Omniverse Usd Performance Tuning

NVIDIAOfficialCreated by NVIDIA Omniverse

Skill
Workflow
Software development
Apache-2.0
Medium risk
Has scripts

From the author

Diagnose and optimize USD stages for slow loading, high memory, low FPS or validation; preserve asset boundaries and reuse shared definitions.

— the description by NVIDIA Omniverse

AutoGineer summary

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

This skill provides a structured workflow for diagnosing and optimizing USD stages. It focuses on improving performance metrics like load times, memory footprint, and frame rates by identifying and optimizing unique asset definitions, reusing shared definitions, and pruning unnecessary data. The process emphasizes preserving original asset boundaries, animation, and interface requirements, ensuring that downstream applications remain functional after optimization.

What it does

Inspects USD stages to identify performance bottlenecks and structural inefficiencies.
Classifies assets into atomic, package, or aggregate structures to guide optimization.
Performs native USD optimization operations on disjoint scopes.
Publishes semantic libraries to enable efficient asset reuse.
Validates composition, ownership, and interface accessibility of the optimized output.
Profiles the final output against the original baseline to report measured improvements.

When to use it

When a USD stage suffers from slow loading times or high memory consumption.
When rendering performance (FPS) needs improvement in a production environment.
Before finalizing a USD assembly for downstream consumption.
When you need to standardize asset structures according to factory-level guidelines.

Advantages

Maintains asset boundaries and critical metadata during optimization.
Reduces scene complexity through intelligent reuse of shared definitions.
Provides verifiable reports on performance gains and composition integrity.
Supports large-scale scene optimization by processing independent definition scopes.

Usage examples

“Inspect the USD stage at /data/factory.usdc for performance issues and prepare a review workspace.”: The agent initializes the workspace, runs the inspection script, and provides a summary of candidates for optimization.
“Submit the optimization decisions for the factory stage based on the generated JSON.”: The agent processes the boundary decisions, executes the optimization operations, and generates the final optimized USD assembly.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/omniverse-usd-performance-tuning/download

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

Purpose

Improve loading, memory use or rendering for the requested workload. Preserve the prim paths, appearance, placement and behavior required by downstream applications. Follow the component/subcomponent model in NVIDIA's Factory-Level USD Structuring guide. Apply the relevant parts of that model; a static asset does not need new animation or physics.

Inputs

Required: the source USD stage. When no workload is specified, use factory layout planning and visualization and record that assumption in the request/report. Explicit workload and preservation requirements take precedence. Optional: an output workspace, preservation requirements, physical error budget, existing library indexes and recorded runtime evidence. Use values from the current user request; reuse recorded values only while they remain applicable. Follow setup when no output path is supplied.

Prerequisites

Verify the selected runtime and required capabilities using setup. Keep its preflight record with the run; installation and remote access are conditional steps described there.

Instructions

Inspect. Establish the workload, preservation requirements and output workspace. Set up progress and dashboard access before assessment. Verify setup and inspect the selected source configuration.
Judge boundaries. Review equipment, service-system sections and architectural elements in the selected configuration, including animation and interiors. Record owners, editable interfaces, equipment categories and optional families using boundary guidance.
Classify and author. Classify accepted assets children before parents. Capture or reuse the original profile before authoring. Prepare reusable definitions, materials and occurrence opinions using asset authoring.
Optimize unique definitions. Run supported native operations concurrently on disjoint scopes with the recorded physical budget.
Publish semantic libraries. Use one writer per category layer and remap consumer references.
Check composition. Check authored ownership, references, bindings, placement, animation and interface accessibility through the written-output checks.
Profile and report. Compare the final consumer with the original baseline under comparable conditions and report measured benefits, completion and operations by asset definition.

sequenceDiagram
    participant A as Agent
    participant T as USD tools
    participant W as Definition workers
    participant L as Library publisher
    participant D as Read-only progress dashboard
    Note over A,L: One compact progress state also renders text progress
    T-->>D: Recorded phase, assessment rounds, counters and decision reasons
    A->>T: Inspect original selected configuration
    T->>T: Collect cached child-to-parent summaries and repetition nominations
    T->>T: Load supplied indexes once and batch authored product clues
    T-->>A: Product review groups, member context, animation and reuse evidence
    loop Until boundaries and interiors are reviewed
        A->>A: Select asset boundaries, then identify required internal prims and properties
        A->>T: Submit boundary decisions with reviewed scope, required edits, category and optional family
        T->>T: Reuse unchanged source evidence, recompute boundary-dependent results
        opt Geometry ownership requires reconstruction
            T->>T: Regroup or native split, remap paths and reassess affected regions
        end
        T-->>A: Remaining candidates and member exceptions
    end
    T->>T: Classify selected child assets before parent assets, confirm reuse
    T->>T: Capture or reuse original baseline profile
    T->>T: Confirm accepted product identities from source and supplied indexes; reconcile complete parent-child groups
    T->>T: Skip worker inputs for confirmed groups; author remaining materials, editable prims and occurrence overrides
    T->>T: Determine merge groups on prepared interiors; preserve selected child assets and required prims
    T->>W: Read-only input USD layers for existing-library matching
    par Independent preparation jobs
        W->>W: Confirm remaining product, exact source or authorized normalized reuse; preserve occurrence placement
        W->>W: Consolidate accepted product or compatible exact/normalized leaf inputs; retain every occurrence
    end
    W-->>T: Confirmed library hits and definitions requiring optimization
    T->>W: Owned geometry prim paths in read-only input layers
    par Independent definition scopes
        W->>W: Remove small meshes when authorized; apply asset representation: reserve merge inputs or fit analytic prims; reduce, merge (native clustering if required), prune, flatten, prune
    end
    W->>L: Geometry outputs and assembly authoring outcomes
    L->>L: Share eligible static whole definitions within current category publication
    L->>L: Share remaining whole meshes; write USD reuse metadata and compact index
    L->>T: Output USD stage with rewritten references and dependencies
    T->>T: Check authored composition, ownership, animation and interfaces
    T->>T: Profile final output USD stage and report results by asset definition
    T-->>A: Measured changes, coverage and unresolved work
    T-->>D: Final verdict and report/library paths

Use the provided tools for traversal, native operations, authoring and checks; the agent supplies engineering judgments. Preserve current variant selections, authored animation, required materials and interfaces. Trust supported USD Optimize operations with valid settings and scopes. Deep geometric comparisons and whole-scene edit trials belong in development tests, not ordinary execution.

Complete when every unique definition has an executed or evidenced unchanged outcome, every asset has justified ownership and structure, and the written consumer passes composition checks. Retain native failures, unresolved judgments and missing required capabilities as incomplete work. Repeat only affected work when evidence changes.

Scope and authorization

Diagnosis and validation requests remain read-only. An optimization request authorizes work in a separate output workspace within its stated preservation limits.
Never overwrite source layers or inherited libraries without explicit authorization.
Reuse authorization already given. Ask only for a missing decision that changes identity, required appearance or a loss budget; explain the affected component and consequence.
Report only observed changes. Measure rendering before claiming higher FPS or lower GPU use.

Load only the relevant guidance

Request	Read
Validation only	Setup, then the validation section of operations
Approved operation chain	Setup, operations, verification
Existing or persistent asset library	Add the library index requirements to the assessment
Missing standalone runtime	Installation
Remote stage	Remote access before the first open
Report from completed evidence	Verification

Use upstream package guidance for selected operation mechanics and proxy workflows. Keep large logs on disk as specified in setup.

Limitations

Convert CAD input with the source converter's workflow and NVIDIA's CAD conversion guidance. Render profiling requires NVIDIA/omniperf or the user's available profiler; USD open and traversal timings alone do not establish rendering improvements.

Troubleshooting

For unavailable imports, operations or remote assets, follow setup. For incomplete execution or failed composition checks, follow verification; retain the failed artifacts and report the missing capability or failed check.

Examples

After runtime preflight, use the verified Python interpreter from setup to inspect a stage:

python scripts/successor_assessment.py inspect --source /data/factory.usdc --workspace /data/factory-review

Review the generated candidates using boundary guidance, fill the generated decision template, then submit it:

python scripts/successor_assessment.py decide --workspace /data/factory-review --decisions /data/factory-review/decisions.json

These commands perform assessment; continue through the instructions above only when review is complete. Run from the skill directory; generated data stays in the workspace.

Output Format

Maintain live progress and editable steering throughout execution.

For end-to-end optimization, provide the written USD assembly, validated report JSON, and generated Markdown and HTML reports. State the runtime, operations actually executed, preservation and coverage results, measured changes, source-write status, and artifact paths. Diagnosis-only work may return a findings summary stating that no optimized stage was written.

Files

ATTRIBUTION.mdAdded by AutoGineer964 B
BENCHMARK.md12.0 KiB
CHANGELOG.md2.4 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md10.2 KiB
agents/openai.yaml375 B
assets/progress-dashboard/app.jsScript9.8 KiB
assets/progress-dashboard/index.html2.4 KiB
assets/progress-dashboard/styles.css3.8 KiB
config/skillspector-baseline.yaml7.3 KiB
evals/README.md1.8 KiB
evals/evals.json12.4 KiB
evals/files/completed-run/after.usda399 B
evals/files/completed-run/before.usda399 B
evals/files/completed-run/evidence.json745 B
evals/files/factory/cases/factory.usda13.6 KiB
evals/files/factory/materials.usda1.8 KiB
references/asset-authoring.md10.1 KiB
references/component_scope.pyScript18.0 KiB
references/geometry_tolerance.pyScript2.1 KiB
references/install-standalone.md2.4 KiB
references/library-reuse.md7.6 KiB
references/operations.md22.4 KiB
references/operations/README.md7.9 KiB
references/operations/operations.json52.4 KiB
references/operations/registry-args.json2.1 KiB
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references/remote-assets.md1.6 KiB
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references/report-templates/optimization-report.md.template6.2 KiB
references/report-templates/render_preview.pyScript17.9 KiB
references/report-templates/theme.css711 B
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references/setup-usd-performance-tuning/scripts/setup-preflight.schema.json6.4 KiB
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references/structure.md20.8 KiB
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scripts/animation_context.pyScript6.8 KiB
scripts/composition_verification.pyScript16.9 KiB
scripts/failure_reporting.pyScript2.7 KiB
scripts/product_clues.pyScript6.2 KiB
scripts/progress_dashboard.pyScript12.6 KiB
scripts/stage_profile.pyScript15.7 KiB
scripts/successor_assessment.pyScript34.1 KiB
scripts/successor_execution.pyScript15.7 KiB
scripts/successor_progress.pyScript22.3 KiB
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scripts/successor_workflow.pyScript55.0 KiB
scripts/usd_tuning.pyScript24.7 KiB
skill-card.md5.5 KiB
skill.oms.sig26.5 KiB

evals/evals.json

{
  "skill_name": "omniverse-usd-performance-tuning",
  "evals": [
    {
      "id": "usd-performance-broad-optimization-flow",
      "prompt": "Optimize /workspace/input/factory/cases/factory.usda for factory layout loading, preserving animated equipment links. No USD runtime is installed; don't install one. Write your plan to /workspace/output/plan.md.",
      "expected_output": "The missing runtime is recorded as a blocker in /workspace/output/setup-preflight.json without installing anything, the source stays unchanged, and plan.md is based on a read-only reading of the stage: the repeated robots and clamps are reuse candidates, the editable robot links and the animated clamp jaw stay independently editable, and the optimized output, composition checks and before/after profile are planned for /workspace/output. No operation or improvement is claimed.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Reads the canonical skill and setup.md.",
        "Records the missing runtime in setup-preflight.json and does not install software.",
        "Leaves the source stage unchanged.",
        "Identifies the repeated robots and clamps as reuse candidates and keeps the editable links and animated jaw independently editable.",
        "Plans a separate output with composition checks and a before/after profile.",
        "Does not claim that any operation ran or that anything improved."
      ],
      "files": [
        "evals/files/factory/"
      ]
    },
    {
      "id": "usd-performance-runtime-preflight-gate",
      "prompt": "Optimize /workspace/input/factory/cases/factory.usda into /workspace/output. No setup-preflight.json or runtime probe exists yet, and installing software is not authorized.",
      "expected_output": "Read setup.md, probe the available interpreter and package imports, and save /workspace/output/setup-preflight.json recording that USD Optimize is unavailable (blocked_missing_usd_optimize) with the observed import failures. Nothing is installed and no optimization is claimed; any further work is read-only.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Reads the canonical skill and setup.md.",
        "Probes the interpreter and package imports instead of assuming a runtime.",
        "Writes setup-preflight.json recording the missing capabilities before any operation.",
        "Does not install software or claim that optimization ran."
      ],
      "files": [
        "evals/files/factory/"
      ]
    },
    {
      "id": "usd-performance-destructive-op-approval",
      "prompt": "Flatten /workspace/input/factory/cases/factory.usda into one layer and decimate high-poly meshes for a Kit viewer, writing to /workspace/output. No USD runtime is installed; don't install one. Write your plan to /workspace/output/plan.md.",
      "expected_output": "plan.md treats flattening and decimation as already authorized and does not ask for that permission again. It asks one question for the missing decimation error budget, in the stage's units (metres), instead of choosing a reduction rate, and plans a separate output in /workspace/output while the source stays unchanged.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Records the explicitly requested flattening and decimation intent and does not request the same authorization again.",
        "Asks for the missing decimation error budget in stage units rather than inventing a reduction rate.",
        "Plans a separate output and leaves the source unchanged.",
        "Records the missing runtime instead of claiming execution."
      ],
      "files": [
        "evals/files/factory/"
      ]
    },
    {
      "id": "usd-performance-expensive-validation-approval",
      "prompt": "Optimize /workspace/input/factory/cases/factory.usda into /workspace/output. Occlusion and cross-component duplicate checks may be useful. No USD runtime is installed; don't install one. Write your plan to /workspace/output/plan.md.",
      "expected_output": "plan.md scopes occlusion and cross-component duplicate checks to flagged candidates (non-transparent containment pairs, nominated duplicate regions) with bounded timeouts instead of whole-stage sweeps, and records any check it cannot run as not performed. Occlusion is detection only: deleting occluded geometry needs the user's explicit purpose. The plan ends in an optimization-report deliverable, and the missing runtime is recorded as a blocker.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Reads the canonical skill and the validation scope guidance in its operations reference.",
        "Scopes occlusion and duplicate checks to flagged candidates instead of whole-stage sweeps.",
        "Treats occlusion as detection and does not plan deletion without the user's explicit purpose.",
        "Records checks that cannot run as not performed rather than dropping them.",
        "Keeps the plan anchored on an optimization-report deliverable."
      ],
      "files": [
        "evals/files/factory/"
      ]
    },
    {
      "id": "usd-performance-viewer-build-distractor-negative",
      "prompt": "Which workflow would you use to build a browser-based RTX USD viewer with camera controls, object picking, a stage tree and render settings? Select the appropriate skill if available; do not implement the application.",
      "expected_output": "This is an application/viewer construction request, not a USD performance diagnosis or optimization workflow.",
      "expected_skill": null,
      "assertions": [
        "Does not select omniverse-usd-performance-tuning.",
        "Does not run profiling, validation, Usd Optimize operations, or optimization-report steps.",
        "Routes to a viewer or application-building skill if one is available."
      ],
      "files": []
    },
    {
      "id": "usd-performance-structural-only-report",
      "prompt": "Usd Optimize and OpenUSD are not installed here, and I don't want anything installed. Assess /workspace/input/factory/cases/factory.usda anyway and write a structural-only report to /workspace/output.",
      "expected_output": "The workflow continues in structural-only mode from a read-only reading of the stage: no mesh operations and no USD validation run, and the report says so. The report JSON passes validate_report.py with verdict neutral, sets workflow_mode to structural_only and records the Usd Optimize blocker in its notes, without inventing a structural-only verdict value.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Reads the canonical skill and the verification reference.",
        "Continues in structural-only mode instead of halting, installing software or fabricating results.",
        "Runs no mesh operations and states that USD validation could not run.",
        "Produces a report that passes validate_report.py with verdict in improved|neutral|regressed|mixed and workflow_mode structural_only.",
        "Records the Usd Optimize blocker in the report notes."
      ],
      "files": [
        "evals/files/factory/"
      ]
    },
    {
      "id": "usd-performance-report-schema-conformance",
      "prompt": "Finish the report for the completed run in /workspace/input/completed-run/evidence.json. The before and after stages are alongside it. Write final JSON, Markdown and HTML to /workspace/output. Do not rerun optimization or invent missing measurements.",
      "expected_output": "Report JSON written from the evidence conforms to optimization-report.schema.json, with verdict neutral because the before and after metrics are identical, and the unmeasured fields left unmeasured. It passes references/optimization-report/scripts/validate_report.py, and the Markdown and HTML are generated from the same JSON with references/report-templates/render_preview.py using --fixture and --output, not by hand.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Reads the canonical skill and the verification reference.",
        "Writes report JSON against optimization-report.schema.json with verdict in improved|neutral|regressed|mixed.",
        "Validates the finished report with validate_report.py before finishing.",
        "Generates the Markdown and HTML with render_preview.py using --fixture and --output, not by hand.",
        "Does not invent measurements or verdict values such as structural-only or no-op-needed."
      ],
      "files": [
        "evals/files/completed-run/"
      ]
    },
    {
      "id": "usd-performance-no-overwrite-source",
      "prompt": "Run mesh cleanup and decimation on /workspace/input/factory/cases/factory.usda to make it lighter, writing to /workspace/output. No USD runtime is installed; don't install one. Write your plan to /workspace/output/plan.md.",
      "expected_output": "plan.md reuses the explicit cleanup and decimation authorization and asks only for the missing decimation error budget. It writes the optimized stage to a new file in /workspace/output, never the source path, and plans to record the output path and before/after measurements in the optimization report. The source file is unchanged at the end.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Reuses the explicit operation authorization and asks only for missing parameters.",
        "Plans the optimized output as a new file in /workspace/output and does not overwrite the source.",
        "Leaves /workspace/input/factory/cases/factory.usda unchanged.",
        "Plans to record the output path and before/after measurements in the optimization report."
      ],
      "files": [
        "evals/files/factory/"
      ]
    },
    {
      "id": "usd-performance-zero-work-operation",
      "prompt": "The operation completed. Did anything actually change? Inspect /workspace/input/completed-run/evidence.json and its before/after USD stages, then report the result in /workspace/output.",
      "expected_output": "The operation succeeded but changed nothing: the before and after stages are identical and every recorded metric matches. The result is reported as neutral, without claiming an improvement the metrics do not support.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Reads the omniverse-usd-performance-tuning SKILL.md.",
        "Compares before/after metrics and stages rather than trusting the operation's success status alone.",
        "Recognizes a success-but-zero-work result and reports it as neutral.",
        "Does not present an unchanged stage as an improvement."
      ],
      "files": [
        "evals/files/completed-run/"
      ]
    },
    {
      "id": "usd-performance-quality-budget-not-triangle-budget",
      "prompt": "Optimize /workspace/input/factory/cases/factory.usda for fifty loaded stations. Keep named equipment addressable and moving links editable; merge static interiors. Preserve 0.5 mm detail, without a triangle target, visible loss or shrinking world bounds. No USD runtime is installed; don't install one. Write your plan to /workspace/output/plan.md.",
      "expected_output": "plan.md treats the 0.5 mm tolerance as a decimateMeshes quality budget (maxMeanError 0.0005 in this metre-unit stage, reductionFactor 0.0, pinBoundaries true) and does not turn it into a triangle count or reduction rate. It scopes merge to one call per named equipment unit so each stays addressable by path, keeps the moving links editable, and carries the no-visible-loss and no-shrink invariants into a verification step that compares bounds and surface against the source.",
      "expected_skill": "omniverse-usd-performance-tuning",
      "assertions": [
        "Plans decimateMeshes against the stated surface-error budget in stage units and keeps the quality stop condition live rather than substituting a triangle count or reduction rate.",
        "Does not ask the user for a triangle target or a reduction percentage when a quality budget has already been given.",
        "Scopes merge beneath each named unit so the named units remain addressable by path, rather than a single stage-wide merge.",
        "Carries the no-visible-loss and no-shrink constraints into an explicit verification step comparing bounds and surface against the source.",
        "States plainly that any requested step the skill has no phase for is out of scope instead of improvising or silently dropping it."
      ],
      "files": [
        "evals/files/factory/"
      ]
    }
  ]
}

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: Needs Python, Needs Node.js

The author says: Requires a verified standalone Usd Optimize/OpenUSD runtime for operations, usd-validation-nvidia for supported checks, and a writable output workspace. Remote assets require a working resolver. Render profiling is an external handoff.

Security

What our scanner noticed· Medium risk

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Attribution and licence

Created by

NVIDIA Omniverse

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/omniverse-usd-performance-tuning · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

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

Download ZIP

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