Omniverse Usd Performance Tuning

OpenAIOfficialCreated by NVIDIA OmniverseDistributed by OpenAI

Skill
Workflow
Software development
Apache-2.0
Medium risk
Has scripts

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

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/openai/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

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

Start from the mandatory runtime context gate before producing tuning output, unless the prompt is only asking for a static classification test.
Classify broad optimization requests as ready_to_plan; reserve approval_required for prompts that explicitly name a destructive operation to execute before planning.
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.
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

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.
Identify the target problem:
- Load time.
- FPS or interactivity.
- GPU or system memory.
- Crash or device lost.
- CAD conversion quality.
- Validation failure.
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.
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/usd-validation-runner/README.md

USD Validation Runner (master router)

When to Use

Use this reference for validation-only requests or when the performance workflow reaches the validation-routing phase.

Instructions

Identify whether the request is validation-only or a validation phase inside the optimization workflow.
Select the smallest validation stack that can change the user-visible decision or operation plan.
Ask before full Asset Validator sweeps or Tier 3 expensive cross-component checks.
Route execution to the owning validation reference or skill and preserve evidence paths for later reporting.

Pre-flight Checklist

Before running validators, re-read and confirm:

[ ] references/validation-scoping.md — tier selection, phase-aware subsets, deferred validators, full-sweep approval gate.
[ ] SA report's phase_recommendation and summary_counts drive tier selection.
[ ] Apply runtime-artifact-token-budget.md for large CSV/log output.
[ ] Route findings to so-interpret-validators for op-chain construction — do not map findings to ops yourself.

Output Format

Return a scoped validation plan or validation summary naming the selected validator stack, skipped expensive checks, approval gates, artifact paths, and findings that affect the optimization plan.

Purpose

Use this reference whenever a workflow needs to surface USD validity or performance validator issues. It picks the smallest validation stack that can affect the optimization plan, records the evidence contract, and routes concrete execution to the owning skill or reference.

This reference does not execute optimization operations and does not choose fix strategies.

Use this reference as the entry point for validation-only requests such as "validate this USD", "run Asset Validator", or "show validation issues". For broad performance diagnosis, slow loading, high memory, low FPS, or "what should I optimize?", start with omniverse-usd-performance-tuning so structure assessment can scope validation before expensive validator runs.

For omniverse:// targets, start with omniverse-authentication before this skill attempts runtime probing or stage open.

Tier tables and the JSON plan template live in references/validation-scoping.md under this reference. This router points at that reference for tier detail rather than duplicating it, which keeps validation policy portable for future SO/OAV repo splits.

Prerequisites

Target stage or asset paths and resolver context.
Available validator runtime (Omni Asset Validator inside Kit, project-managed AV install, or installed Scene Optimizer APIs).
Artifact directory for logs, CSV/JSON findings, and provider summaries.
Baseline, waiver, or failure policy for pre/post processing gates.
(For perf-stack scoping) usd-structure-assessment report with phase_recommendation and flagged_assets.

Session-start runtime gate (mandatory when this is the entry skill)

If this reference is the entry skill for the user's request (i.e., the agent invoked /usd-validation-runner directly rather than through omniverse-usd-performance-tuning), run the session-start gate from skills/omniverse-usd-performance-tuning/references/setup-usd-performance-tuning/references/runtime-context-header.md Mandatory session-start gate before any routing. The gate determines output_path, checks <output_path>/setup-preflight.json, invokes setup-usd-performance-tuning if the preflight is missing, then surfaces Format A + the 4-option confirmation. Do not pick a validator stack until the user has confirmed the runtime.

If invoked downstream of an entry skill that already fired the gate in the same session, skip the gate and proceed.

Limitations

Validation depends on tools installed outside this repo.
Scene Optimizer validation results are valid only after imports and checker classes are verified in the active runtime.
Validation evidence is not cleanup; auto-fixes require separate change recording.

Troubleshooting

If omni_asset_validate is unavailable, record it as missing rather than fabricating a pass.
If Scene Optimizer validator imports fail, do not report SO-specific results.
If the bundled validator-venv is slow or lacks dependencies, prefer a Kit or project-managed Asset Validator environment.

Validator Stack Matrix

The router picks one or more stacks based on the intent and the structure assessment phase_recommendation. Each stack lists the owned reference that describes the concrete validation command; this reference orchestrates the routing.

All validation cost-control policy lives in references/validation-scoping.md. That reference owns tier tables, the full-sweep approval gate, deferred-validator rules, spot-check policy, phase-aware subset selection, and the per-rule timeout pattern. Read it when building a perf-stack plan. The key principles:

Structure assessment first — never start with a full default AV sweep.
Full-sweep approval gate — ask before a default rule set on large/unknown assets.
SO analysis stays in the validation plan regardless of execution path.
Spot-check before dropping signal — masked-stage with ≥25% mesh coverage.

Pre-Mutation USD Stack

For "is this asset USD-correct enough to mutate?", run only the checks needed to protect the requested mutation.

Step	Owner	When
1	Inline minimum-openability check	Always before mutation. Confirm the stage opens, root/default prim is valid enough for the task, asset paths resolve, and scale/up-axis metadata is known or recorded as missing.
2	`references/validate-usd-asset-validator.md`	When executable NVIDIA Omniverse Asset Validator coverage is needed. Scope by targeted rules/categories unless the user explicitly approves a full sweep.
3	External profile/package validators	Only when the user explicitly requests a domain profile such as SimReady or a package-validation workflow. Do not keep those command references in this package.

Performance stack (scoped)

For "is this asset performant?" The chain is: scope -> run -> interpret. Tier and phase-aware scoping live in references/validation-scoping.md; the router instructs the agent to load that reference and emit a plan, then invoke the downstream tools.

Step	Tool / reference	When
1	(Read `references/validation-scoping.md`)	Build the tier plan from the structure assessment's `flagged_assets` + `phase_recommendation`.
2	`so-run-validators`	Execute the tier plan against the asset.
3	`so-interpret-validators`	Read artifacts, classify findings (T1/T2/T3), recommend fixes.

Phase-aware subset selection

Owned by references/validation-scoping.md → Decision Tree. Document the chosen subset in the validation plan so Phase 6 re-validation can reproduce the same scope.

Routing decision

For a given request, pick the stack(s) using this table:

Intent	Stacks
"Validate this USD before any mutation."	Pre-mutation USD stack: minimum-openability plus targeted Asset Validator coverage when needed.
"What's wrong with this asset?" (broad performance ask)	`usd-structure-assessment` first, then Performance stack scoped per the structure assessment. Add pre-mutation USD stack only when the request is about USD validity or mutation safety.
"Run perf validators only."	Performance stack only.
"Validate the optimized output (Phase 6 re-validation)."	Same stacks that ran in Phase 2c, for a fair before/after comparison.
"Validate a SimReady package/profile."	Defer to the external SimReady/Foundation validation workflow; keep only local USD and performance evidence in this package.

If intent is ambiguous, ask the user before expanding scope - especially before invoking Tier 3 perf validators or a default full-stage Asset Validator sweep (both can be very slow and can produce very large logs/reports).

For performance work, the validation output should be a compact operation-oriented summary, not a raw issue dump. Use so-interpret-validators/references/rule-reference.md for rule-to-operation mapping instead of maintaining another mapping in this router.

Commands

references/validate-usd-asset-validator.md owns Asset Validator runtime details. so-run-validators owns Scene Optimizer performance-validator execution. This router keeps only the common direct invocation shape:

Base Asset Validator (used by validate-usd-asset-validator). Probe the selected runtime before using optional output flags; use JSON output when the selected runtime advertises it, otherwise fall back to CSV. The commands below are invocation shapes, not default scope choices. A bare full-stage invocation is allowed only after the full-sweep gate has been approved.

omni_asset_validate --help
omni_asset_validate /path/to/asset.usd \
  --category Geometry \
  --csv-output /path/to/artifacts/issues.csv

Minimum openability baseline is a USD-open check, not a default Asset Validator run:

from pxr import Usd

stage = Usd.Stage.Open("/path/to/asset.usd")
if stage is None:
    raise RuntimeError("stage did not open")
root = stage.GetDefaultPrim()
print({
    "opened": True,
    "default_prim": root.GetPath().pathString if root else None,
    "up_axis": stage.GetMetadata("upAxis"),
    "meters_per_unit": stage.GetMetadata("metersPerUnit"),
})

For SO-specific validators, follow so-run-validators; SO validator rules auto-register via @register_rule decorators when both packages share the same Python environment.

Programmatic per-rule API

For one-rule-at-a-time validation (used by the per-rule timeout pattern in references/validation-scoping.md), drive the engine directly:

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

registry = CategoryRuleRegistry()
rule = next(
    rule
    for rule in registry.get_rules("Usd:Performance")
    if rule.__name__ == "PrimitiveFitChecker"
)

engine = ValidationEngine(init_rules=False)
engine.enable_rule(rule)
stage = Usd.Stage.Open("path/to/asset.usd")
results = engine.validate(stage)                           # synchronous
for issue in results.issues():
    ...

Inside Kit, import omni.asset_validator.core instead of omni.asset_validator (the .core submodule is the in-Kit name). The standalone package uses the plain omni.asset_validator import.

Use this API when you need:

Per-rule timeouts on a large stage (see references/validation-scoping.md Per-rule timeout pattern).
A single rule's findings without running the full default set.
Cross-runtime parity checks (same stage, same rule, both Kit and standalone).

Do not route validation through the Scene Optimizer package's bundled validator-venv — it may lack numpy, making large-stage validation slow. Use a project-managed venv with both omniverse-asset-validator and the SO package on PYTHONPATH.

Required gates

Pre-processing (before any mutation):

Stage opens.
Asset paths resolve.
Minimum-openability and selected Asset Validator checks complete.
Known blocker findings are either fixed or waived.

Post-processing (Phase 6 re-validation):

Stage opens.
Validation is no worse than baseline unless explicitly accepted.
Generated outputs are recorded.
Processor report and validation report are attached to the optimization plan.

Output

Emit validation-report.json matching scripts/validation-report.schema.json. The report must point to provider artifacts including issues.csv, provider-summary.json, and run.log, and include the chosen tier/phase scoping so Phase 6 can reproduce it.

Rules

Validation is evidence, not cleanup by itself.
Convert findings into candidate operations via so-interpret-validators/references/rule-reference.md; do not hand the user raw validator jargon as the recommendation.
Do not auto-fix without recording which layers or files changed.
Do not continue after structural USD failures unless the plan is diagnosis-only.
Do not call Scene Optimizer operation skills until validation evidence has been captured or the user has explicitly chosen to proceed without it.
All cost-control rules (tiers, gates, thresholds) live in references/validation-scoping.md. Do not invent thresholds inline.

References

references/validation-scoping.md - tier 1/2/3 tables, JSON plan template, scene-aware adjustment, phase-aware subset rules. Read this when building a perf-stack plan.
skills/omniverse-usd-performance-tuning/references/usd-validation-runner/references/so-run-validators/references/infrastructure.md - SO validator infrastructure (programmatic API, REQUIRES_MESH cache, entry-point allow-list, CLI gotchas, libusd alignment). Read this when debugging SO validator setup.
skills/omniverse-usd-performance-tuning/references/workflow.md - canonical 7-phase flow context for where validation sits.

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

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This skill includes scripts that your agent may run. Read them before you use it.

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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

2026-11-08

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

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by NVIDIA Omniverse

Omniverse USD Performance Tuning

When to Use

Instructions

Output Format

Entry skill rule

Runtime context — session-start gate (mandatory)

Runtime artifact token budget

Plan-time vs execution-time approval

Output expectation

Purpose

Prerequisites

Examples

Triage order

Optimization ordering

Rules

Limitations

Troubleshooting

References

Required execution flow

Files

references/usd-validation-runner/README.md

USD Validation Runner (master router)

When to Use

Instructions

Pre-flight Checklist

Output Format

Purpose

Prerequisites

Session-start runtime gate (mandatory when this is the entry skill)

Limitations

Troubleshooting

Validator Stack Matrix

Pre-Mutation USD Stack

Performance stack (scoped)

Phase-aware subset selection

Routing decision

Commands

Programmatic per-rule API

Required gates

Output

Rules

References

Requirements and compatibility

Security

Attribution and licence

Related skills

Diagnosing Bugs

Systematic Debugging

Performance Optimization

Debugging And Error Recovery

Animation Performance

Parallel Debugging

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