrypoints, optionally verifies or deploys Content Agents, and writes a manifest that downstream references can consume.

This reference is a setup and readiness contract. It is not a monolithic CAD-to-SimReady workflow runner and it does not run conversion, property assignment, conformance, validation, rendering, or packaging on an asset.

Prerequisites

Python 3.12.
uv when a repository pyproject.toml is available and the project Python environment should be synchronized.
git, and git-lfs when LFS fixtures or source assets must be materialized.
Network and repository access for the upstream sources listed below.
Docker, Docker Compose v2, NVIDIA Container Toolkit, an NVIDIA driver, an NVIDIA GPU, and NVIDIA_API_KEY when managed local Content Agents deployment is requested.

Windows hosts can use the same Python preflight script and PowerShell wrapper for checkout and Python-runtime preparation. Managed Content Agents deployment requires a Linux Docker/GPU host; on Windows use WSL2/Linux Docker or provide healthy service endpoints.

Upstream Sources

The preflight installs or verifies local checkouts under ${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME/.physical-ai-skill-hub/upstreams} unless a per-upstream override is set.

Area	Upstream	Default checkout / override
CAD conversion	`https://github.com/NVIDIA-Omniverse/usd-convert-cad`	`usd-convert-cad`, `USD_CONVERT_CAD_ROOT`
Gaussian splat conversion	`https://github.com/NVIDIA-Omniverse/usd-convert-gsplat`	`usd-convert-gsplat`, `USD_CONVERT_GSPLAT_ROOT`
SimReady validation and FET skills	`https://github.com/NVIDIA/simready-foundation` on branch `main`	`simready-foundation`, `SIMREADY_FOUNDATION_ROOT`
Content Agents services	`https://github.com/nvidia-omniverse/content-agents` on branch `main`	`content-agents`, `CONTENT_AGENTS_UPSTREAM_ROOT`

The upstream URLs remain documented because they are the source of truth for external NVIDIA technology. Operationally, downstream references should prefer the preflight manifest when it is present.

CLI Pattern

Linux/macOS:

.agents/skills/omniverse-cad-to-simready/references/preflight/scripts/preflight.sh \
  --env-file "$HOME/.physical-ai-skill-hub/state/cad-to-simready-preflight.env" \
  --markdown-report "$HOME/.physical-ai-skill-hub/state/cad-to-simready-preflight.md"

. "$HOME/.physical-ai-skill-hub/state/cad-to-simready-preflight.env"

Windows PowerShell:

.\.agents\skills\omniverse-cad-to-simready\references\preflight\scripts\preflight.ps1 `
  --powershell-env-file "$HOME\.physical-ai-skill-hub\state\cad-to-simready-preflight.ps1" `
  --markdown-report "$HOME\.physical-ai-skill-hub\state\cad-to-simready-preflight.md"

. "$HOME\.physical-ai-skill-hub\state\cad-to-simready-preflight.ps1"

Dependency bootstrap without Content Agents service deployment:

python3 .agents/skills/omniverse-cad-to-simready/references/preflight/scripts/preflight.py \
  --skip-content-agents \
  --env-file "$HOME/.physical-ai-skill-hub/state/cad-to-simready-preflight.env"

Read-only readiness check:

python3 .agents/skills/omniverse-cad-to-simready/references/preflight/scripts/preflight.py \
  --check-only \
  --skip-deploy

Manifest Contract

The default manifest path is:

${PHYSICAL_AI_SKILL_HUB_STATE:-$HOME/.physical-ai-skill-hub/state}/cad-to-simready-preflight.json

Set PHYSICAL_AI_PREFLIGHT_MANIFEST to point downstream references at a specific manifest. Set PHYSICAL_AI_REQUIRE_PREFLIGHT=1 to make downstream references block instead of falling back to legacy direct discovery when the manifest is missing or the required component is not ready.

The generated env file exports:

PHYSICAL_AI_PREFLIGHT_MANIFEST
PHYSICAL_AI_REQUIRE_PREFLIGHT=1
PHYSICAL_AI_SKILL_HUB_HOME
PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT
PATH with the repository .venv/bin prepended when the project virtual environment is present, so direct reference scripts can find bundled CLIs such as urdf_usd_converter
per-upstream root variables such as USD_CONVERT_CAD_ROOT
prepared runtime variables such as PHYSICAL_AI_SIMREADY_VALIDATE_VENV
ready service endpoints such as CONTENT_AGENTS_MATERIAL_AGENT_BASE_URL, CONTENT_AGENTS_PHYSICS_AGENT_BASE_URL, and RENDER_ENDPOINT

Use --env-file for POSIX shells and --powershell-env-file for PowerShell.

The manifest never writes API keys, bearer tokens, or file-backed secret contents. Command output is redacted before it is included in the report.

Content Agents Policy

Content Agents readiness is included by default. Preflight first reuses healthy existing CONTENT_AGENTS_*_BASE_URL and renderer endpoints. Material, Physics, and Texture endpoints must also report configured API keys when their health payload includes api_keys_configured; a healthy container without service credentials is not workflow-ready. If endpoints are not ready and deployment is enabled, preflight checks Docker/GPU/auth prerequisites with nvidia-smi, the Docker daemon, Docker Compose v2, and NVIDIA_API_KEY, then invokes executable upstream deployment entrypoints only when the content-agents checkout publishes them. The upstream collection helper starts agent services; when the shared OVRTX endpoint is still unhealthy after that step, preflight invokes the upstream standalone OVRTX Docker Compose entrypoint from apps/ovrtx_rendering_api/docker-compose.yml and waits for the host /health endpoint. For managed local deployment, known Content Agents credential environment variables such as NVIDIA_API_KEY are mirrored into the upstream checkout's private .env file with owner-only permissions so Docker Compose can pass them to containers. Those values are not written to the preflight manifest or generated downstream env file. When NGC_API_KEY is absent, the managed local deployment mirrors NVIDIA_API_KEY into that name inside the upstream .env because the upstream collection's local render endpoint is reached from containers through a Docker host alias.

For remote or NVCF-style endpoints, preflight records the provided endpoint as ready without treating generic unauthenticated /health failures as blockers. The selected service wrapper still performs the authenticated service call and reports any real request failure.

Do not encode service-specific Docker Compose files, image names, ports inside containers, or deployment runbooks in this repo. If the selected upstream checkout exposes only documentation-driven deployment skills, preflight reports Content Agents as blocked and points the user back to the upstream deployment skills or to provided healthy endpoints.

Use --skip-content-agents only when Content Agents are explicitly out of scope, such as conversion-only, validation-only, or no material/physics assignment. Use --skip-deploy when endpoints should be verified but services must not be started.

Preflight can reduce dependency checks to the requested workflow target and source route. Use --targets conversion, --targets validation, or --targets conversion,validation,content-agents to choose workflow areas. Use --source-asset /path/to/input.urdf or --source-format urdf to infer the conversion route, --output-root /path/to/output to verify the output directory is writable or creatable, or pass --conversion-tools repo-python,usd-convert-cad for an explicit converter set. URDF and MuJoCo/MJCF routes require only the repo Python conversion tools; CAD and mesh routes require usd-convert-cad; Gaussian splat routes require usd-convert-gsplat. Validation targets also gate OpenUSD Python APIs (pxr.Usd, pxr.UsdGeom, and pxr.UsdPhysics) and the upstream Asset Validator runtime (omni_asset_validate or omni.asset_validator) before SimReady validation. On Linux aarch64, if the SimReady Foundation requirements cannot resolve PyPI usd-core, preflight retries the SimReady validation runtime with usd-exchange>=2.3.0, omniverse-asset-validator, omniverse-usd-profiles, non-usd-core Foundation requirements, and simready-validate installed without dependencies.

If uv is missing, the repo_python runtime entry includes an install hint: curl -LsSf https://astral.sh/uv/install.sh | sh.

Output Format

The JSON report includes:

overall status: ready or blocked
selected targets
selected conversion tools and route-selection reason
request input readiness for the source asset and output root
normalized paths for home, state, upstream, venv, project, and output roots
upstream checkout path, URL, branch, commit, and status
runtime readiness for repo Python, Git LFS, converters, OpenUSD Python APIs, Asset Validator, SimReady validation, and Content Agents
Content Agents local deployment host diagnostics for nvidia-smi, Docker daemon access, and Docker Compose v2 when local deployment may be needed
service readiness for OVRTX, Material, Physics, and optional Texture
non-secret downstream environment exports
command steps with redacted output tails
blocker messages

The Markdown report summarizes the same status for humans.

Pass/Fail Policy

Return success only when every selected target is ready or explicitly skipped. Report blocked when a selected runtime, checkout, CLI, service endpoint, or deployment prerequisite is missing. Do not scan broad developer workspaces or reuse arbitrary old clones.

Next Steps

After preflight succeeds, source the generated env file, then run the normal atomic references in the omniverse-cad-to-simready workflow. Downstream references will consume the manifest and prepared local paths/endpoints before trying direct legacy discovery.

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

2026-11-08

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/preflight/README.md

CAD to SimReady Preflight

When to Use

Prerequisites

Upstream Sources

CLI Pattern

Manifest Contract

Content Agents Policy

Output Format

Pass/Fail Policy

Next Steps

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