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Omniverse Usd Performance Tuning

OpenAIOfficialCreated by NVIDIA OmniverseDistributed by OpenAI

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Medium risk
  • Has scripts
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From the author

Top-level workflow skill for USD performance diagnosis and optimization. Use for slow loading, high memory, low FPS, or 'optimize my scene' requests; delegates auth/runtime setup to Phase 0 owners.
— the description by NVIDIA Omniverse

AutoGineer summary

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

The omniverse-usd-performance-tuning skill provides a structured, end-to-end procedure for addressing performance bottlenecks in USD assets. It manages the lifecycle of performance tuning, from initial runtime context setup and baseline profiling to composition auditing, validation, and targeted scene optimization. The skill enforces a rigorous milestone-based approach, ensuring that all destructive operations are planned and approved, and that performance improvements are verified through comparative profiling and standardized reporting.

What it does

  • Orchestrates the end-to-end USD performance tuning lifecycle.
  • Validates runtime environments and handles authentication for remote assets.
  • Conducts composition audits and structural assessments of USD stages.
  • Executes validated optimization operations using Scene Optimizer.
  • Generates structured optimization reports comparing baseline and post-optimization metrics.
  • Enforces safety gates for destructive operations and mutation.

When to use it

  • When a USD scene exhibits slow loading times or low frame rates.
  • When memory usage is excessively high or GPU crashes occur.
  • When you need to optimize a scene for better interactivity or performance.
  • When you need to diagnose CAD conversion quality issues.
  • When you need to validate a scene before and after applying optimizations.

Advantages

  • Ensures consistent performance diagnosis through a standardized workflow.
  • Prevents accidental data loss by requiring approval for destructive operations.
  • Provides clear, evidence-based reporting on performance improvements.
  • Integrates seamlessly with existing Omniverse validation and optimization tools.
  • Maintains strict separation between diagnosis, planning, and execution phases.

Usage examples

“This USD scene loads very slowly; please triage what I should check first.”
The agent will initiate the runtime context gate, perform a composition audit, and provide a structured plan to identify the source of the slow load time.
“Optimize my CAD scene for better performance.”
The agent will verify the runtime, assess the CAD conversion quality, and propose a milestone-based optimization plan including validation and profiling.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/openai/omniverse-usd-performance-tuning/download

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

    mkdir -p .agents/skills && unzip omniverse-usd-performance-tuning.zip -d .agents/skills

Documentation by NVIDIA Omniverse

Omniverse USD Performance Tuning

<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->

<!-- SPDX-License-Identifier: Apache-2.0 -->

When to Use

Use this workflow for broad performance asks such as slow loading, high memory, low FPS, GPU crashes, conversion-quality triage, or generic requests to optimize a USD scene.

Instructions

  1. Start from the mandatory runtime context gate before producing tuning output, unless the prompt is only asking for a static classification test.

  2. Classify broad optimization requests as ready_to_plan; reserve approval_required for prompts that explicitly name a destructive operation to execute before planning.

  3. Plan the full canonical chain through optimization-report, preserving the structured milestone order and the profile-stage:baseline / profile-stage:after labels when listing milestones.

  4. Invoke downstream skill bodies only when their phase is reached, and keep raw runtime artifacts on disk while reading compact summaries.

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

Output Format

Return a plan or status summary that names the selected entry skill, uses ready_to_plan for generic optimization requests, includes the full milestone chain through optimization-report, and labels profile phases as profile-stage:baseline and profile-stage:after. For structured outputs, the broad-optimization milestone subsequence is omniverse-usd-performance-tuning -> profile-stage:baseline -> usd-structure-assessment -> usd-validation-runner -> restructure-decision -> apply-restructure -> so-run-validators -> so-interpret-validators -> so-run-operations -> profile-stage:after -> compare-profiles -> optimization-report. End-to-end execution should produce an optimized stage when mutation runs and a report conforming to the optimization-report reference's schema (scripts/optimization-report.schema.json within that reference).

Use this workflow for broad performance asks such as slow loading, low FPS, high memory, GPU crashes, conversion quality, or "optimize my scene."

Entry skill rule

This skill is the named entry point for broad performance work whenever the agent has any verified way to do that work. Runtime probing details live in setup-usd-performance-tuning; this rule only decides which skill owns the user-facing performance request.

  • If the setup probe shows any verified runtime path - Kit, standalone, or even a partial stack such as Asset Validator only - enter here. If the user's requested tool is missing, return the specific blocked_code (blocked_missing_scene_optimizer, blocked_missing_so_operation, etc.) instead of substituting another workflow.

  • Enter at setup-usd-performance-tuning only when no runtime path is verified and runtime choice/setup is the first unresolved problem.

  • For omniverse:// assets, enter at omniverse-authentication first. Authentication precedes setup and triage for remote assets.

The decision is about ownership, not order. Setup, authentication, and triage all run in their normal phase order; this rule only fixes which skill the agent names as the entry skill in its response.

Runtime context — session-start gate (mandatory)

Before any other tuning output, follow the mandatory session-start gate in skills/omniverse-usd-performance-tuning/references/setup-usd-performance-tuning/references/runtime-context-header.md. That reference owns output_path, the canonical setup-preflight.json location, Format A/Format B, and the "do not improvise a silent probe" anti-pattern.

Required outcomes:

  • Missing or unreadable preflight: invoke setup-usd-performance-tuning.

  • Present preflight: print Format A and wait for the user to choose Continue, Change Kit, Switch to standalone, or Re-run probe.

  • Confirmed runtime in the same session: use compact Format B for follow-up status.

[Kit: {runtime_context.kit.application} {runtime_context.kit.version}  |  SO: {runtime_context.sceneOptimizer.version}  |  AV: {runtime_context.assetValidator.version}]

Runtime artifact token budget

Before reading Kit logs, Asset Validator CSVs, Scene Optimizer logs, Tracy CSVs, or other runtime output, follow references/runtime-artifact-token-budget.md. Keep raw artifacts on disk, read summary JSON first, and use bounded log snapshots instead of full dumps or live streams.

Plan-time vs execution-time approval

approval_required at planning time is reserved for requests that explicitly name a destructive operation. Use the following rule when deciding between ready_to_plan and approval_required:

  • approval_required at planning time — the user's request itself names a destructive operation: "flatten this stage", "decimate the meshes", "merge prototypes", "delete unused prims", or any specific named mutation that cannot be undone within the same workflow. In this case the agent's first response must be an approval prompt that names the operation, before the agent commits to a plan that executes it.

  • ready_to_plan at planning time — the user's request is general: "optimize this scene", "make it load faster", "reduce GPU memory", "improve interactivity". The agent lays out the full plan, including any destructive operations the plan would invoke (for example so-run-operations with mergeMaterials), without withholding the plan itself. Approval for each destructive operation is requested alongside plan approval.

The distinction is between authorising a plan and authorising a destructive action. A general optimisation request authorises planning; it does not authorise execution of specific destructive operations.

For structured runtime-test responses and similar planning summaries:

  • A future restructure-decision prompt is a planned user-decision gate, not a reason to set the top-level response decision to approval_required for a generic optimization request.

  • For a generic optimization request, set decision: "ready_to_plan" and include the full intended chain in both committed_milestones and planned_phases, through optimization-report.

  • It is valid for gates_observed to include asks_user_for_restructure_decision while the top-level decision remains ready_to_plan.

  • Whenever a chain names profile phases, use the exact labels profile-stage:baseline and profile-stage:after; do not emit the ambiguous bare profile-stage token.

  • Start structured milestone lists with omniverse-usd-performance-tuning as the owning entry skill. Include setup-usd-performance-tuning only as additional Phase 0 context, not as a replacement for the entry skill milestone.

  • For broad optimization requests, preserve the milestone subsequence from Output Format above exactly, with optional extra analysis steps inserted only where they do not reorder it.

  • Do not list so-run-validators or so-interpret-validators before restructure-decision in broad optimization milestone summaries. Phase-aware validator routing still happens through usd-validation-runner; the SO validator executor/interpreter milestones appear after the restructure decision path in the structured plan contract.

Output expectation

End-to-end optimization work should produce both an optimized USD stage, when mutation is executed, and a structured optimization report conforming to the optimization-report reference's scripts/optimization-report.schema.json. The HTML report must be rendered from references/report-templates/optimization-report.html.template via render_preview.py — never hand-write HTML. Diagnosis-only work should still end with a report or summary that states no optimized stage was written.

Purpose

Route digital twin USD performance requests into the right diagnostic and optimization workflow while preserving evidence before mutation.

Prerequisites

  • Stage path or enough context to identify the target asset.

  • User goal: diagnosis only, validation, profiling, or processor execution.

  • Runtime availability status from setup-usd-performance-tuning when not already known.

  • Permission status for in-place mutation vs writing a separate optimized output.

Examples

  • "This USD loads slowly; triage what to check first."

  • "Route a low-FPS CAD scene through the performance workflow."

Triage order

  1. Runtime gate. Follow the mandatory session-start gate above before validation, profiling, or optimization. Do not scan, probe, install, or pick Kit/standalone runtimes directly in this skill; setup-usd-performance-tuning owns probe/chooser/install dispatch and writes the preflight consumed here.

  2. Identify the target problem:

    • Load time.

    • FPS or interactivity.

    • GPU or system memory.

    • Crash or device lost.

    • CAD conversion quality.

    • Validation failure.

  3. Gather minimum context:

    • Stage path and size.

    • Whether the stage is local, mounted, or omniverse:// remote. For remote assets, route through omniverse-authentication before first open.

    • Kit or USD runtime.

    • Whether the workload is CAD, VFI, AIF, Isaac, or generic OpenUSD.

    • Whether in-place mutation is allowed.

    • Whether the user wants diagnosis only or processor execution.

  4. Route:

    • USD composition questions: usd-structure-assessment (composition is now part of the SA umbrella; deeper detail in skills/omniverse-usd-performance-tuning/references/usd-structure-assessment/references/composition-audit.md).

    • Validation and content issues: usd-validation-runner (master router; routes to validate-* family or so-run-validators based on intent).

    • Edit/output decisions: usd-edit-target-planner (also owns variant/payload gates).

    • Repeated copied hierarchy or high mesh count with no instancing: usd-hierarchy-dedupe-candidates.

    • Restructure decision (monolithic stage, asset boundary materialization): restructure-decision.

    • CAD converter settings: read references/cad-conversion/README.md (niche pre-USD concern; see reference for details).

    • Scene Optimizer: so-run-validators, so-interpret-validators, so-run-operations.

Optimization ordering

Follow the canonical ordering in workflow.md § Operation ordering invariants. The high-level rule: prototypes first → per-asset validation → stage-level operations last. The workflow reference owns the full invariant list (meshCleanup before decimateMeshes, deduplication before decimation, never merge if instanced, etc.) and the analysis-only ops catalogue.

Rules

  • Always run composition audit before mutation.

  • Always validate before and after processor execution.

  • Optimize prototypes before per-asset validation.

  • Do not run whole-stage mesh deduplication on very large CAD scenes before checking for hierarchy-level reuse.

  • Do not recommend a fixed optimization stack without bottleneck evidence.

  • Do not invent numeric thresholds or expected percentage wins.

  • Prefer canonical SO ops over specialty / documentary ones. The op curation in references/operations/_curation.json classifies every op as canonical, specialty, analysis, documentary, or deprecated. When more than one op could resolve the same finding, recommend the canonical one first and only reach for a specialty op when the user explicitly asks or the rationale warrants it. Specifically:

    • For vertex welding, prefer canonical meshCleanup with explicit flags over the standalone mergeVertices op. The standalone op is a legacy/specialty surface; use upstream usd-optimize for the operation mechanics and local approval policy before mutating.

    • For hierarchy dedupe, recommend usd-hierarchy-dedupe-candidates + apply-restructure (the USD-authored rewrite path).

    • For per-mesh dedupe, recommend deduplicateGeometry (canonical) over findCoincidingGeometry (analysis — produces a report, not a change).

    • Do not recommend documentary-status ops (e.g., boxClip, deletePrims, removeAttributes, removeUntypedPrims, merge outside its narrow non-instanced case) without an explicit user request. Documentary ops survive in the per-op references/operations/<key>.md routing stubs for completeness but are excluded from agent-initiated recommendations.

    • Specialty ≠ documentary. Ops classified as specialty in _curation.json either (a) have validator-finding evidence that wires them into the so-interpret-validators chain (e.g. sparseMeshes, optimizePrimvars), or (b) are load-bearing escape hatches needed for specific downstream contexts (e.g. primitivesToMeshes when output must be UsdGeomMesh, utilityFunction for instancing toggles and material rebinding, pythonScript for so-create-proxy recipes). Recommend specialty ops when their validator fires OR when their downstream context applies — the suppression above only targets documentary ops.

Limitations

  • Does not replace downstream reference instructions; load each required reference before executing it.

  • Does not install runtimes directly; follow setup or install references when requirements are missing.

  • Does not authorize mutation when the user has not allowed writes.

Troubleshooting

  • If runtime status is unclear, run setup-usd-performance-tuning before profiling or validation.

  • If the reported problem is vague, gather stage path, workload type, and whether diagnosis or execution is requested.

  • If the workflow suggests mutation before evidence, return to baseline profiling and composition audit first.

References

Before routing, read:

  • skills/omniverse-usd-performance-tuning/references/usd-structure-assessment/references/optimization-tradeoffs.md — identify which pipeline phase the scene is in (extraction, structuring, or optimization). The right action depends on the phase.

  • skills/omniverse-usd-performance-tuning/references/usd-structure-assessment/references/factory-level-structuring.md — understand the three pillars (assets, aggregation, animation) and the seven-step structuring pattern.

If you have network access, prefer the live URLs (noted in each reference file) for the most current version.

Required execution flow

Read references/workflow.md for the canonical Phase 0-7 flow, including Kit/standalone branches, validator-stack routing, operation ordering, termination conditions, duration hints, and the optional iteration pattern. The compact root map at references/skill-map.md only routes agents into this workflow.

Do not treat downstream phase names as plain checklist labels. Before executing each step, load that phase's nested README.md reference and follow its instructions. Claude Code only exposes the public catalog skill; it does not recursively inject profile-stage, usd-structure-assessment, or other nested references.

The final deliverable must come from optimization-report: save both the structured JSON report and the generated Markdown summary. Do not substitute an ad hoc SUMMARY.md or chat-only recap for the optimization report.

For deeper subtopic guidance, consult the references:

  • skills/omniverse-usd-performance-tuning/references/usd-structure-assessment/references/composition-audit.md, skills/omniverse-usd-performance-tuning/references/usd-structure-assessment/references/layer-health.md - subtopic detail for SA's Phase 1 checklist.

  • skills/omniverse-usd-performance-tuning/references/usd-structure-assessment/references/instancing-readiness/references/instancing-tradeoffs.md - merge safety, decision tree for instancing choices.

  • skills/omniverse-usd-performance-tuning/references/usd-structure-assessment/references/usd-edit-target-planner/references/variants-payloads.md - deeper variant/payload trade-offs (gates are inline in usd-edit-target-planner).

  • references/cad-conversion/README.md - CAD converter settings.

  • references/upstreams/usd-optimize.md - upstream SO mechanics and prebuilt package resolution.

  • skills/omniverse-usd-performance-tuning/references/usd-validation-runner/references/so-run-validators/references/infrastructure.md - local handoff for SO validator infrastructure.

  • skills/omniverse-usd-performance-tuning/references/usd-validation-runner/references/validation-scoping.md - tier 1/2/3 plan + scene-aware adjustment.

  • skills/omniverse-usd-performance-tuning/references/optimization-report/references/optimization-report-template.md - the data contract every phase populates.

For full Kit runtime profiling (FPS, frame time, Hydra/RTX metrics), refer to the external profiling skills at NVIDIA/omniperf.

Files

references/setup-usd-performance-tuning/references/install-so-standalone/README.md

Install Scene Optimizer Standalone

<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->

<!-- SPDX-License-Identifier: Apache-2.0 -->

When to Use

Use when SO core operations or packaged SO validator rules are needed outside Kit.

Instructions

  1. Confirm the target asset, artifact, or user intent and check the prerequisites listed below.

  2. Read only the referenced files needed for the current phase, failure mode, or output contract.

  3. Follow the workflow, rules, and safety gates in this reference before invoking downstream references or shell commands.

  4. Return the result using the Output Format section and name any blocked prerequisite or unresolved user decision.

Output Format

Return a concise status or report that names the input, selected runtime or evidence source, actions planned or performed, artifacts written, blockers, and the next validation or user-decision step. When a schema or template is referenced below, conform to that contract.

Purpose

PyPI wheel isn't released yet; this reference consumes a prebuilt scene_optimizer_core_...release.zip package. Do not clone the Scene Optimizer source repo, run repo.sh, or depend on repo helper wrappers for standalone runtime setup. The package is ~350-380 MB; download + extract takes ~1-2 min on a fast connection. EULA env var not needed (no Kit).

Use this reference for standalone Scene Optimizer core operations and the packaged omni.scene.optimizer.validators rules when a Kit runtime is unavailable or not desired. For validator execution, pair this package with a project-managed omniverse-asset-validator environment that can import the same SO package. Kit remains useful when automatic extension registration or render-time profiling is needed.

Prerequisites

Python 3.12 is a HARD requirement. The drop ships cp312-only wheels. There is no abi3, no cp310/cp311/cp313 fallback, and no source build path here. Installing under any other Python will appear to succeed until the first import omni.scene.optimizer.core, which fails with a cryptic ABI error. Verify python3.12 --version before downloading the ~330 MB zip.

python3.12 --version            # required — package is cp312-only, no fallback
command -v unzip                # preferred extractor on Linux (Windows: Expand-Archive)

If either is missing, install before continuing (apt-get install python3.12 unzip on Debian/Ubuntu; on systems without a 3.12 package, uv python install 3.12 is also fine but see the uv-managed Python note in Step 4).

Step 2 — Pick Archive or Extracted Root by Platform

Use a user-provided package archive path, direct archive URL, or extracted package root when supplied. Do not clone the source repository. If an extracted package root is supplied and it has the sentinel paths listed under Package Version, set SO_HOME and SCENE_OPTIMIZER_PACKAGE_ROOT to that root and skip the download/extract steps.

Current public direct archive URLs:

  • Linux x86_64: https://d4i3qtqj3r0z5.cloudfront.net/scene_optimizer_core_usd_25.11_py_3.12%40110.1.0%2Bmaster.401.324ccecb.gl.manylinux_2_35_x86_64.release.zip

  • Windows x86_64: https://d4i3qtqj3r0z5.cloudfront.net/scene_optimizer_core_usd_25.11_py_3.12%40110.1.0%2Bmaster.401.324ccecb.gl.windows-x86_64.release.zip

For direct archive URLs, @ -> %40 and + -> %2B. Auto-pick by uname -s/-m.

Step 3 — Pick install location

Ask the user to choose:

  • Per-user (default): ~/scene-optimizer/ — shared across projects, downloaded once. Same literal on Linux/Windows shells.

  • Project-local: $(pwd)/packages/scene-optimizer/ — isolated to this CWD.

Step 4 — Download, extract, configure

Use this step only for a direct archive path or URL.

export SO_PACKAGE=<direct archive path or URL>
export SO_HOME=<chosen path>
mkdir -p "$SO_HOME"
case "$SO_PACKAGE" in
  http://*|https://*) curl -L "$SO_PACKAGE" -o "$SO_HOME/scene_optimizer_core.zip" ;;
  *) cp "$SO_PACKAGE" "$SO_HOME/scene_optimizer_core.zip" ;;
esac
cd "$SO_HOME"
python3.12 - <<'PY'
import zipfile

archive = "scene_optimizer_core.zip"
if not zipfile.is_zipfile(archive):
    raise SystemExit(
        f"{archive} is not a zip archive; set SO_PACKAGE to a direct .zip "
        "archive path or URL and retry"
    )
PY
unzip -q scene_optimizer_core.zip

cat > "$SO_HOME/activate.sh" <<'EOF'
export SO_HOME="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export SCENE_OPTIMIZER_PACKAGE_ROOT="$SO_HOME"
export PYTHONPATH="$SO_HOME/python:$SO_HOME/usdpy:$PYTHONPATH"
export LD_LIBRARY_PATH="$SO_HOME/lib:$SO_HOME/extraLibs:$LD_LIBRARY_PATH"

# uv-managed Python 3.12 ships libpython3.12.so.1.0 outside the system
# loader path. Prepend the chosen interpreter's lib dir so SO's C++
# extensions can dlopen it. No-op when the interpreter is a system Python.
_so_pylib="$(python3.12 -c 'import sys, os; print(os.path.join(sys.base_prefix, "lib"))' 2>/dev/null)"
if [ -n "$_so_pylib" ] && [ -d "$_so_pylib" ]; then
    export LD_LIBRARY_PATH="$_so_pylib:$LD_LIBRARY_PATH"
fi
unset _so_pylib
EOF
source "$SO_HOME/activate.sh"

Env vars are session-scoped. Re-source $SO_HOME/activate.sh in any new shell.

uv-managed Python 3.12. When python3.12 was installed via uv python install 3.12, libpython3.12.so.1.0 lives under ~/.local/share/uv/python/cpython-3.12.*/lib/ and is not on the default loader path. Without the snippet above, the first SO import fails with ImportError: libpython3.12.so.1.0: cannot open shared object file. The _so_pylib block in activate.sh derives the right directory from sys.base_prefix so it works for both uv-managed and system Pythons.

On Windows: write activate.bat instead, using set SCENE_OPTIMIZER_PACKAGE_ROOT=%SO_HOME% and set PATH=%SO_HOME%\lib;%SO_HOME%\extraLibs;%PATH% (no LD_LIBRARY_PATH). Windows resolves python312.dll through the launcher that started the process, so the uv-managed-Python caveat above does not apply.

Step 5 — Verify

python3.12 - <<'PY'
def operation_count():
    try:
        from omni.scene.optimizer.core.bindings._omni_scene_optimizer_core import acquire_interface

        iface = acquire_interface()
        if hasattr(iface, "get_operations"):
            return "bindings.acquire_interface", len(iface.get_operations())
    except Exception:
        pass

    import omni.scene.optimizer.core as soc

    core = soc.SceneOptimizerCore.getInstance()
    return "SceneOptimizerCore.getInstance", len(core.getOperations())

surface, count = operation_count()
print(f"{surface}: {count} operations")
PY

Expect >= 40 (the exact count varies by build). Standalone drops do not all expose the same Python API: SO 110.0.2-style environments may require acquire_interface().get_operations() / acquire_interface().execute_operation(op, context, json_string), while other builds expose SceneOptimizerCore.getInstance().getOperations() / executeOperation. Operation scripts must probe the selected runtime before choosing an API surface.

Limitations

The standalone package supports analysis-mode operations — set ExecutionContext.analysisMode = 1 to get per-operation findings without the full validator engine.

The drop may include a bundled validator-venv/. Do not use it as the default runtime — it may lack numpy and is slower on large stages. Use a project-managed venv with install-asset-validator-standalone instead.

SO Validator Auto-Registration

The standalone SO package includes omni.scene.optimizer.validators — 25 Python validator rules (mesh density, unused UVs, primitive fit, etc.) that use @register_rule decorators. When OAV and the SO package share the same Python environment, importing the validators auto-registers them:

import omni.scene.optimizer.validators  # triggers @register_rule decorators

from omni.asset_validator import CategoryRuleRegistry
registry = CategoryRuleRegistry()
# Now includes "Usd:Performance" and "Omni:Geometry" categories

No register_all() call is needed for rule discovery. The @register_rule("Usd:Performance") decorator on each checker class handles registration at import time.

To run only SO performance rules (excluding base OAV rules):

import omni.scene.optimizer.validators  # auto-registers
from omni.asset_validator import ValidationEngine, CategoryRuleRegistry
from pxr import Usd

registry = CategoryRuleRegistry()
so_rules = [
    rule
    for category in ("Usd:Performance", "Omni:Geometry")
    for rule in registry.get_rules(category)
]

engine = ValidationEngine(init_rules=False)
for rule in so_rules:
    engine.enable_rule(rule)

stage = Usd.Stage.Open("path/to/asset.usd")
results = engine.validate(stage)

The standalone import is from omni.asset_validator import ValidationEngine (no .core). The .core submodule only exists inside a running Kit session.

Package Version

Current expected package family (Kit 110.1 parity):

[email protected]+master.401.324ccecb.gl.<platform>.release.zip

Expected layout after unpack:

$SO_HOME/
├── .agents/     # Operation guides and SO skills packaged for agents
├── python/      # Python modules (omni.scene.optimizer.*)
├── usdpy/       # USD Python bindings (pxr.*)
├── lib/         # Core shared libraries
├── extraLibs/   # Additional dependencies
└── docs/        # Prebuilt package install notes

Sentinel check (all runtime dirs plus agent docs must exist for a valid install):

for sub in .agents python lib extraLibs usdpy; do
    [[ -d "$SO_HOME/$sub" ]] || echo "MISSING: $sub"
done
[[ -f "$SO_HOME/.agents/operations/INDEX.md" ]] || echo "MISSING: .agents/operations/INDEX.md"

Environment for Docker/CI

Set WU_SO_PACKAGE_DIR to point tools at the local backend:

export WU_SO_PACKAGE_DIR="$SO_HOME"
export SCENE_OPTIMIZER_PACKAGE_ROOT="$SO_HOME"

If absent, downstream tools may fall back to NVCF cloud backend or fail.

Troubleshooting

  • If omni.scene.optimizer.core cannot be imported, confirm Python 3.12 is running and $SO_HOME/activate.sh has been sourced in the current shell.

  • ImportError: libpython3.12.so.1.0: cannot open shared object file → the active python3.12 is uv-managed (or otherwise installed outside the system loader path) and $SO_HOME/activate.sh was not re-sourced after a fresh shell or after the uv install. The activate script prepends $(python3.12 -c 'import sys, os; print(os.path.join(sys.base_prefix, "lib"))') to LD_LIBRARY_PATH; re-source it. If the import still fails, run python3.12 -c 'import sys; print(sys.base_prefix)' manually and confirm a lib/libpython3.12.so.1.0 exists under that prefix.

  • If library loading fails on Linux, verify $SO_HOME/lib and $SO_HOME/extraLibs are present in LD_LIBRARY_PATH.

  • If the install looks incomplete, run the sentinel check above and redownload when any required directory is missing.

  • If downstream tools use a cloud backend or fail to find the package, set WU_SO_PACKAGE_DIR="$SO_HOME" in the same environment.

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 Python

The author says: Orchestrator skill. Downstream phases may require Kit, Scene Optimizer, Asset Validator, USD Python, writable output paths, and omniverse:// authentication selected by setup-usd-performance-tuning.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network
  • 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
NVIDIA Omniverse
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/nvidia/skills/omniverse-usd-performance-tuning · commit d420d13
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download omniverse-usd-performance-tuning.zip