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Omniverse Cad To Simready

NVIDIAOfficialCreated by OmniverseDistributed by NVIDIA

  • 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

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as an orchestrator for the CAD to SimReady pipeline, coordinating a series of specialized stages including asset conversion, material and physics assignment via Content Agents, validation, and final packaging. It is designed to manage the flow between discrete tools rather than replacing them, ensuring that assets meet specific simulation requirements through a structured, documentation-driven process.

What it does

  • Coordinates end-to-end conversion from source CAD or mesh assets to SimReady USD.
  • Manages property assignment for materials and physics using Content Agents.
  • Performs multi-stage validation gates to ensure asset viability and conformance.
  • Automates the assembly of deliverable packages including thumbnails and USD dependencies.
  • Provides consolidated reporting on workflow status, validation findings, and stage outputs.
  • Supports flexible execution modes including conversion-only and validation-only paths.

When to use it

  • When you need to transform raw CAD or source assets into simulation-ready USD files.
  • When you require automated material and physics assignment for 3D assets.
  • When you need to validate assets against SimReady conformance profiles.
  • When you are preparing assets for packaging and distribution in a simulation environment.

Advantages

  • Provides a structured, repeatable pipeline for complex asset preparation.
  • Integrates multiple specialized stages into a cohesive workflow.
  • Ensures consistent validation and conformance across the asset lifecycle.
  • Supports modular execution by allowing specific stages to be skipped or isolated.

Usage examples

“Convert my CAD file at /path/to/model.step to a SimReady asset.”
The agent will initiate the preflight check, convert the file to USD, apply material and physics properties, validate the result, and output a report.
“Validate this existing USD file for SimReady conformance.”
The agent will run the validation-only workflow, checking the provided USD against SimReady profiles and reporting any findings.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/omniverse-cad-to-simready/download
  2. 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 for an end-to-end pipeline from a source asset to a SimReady asset or package. It coordinates existing conversion, authoring, validation, conformance, rendering, and packaging references directly; do not replace it with a single monolithic runner command.

This skill is documentation-driven and does not ship scripts/run.py; it must not depend on a repository checkout. Shell is declared because this workflow invokes installed stage reference scripts directly, from each reference's installed directory, and it still must not grow a monolithic runner.

Prerequisites

  • Prefer preflight first for deterministic setup: it installs/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).

  • A Content Agents model provider key when local deployment will run, or explicit endpoint variables plus usage tokens for already-running endpoints; see references/preflight/README.md for the full list.

  • Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and OVRTX stages.

  • Local upstream checkouts under ${OMNIVERSE_CAD_TO_SIMREADY_UPSTREAM_ROOT:-$HOME/.omniverse-cad-to-simready/upstreams} when a stage needs upstream scripts or specs.

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. For conversion-only requests, 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. For validation-only requests, set property_assignment_intent=skip and validate the USD the user provided without rerunning conversion.

Run preflight (or verify an existing PHYSICAL_AI_PREFLIGHT_MANIFEST) before any converter, validation, Content Agents, OVRTX, packaging, or FET step; treat it as dependency bootstrap, not workflow routing. Use --skip-content-agents for conversion-only/validation-only requests.

When property_assignment_intent=run, verify or deploy Content Agents services immediately after confirming the source path and resolving intent, before asset-context inspection, converter dependency checks, conversion, validation, conformance, rendering, packaging, or upstream source builds. Treat explicitly provided healthy endpoints as user-owned; otherwise run deploy-content-agents, which deploys the shared standalone OVRTX renderer, then Material, Physics, and optional Texture service containers in order.

Instructions

  1. Confirm the source asset path exists, resolve output_root, and classify the request as end-to-end, conversion-only, validation-only, or packaging.

  2. Resolve property_assignment_intent before running any asset inspection, converter probe, conversion, validation, conformance, rendering, or packaging step.

  3. 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.

  4. Verify or deploy Content Agents services first when property_assignment_intent=run; block on missing authentication or unhealthy services instead of continuing.

  5. Read references/workflow.md and references/commands.md, then run only the stage references needed for the current request.

  6. Run identify-asset-context on the original source asset when web search is available or property assignment will run.

  7. Route the source through convert-to-usd, or skip conversion for existing USD input and treat the source path as the current USD path.

  8. 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.

  9. Run Content Agents material, physics, and optional texture assignment on the converted/minimum-valid USD when requested or required.

  10. Run simready-conform-profile on the latest simulation USD path after property assignment and preserve every selected FET repair report.

  11. Run validation gates in order: omni-asset-validate, omni-asset-validate-geometry, omni-asset-validate-physics, and simready-validate.

  12. Rerun simready-conform-profile when simready-validate reports a repairable requirement, then rerun profile validation on the newest authored USD.

  13. 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.

  14. Emit the consolidated workflow report with the final USD path, all stage reports, validation findings, rerun reasons, and next work.

Output Format

Emit a consolidated workflow report in Markdown, and include JSON when the workflow writes structured artifacts. Report overall status as passed, blocked, failed, or needs_rerun. See references/workflow.md for the required Markdown and JSON report fields.

Detailed References

Read only the references needed for the current request:

  • references/preflight/README.md: deterministic local setup, manifest/env contract, wrappers, deployment opt-out, and guardrail behavior.

  • references/workflow.md: inputs, source routing, detailed workflow, validation policy, output report fields, and next steps.

  • references/commands.md: concrete portable script command patterns.

  • references/assemble-package-source/README.md: two-zone package source assembly, root USD naming, thumbnail placement, and deliverable checks.

  • references/troubleshooting.md: symptom/cause/fix table plus FET (GSP.001/RB.MB.001) repair-routing detail.

  • references/publishing-layout.md: frontmatter compatibility-field notes and layout rationale for this skill's own file tree.

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 the nested references/ tree is intentional. See references/publishing-layout.md for the alias list, frontmatter field placement, and flattening rules.

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.

Troubleshooting

Read references/troubleshooting.md only when a specific stage or validation gate is failing; it owns the symptom/cause/fix table and FET repair routing.

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/simready-conform-profile/references/FET_000_CORE/scripts/run.py

#!/usr/bin/env python3
# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

from __future__ import annotations

import argparse
import json
from pathlib import Path
import shutil
import sys
from typing import Any

sys.path.insert(0, str(Path(__file__).resolve().parents[5] / "shared"))

from script_utils import check_result as _check, emit_json_report, resolve_output_path


SKILL = "FET_000_CORE"
SIMREADY_METADATA_LAYER_KEY = "SimReady_Metadata"
SUPPORTED_USD_EXTENSIONS = {".usd", ".usda", ".usdc", ".usdz"}
ROOT_LAYER_EXTENSIONS = {".usd", ".usda", ".usdc"}
AUTHORING_TOOL = "pxr.Usd rootLayer.Save"


def _load_extra_metadata(metadata_json: Path | None) -> dict[str, Any]:
    if metadata_json is None:
        return {}
    payload = json.loads(metadata_json.read_text(encoding="utf-8"))
    if not isinstance(payload, dict):
        raise ValueError(f"{metadata_json} must contain a JSON object")
    return payload


def _build_metadata(
    *,
    asset_path: Path,
    output_path: Path,
    identifier: str | None,
    version: str,
    description: str | None,
    profile: str,
    profile_version: str,
    source_asset: str | None,
    generated_by: str,
    author: str | None,
    tags: list[str],
    pipeline_steps: list[str],
    extra_metadata: dict[str, Any],
) -> dict[str, Any]:
    resolved_identifier = identifier or output_path.stem
    metadata: dict[str, Any] = {
        "identifier": resolved_identifier,
        "version": version,
        "description": description or f"SimReady metadata for {resolved_identifier}",
        "profile": profile,
        "profile_version": profile_version,
        "source_asset": source_asset or asset_path.name,
        "generated_by": generated_by,
        "pipeline": pipeline_steps or [SKILL],
    }
    if author:
        metadata["author"] = author
    if tags:
        metadata["tags"] = tags
    metadata.update(extra_metadata)
    return metadata


def _custom_layer_metadata(metadata: dict[str, Any]) -> dict[str, str]:
    layer_metadata: dict[str, str] = {}
    for key, value in metadata.items():
        if value is None:
            continue
        if isinstance(value, (dict, list, tuple)):
            layer_metadata[key] = json.dumps(value, sort_keys=True)
        else:
            layer_metadata[key] = str(value)
    return layer_metadata


def _report(
    *,
    asset_path: Path,
    output_path: Path | None,
    operation: str,
    metadata: dict[str, Any],
    custom_layer_written: bool,
    sidecar_path: Path | None,
    checks: list[dict[str, Any]],
    warnings: list[str],
    next_step: str,
) -> dict[str, Any]:
    errors = [check["message"] for check in checks if check["severity"] == "error" and not check["passed"]]
    requirements_repaired = ["NP.006"] if not errors and (custom_layer_written or sidecar_path is not None) else []
    return {
        "asset_path": str(asset_path),
        "skill": SKILL,
        "tool": AUTHORING_TOOL,
        "passed": not errors,
        "status": "PASS" if not errors else "FAIL",
        "operation": operation,
        "output_usd_path": str(output_path) if output_path is not None else None,
        "requirements_repaired": requirements_repaired,
        "metadata": metadata,
        "custom_layer_key": SIMREADY_METADATA_LAYER_KEY,
        "custom_layer_written": custom_layer_written,
        "sidecar_json_path": str(sidecar_path) if sidecar_path is not None else None,
        "checks": checks,
        "warnings": warnings,
        "errors": errors,
        "next_step": next_step,
    }


def apply_metadata(args: argparse.Namespace) -> dict[str, Any]:
    asset_path = args.asset_path.resolve()
    output_path = resolve_output_path(
        asset_path,
        args.output,
        args.output_dir,
        args.in_place,
        default_stem_suffix="_simready",
    ).resolve()
    operation = "in_place" if args.in_place else "copy_and_apply_metadata"
    checks: list[dict[str, Any]] = []
    warnings: list[str] = []
    metadata: dict[str, Any] = {}
    custom_layer_written = False
    sidecar_path: Path | None = None

    exists = asset_path.exists()
    checks.append(_check("asset_exists", exists, "Asset path exists" if exists else "Asset path does not exist"))
    supported_suffix = asset_path.suffix.lower() in SUPPORTED_USD_EXTENSIONS
    checks.append(_check("supported_usd_extension", supported_suffix, "Asset uses a supported USD extension" if supported_suffix else "Asset must be .usd, .usda, .usdc, or .usdz"))
    if args.in_place and (args.output is not None or args.output_dir is not None):
        checks.append(_check("output_mode_valid", False, "Use either --in-place or an output path, not both"))
    elif args.output is not None and args.output_dir is not None:
        checks.append(_check("output_mode_valid", False, "Use either --output or --output-dir, not both"))
    elif not args.in_place and output_path == asset_path:
        checks.append(_check("output_mode_valid", False, "Output path matches input path; use --in-place to edit the source asset"))
    else:
        checks.append(_check("output_mode_valid", True, "Output mode is valid"))
    if any(check["severity"] == "error" and not check["passed"] for check in checks):
        return _report(asset_path=asset_path, output_path=output_path, operation=operation, metadata=metadata, custom_layer_written=False, sidecar_path=None, checks=checks, warnings=warnings, next_step=args.next_step)

    try:
        extra_metadata = _load_extra_metadata(args.metadata_json)
    except (OSError, json.JSONDecodeError, ValueError) as exc:
        checks.append(_check("metadata_json_valid", False, f"Metadata JSON is invalid: {exc}"))
        return _report(asset_path=asset_path, output_path=output_path, operation=operation, metadata=metadata, custom_layer_written=False, sidecar_path=None, checks=checks, warnings=warnings, next_step=args.next_step)
    checks.append(_check("metadata_json_valid", True, "Metadata JSON is valid" if args.metadata_json else "No metadata JSON override provided", "info"))

    metadata = _build_metadata(
        asset_path=asset_path,
        output_path=output_path,
        identifier=args.identifier,
        version=args.version,
        description=args.description,
        profile=args.profile,
        profile_version=args.profile_version,
        source_asset=args.source_asset,
        generated_by=args.generated_by,
        author=args.author,
        tags=args.tags,
        pipeline_steps=args.pipeline_steps,
        extra_metadata=extra_metadata,
    )
    if not args.no_sidecar:
        sidecar_path = (args.sidecar_json or output_path.with_suffix(".json")).resolve()
        if sidecar_path.exists() and not args.force:
            checks.append(_check("sidecar_available", False, f"Sidecar JSON already exists: {sidecar_path}"))
            return _report(asset_path=asset_path, output_path=output_path, operation=operation, metadata=metadata, custom_layer_written=False, sidecar_path=None, checks=checks, warnings=warnings, next_step=args.next_step)
    if not args.in_place:
        if output_path.exists() and not args.force:
            checks.append(_check("output_available", False, f"Output path already exists: {output_path}"))
            return _report(asset_path=asset_path, output_path=output_path, operation=operation, metadata=metadata, custom_layer_written=False, sidecar_path=sidecar_path, checks=checks, warnings=warnings, next_step=args.next_step)
        output_path.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy2(asset_path, output_path)
        checks.append(_check("output_prepared", True, f"Copied source asset to {output_path}", "info"))
    else:
        checks.append(_check("output_prepared", True, "Editing source asset in place", "info"))

    if output_path.suffix.lower() in ROOT_LAYER_EXTENSIONS:
        try:
            from pxr import Usd
            stage = Usd.Stage.Open(str(output_path))
        except Exception as exc:
            stage = None
            warnings.append(f"OpenUSD stage open raised {type(exc).__name__}: {exc}")
        checks.append(_check("stage_opens", stage is not None, "Stage opens" if stage is not None else "Stage cannot be opened"))
        if stage is not None:
            root_layer = stage.GetRootLayer()
            custom_layer_data = dict(root_layer.customLayerData)
            custom_layer_data[SIMREADY_METADATA_LAYER_KEY] = _custom_layer_metadata(metadata)
            root_layer.customLayerData = custom_layer_data
            custom_layer_written = bool(root_layer.Save())
            checks.append(_check("custom_layer_written", custom_layer_written, f"Authored root layer customLayerData[{SIMREADY_METADATA_LAYER_KEY!r}]" if custom_layer_written else "Failed to save root layer metadata"))
    else:
        warnings.append("USDZ root layers are not edited; metadata is written as sidecar JSON only")
        checks.append(_check("custom_layer_written", True, "Skipped root layer metadata for USDZ sidecar-only mode", "warning"))

    if sidecar_path is not None:
        sidecar_path.parent.mkdir(parents=True, exist_ok=True)
        sidecar_path.write_text(json.dumps(metadata, indent=2, sort_keys=True) + "\n", encoding="utf-8")
        checks.append(_check("sidecar_written", True, f"Wrote sidecar metadata to {sidecar_path}", "info"))
    else:
        checks.append(_check("sidecar_written", True, "Sidecar metadata disabled", "info"))
    warnings.append("Grasp vectors are not authored by this FET000 Core metadata script; handle GSP.001 with FET_005_SIMULATE_GRASP_PHYSICS.")
    return _report(asset_path=asset_path, output_path=output_path, operation=operation, metadata=metadata, custom_layer_written=custom_layer_written, sidecar_path=sidecar_path, checks=checks, warnings=warnings, next_step=args.next_step)


def emit(payload: dict[str, Any], report_path: Path | None, markdown_report_path: Path | None) -> None:
    emit_json_report(
        payload,
        report_path,
        markdown_report_path,
        f"# FET000 Core Metadata Repair Report\n\n- Passed: `{payload['passed']}`",
    )


def main(argv: list[str] | None = None) -> int:
    parser = argparse.ArgumentParser(description="Author SimReady Core metadata onto a USD asset.")
    parser.add_argument("asset_path", type=Path)
    output_group = parser.add_mutually_exclusive_group()
    output_group.add_argument("--output", type=Path)
    output_group.add_argument("--output-dir", type=Path)
    output_group.add_argument("--in-place", action="store_true")
    parser.add_argument("--force", action="store_true")
    parser.add_argument("--identifier")
    parser.add_argument("--version", default="1.0.0")
    parser.add_argument("--description")
    parser.add_argument("--profile", default="Prop-Robotics-Neutral")
    parser.add_argument("--profile-version", default="1.0.0")
    parser.add_argument("--source-asset")
    parser.add_argument("--generated-by", default="omniverse-cad-to-simready")
    parser.add_argument("--author")
    parser.add_argument("--tag", dest="tags", action="append", default=[])
    parser.add_argument("--pipeline-step", dest="pipeline_steps", action="append", default=[])
    parser.add_argument("--metadata-json", type=Path)
    parser.add_argument("--sidecar-json", type=Path)
    parser.add_argument("--no-sidecar", action="store_true")
    parser.add_argument("--next-step", default="simready-validate")
    parser.add_argument("--report", type=Path)
    parser.add_argument("--markdown-report", type=Path)
    args = parser.parse_args(argv)
    payload = apply_metadata(args)
    emit(payload, args.report, args.markdown_report)
    return 0 if payload["passed"] else 1


if __name__ == "__main__":
    raise SystemExit(main())

Requirements and compatibility

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: Declares allowed tools, Needs Python

The author says: Orchestrator skill. Managed Content Agents deployment requires a configured model provider key matching the selected backend, such as NVIDIA_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or GEMINI_API_KEY, Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse/content-agents at the ref pinned in upstream-versions.lock.json. 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
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/omniverse-cad-to-simready · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

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