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://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
references/operations/scripts/operations.schema.json6.2 KiB
references/optimization-report/scripts/apply_palette.pyScript13.3 KiB
references/optimization-report/scripts/asset_results.pyScript5.9 KiB
references/optimization-report/scripts/definition_verification.pyScript7.1 KiB
references/optimization-report/scripts/descent_convergence_common.pyScript9.0 KiB
references/optimization-report/scripts/optimization-report.schema.json39.4 KiB
references/optimization-report/scripts/structure_conformance.pyScript15.2 KiB
references/optimization-report/scripts/validate_report.pyScript55.2 KiB
references/remote-assets.md1.6 KiB
references/report-templates/optimization-report.html.template14.9 KiB
references/report-templates/optimization-report.md.template6.2 KiB
references/report-templates/render_preview.pyScript17.9 KiB
references/report-templates/theme.css711 B
references/setup-usd-performance-tuning/scripts/probe-snapshot.schema.json1.7 KiB
references/setup-usd-performance-tuning/scripts/setup-preflight.schema.json6.4 KiB
references/setup.md3.3 KiB
references/structure.md20.8 KiB
references/upstreams/usd-optimize.md6.3 KiB
references/usd-optimize-run-operations/scripts/asset_authoring.pyScript54.9 KiB
references/usd-optimize-run-operations/scripts/batch-plan.schema.json7.5 KiB
references/usd-optimize-run-operations/scripts/category_publication.pyScript6.3 KiB
references/usd-optimize-run-operations/scripts/current_run_reuse.pyScript11.6 KiB
references/usd-optimize-run-operations/scripts/material_optimization.pyScript4.9 KiB
references/usd-optimize-run-operations/scripts/material_publication.pyScript9.5 KiB
references/usd-optimize-run-operations/scripts/native_reconstruction.pyScript7.9 KiB
references/usd-optimize-run-operations/scripts/native_worker.pyScript68.4 KiB
references/usd-optimize-run-operations/scripts/pack_definitions.pyScript1.6 KiB
references/usd-optimize-run-operations/scripts/persistent_library.pyScript20.3 KiB
references/usd-optimize-run-operations/scripts/persistent_reuse.pyScript31.1 KiB
references/usd-optimize-run-operations/scripts/prim_regroup.pyScript14.9 KiB
references/usd-optimize-run-operations/scripts/run_batch.pyScript32.5 KiB
references/usd-optimize-run-operations/scripts/semantic_library.pyScript48.2 KiB
references/usd-optimize-run-operations/scripts/shared_geometry.pyScript25.5 KiB
references/usd-optimize-run-operations/scripts/status.schema.json7.9 KiB
references/usd-structure-assessment/references/apply-restructure/scripts/apply-restructure-manifest.schema.json35.3 KiB
references/usd-structure-assessment/references/usd-hierarchy-dedupe-candidates/scripts/instance_candidate_finder.pyScript12.7 KiB
references/usd-structure-assessment/references/usd-mesh-fragmentation-candidates/scripts/mesh_fragmentation_finder.pyScript13.8 KiB
references/usd-structure-assessment/scripts/atomic_placement.pyScript4.9 KiB
references/usd-structure-assessment/scripts/audit-report.schema.json2.3 KiB
references/usd-structure-assessment/scripts/boundary_reasoning.pyScript36.0 KiB
references/usd-structure-assessment/scripts/discovery_preview.pyScript8.6 KiB
references/usd-structure-assessment/scripts/optimization-plan.schema.json1.5 KiB
references/usd-structure-assessment/scripts/structure_assessment.pyScript112.2 KiB
references/usd-structure-assessment/scripts/usd-structure-assessment-report.schema.json14.0 KiB
references/usd-validation-runner/references/validator-concepts.json35.8 KiB
references/usd-validation-runner/scripts/usd_validation_executor.pyScript37.4 KiB
references/usd-validation-runner/scripts/validation-report.schema.json3.5 KiB
references/usd-validation-runner/scripts/validation-scope-note.schema.json2.6 KiB
references/usd-validation-runner/scripts/validator-concepts.schema.json3.8 KiB
references/verification.md11.9 KiB
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
scripts/successor_reconstruction.pyScript19.6 KiB
scripts/successor_workflow.pyScript55.0 KiB
scripts/usd_tuning.pyScript24.7 KiB
skill-card.md5.5 KiB
skill.oms.sig26.5 KiB

references/upstreams/usd-optimize.md

usd-optimize / Usd Optimize Package Handoff

Usd Optimize operation mechanics are owned by upstream usd-optimize and ship with the prebuilt Usd Optimize package. This package owns digital twin workflow routing, runtime setup context, validation scope, output workspace policy, batch orchestration, and reporting.

Public repository: https://github.com/NVIDIA-Omniverse/usd-optimize/
Prebuilt packages: GitHub Releases on the repository above (https://github.com/NVIDIA-Omniverse/usd-optimize/releases). Each release carries Linux x86_64, Linux aarch64, and Windows x86_64 zips.
Verified release: 1.3.1. Published 2026-10-08; its CHANGELOG entry is dated 2026-10-07. Check the release page before selecting another version, then verify its package and live API.
Package pattern: usd_optimize_usd_<usd>_py_<python>@<version>.<platform>.release.zip. The operation catalog and validator concepts describe 1.3.1: 48 operations, including moveMaterials, the editStageMetrics.stopAtSkeletonRoot argument and the shrinkwrap temporal arguments. 1.1.0 and earlier lack those and register validator rules under the bare names the concept registry no longer uses, so they are not supported.
Pick the USD flavor explicitly. 1.3.1 ships three USD flavors per platform (usd_25.05, usd_25.11 and usd_26.08), and a bare -p '*manylinux*x86_64*' matches all three. Use usd_25.11 unless a caller needs another:
```
gh release download v1.3.1 -R NVIDIA-Omniverse/usd-optimize \
  -p 'usd_optimize_usd_25.11_py_3.12@*manylinux_2_35_x86_64.release.zip'
```
(or pick the asset from the releases page in a browser).
Asset sizes vary by flavor, so quote the flavor when you quote a size. For 1.3.1 Linux x86_64: usd_25.11 is ~121 MiB (~540 MB extracted), usd_25.05 is ~913 MiB, and usd_26.08 is ~105 MiB.
Package operation guides: docs/operations/<operation>.rst
Package operation runner skill: .agents/skills/run-operations/SKILL.md
Package validator runner skill: .agents/skills/run-validators/SKILL.md
Package validator interpretation skill: .agents/skills/interpret-validators/SKILL.md
Package proxy skill: .agents/skills/create-proxy/SKILL.md
Package install skill: .agents/skills/prebuilt-package/SKILL.md

Operation Guide Resolution

For any operation key listed in references/operations/operations.json, derive the upstream mechanics path instead of storing per-operation package details in this repo. Resolve it under the selected package root ($USD_OPTIMIZE_ROOT), without cloning the source repo. This is the single place this rule is stated; other skill files point here.

Package path template: $USD_OPTIMIZE_ROOT/docs/operations/<operation-key>.rst.
Sidecars: operation index docs/operations.rst, pipeline guidance docs/choosing-operations.rst, invocation docs/cli.rst. The six presets in config_presets/ ship in the package and are what usdOptimize -c config_presets/<name>.json reads.
Upstream web URL template, pinned to the verified tag: https://github.com/NVIDIA-Omniverse/usd-optimize/blob/v1.3.1/docs/operations/<operation-key>.rst.

The package root must contain docs/operations/ and docs/operations.rst, plus the runtime sentinels python/, usdpy/, lib/, and extraLibs/ when it is also used for standalone execution.

The package ships .agents/skills/, including the five skills listed above. It ships no .claude or .codex compatibility aliases; use .agents paths in handoffs.

The package also ships a native bin/usdOptimize CLI and a getOperationDocumentation() method on the core instance. The native CLI does not start a Python interpreter, so the four Python-hosted operations (pythonScript, deleteHiddenPrims, removeUntypedPrims, moveMaterials) are unavailable through it and report invalid operation specified; it lists the other 44. This skill drives Usd Optimize through the Python bindings, where all 48 operations are available.

If no package root exists, download and extract the published usd_optimize_...release.zip package for the target platform from GitHub Releases, or use the package archive path, release-asset URL, or extracted package root supplied by the user. Extract with a tool that restores symbolic links, such as unzip: the shared libraries are symlink chains. Package-internal paths (.agents/..., docs/operations/..., config_presets/, python/, usdpy/, lib/, extraLibs/) were last verified on 2026-10-12 against the extracted 1.3.1 Linux x86_64 usd_25.11 package. If web or raw GitHub fetch is available, the public repository URL can be used for docs-only reads. Do not clone the source repo just to read operation parameters, defaults, or implementation gotchas.

The package ships no Python distribution metadata (no *.dist-info, *.egg-info, or entry_points.txt anywhere in the tree), so importlib.metadata.entry_points() cannot discover the bundled validator plugin. Consequently usd_optimize.validators requires an explicit register_all() call — importing the module alone registers no Usd Optimize rules; register_all() registers 25 (19 under Usd:Performance, 6 under Omni:Geometry). They register as UsdOptimize<Name>, for example UsdOptimizeWindingsChecker: the module rewrites each rule's __name__ at import to keep it clear of identically named usd-validation-nvidia rules. validator-concepts.json names those classes.

Version Reporting

The verified prebuilt package does not stamp a version into its bindings. Record the changelog as version evidence through setup:

Candidate source	1.3.1
`usd_optimize.core.__version__`	absent
`CHANGELOG.md` first heading	`## [1.3.1] - 2026-10-07`

Record the package changelog heading and its source path as version evidence. Distinguish changelog evidence from a version reported by the bindings.

Use references/operations/operations.json — the single catalog carrying both routing metadata and the nested curation block (generated status + authored wired_into; rationale only on overrides) — for digitaltwin routing, risk, confirmation, and recommendation posture. Before invoking any operation, consume <output_path>/setup-preflight.json and confirm the op appears in usdOptimize.operationsAvailable.

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

Large base64-encoded blobs
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

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

2026-11-09

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