[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-omniverse-usd-performance-tuning":3,"mdc-lgkyg1-key":33,"related-org-openai-omniverse-usd-performance-tuning":1789,"related-repo-openai-omniverse-usd-performance-tuning":1994},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":22,"repoUrl":23,"updatedAt":24,"license":25,"forks":26,"topics":27,"repo":28,"sourceUrl":31,"mdContent":32},"omniverse-usd-performance-tuning","optimize USD scene performance","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\u002Fruntime setup to Phase 0 owners.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"openai","OpenAI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fopenai.png",[12,16,19],{"name":13,"slug":14,"type":15},"Performance","performance","tag",{"name":17,"slug":18,"type":15},"Engineering","engineering",{"name":20,"slug":21,"type":15},"Design","design",3992,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins","2026-06-30T19:00:57.102","Apache-2.0",465,[],{"repoUrl":23,"stars":22,"forks":26,"topics":29,"description":30},[],"OpenAI Plugins","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins\u002Ftree\u002FHEAD\u002Fplugins\u002Fnvidia\u002Fskills\u002Fomniverse-usd-performance-tuning","---\nname: omniverse-usd-performance-tuning\ndescription: \"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\u002Fruntime setup to Phase 0 owners.\"\nversion: \"0.1.0\"\nlicense: Apache-2.0\ntools:\n  - Read\n  - Shell\n  - Write\ncompatibility: >\n  Orchestrator skill. Downstream phases may require Kit, Scene Optimizer, Asset Validator, USD Python, writable output paths, and omniverse:\u002F\u002F authentication selected by setup-usd-performance-tuning.\nmetadata:\n  author: NVIDIA Omniverse\n  tags:\n    - triage\n    - performance\n    - usd\n    - profiling\n  domain: ai-ml\n  languages:\n    - python\n---\n# Omniverse USD Performance Tuning\n\n\u003C!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->\n\u003C!-- SPDX-License-Identifier: Apache-2.0 -->\n\n## When to Use\n\nUse 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.\n\n## Instructions\n\n1. Start from the mandatory runtime context gate before producing tuning output, unless the prompt is only asking for a static classification test.\n2. Classify broad optimization requests as `ready_to_plan`; reserve `approval_required` for prompts that explicitly name a destructive operation to execute before planning.\n3. Plan the full canonical chain through `optimization-report`, preserving the structured milestone order and the `profile-stage:baseline` \u002F `profile-stage:after` labels when listing milestones.\n4. Invoke downstream skill bodies only when their phase is reached, and keep raw runtime artifacts on disk while reading compact summaries.\n\nFrontmatter keeps `version` and `tools` at top level for agentskills.io runtime\ncompatibility. NVCARPS discoverability fields live under `metadata`.\n\n## Output Format\n\nReturn 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\u002Foptimization-report.schema.json` within that reference).\n\nUse this workflow for broad performance asks such as slow loading, low FPS,\nhigh memory, GPU crashes, conversion quality, or \"optimize my scene.\"\n\n## Entry skill rule\n\nThis skill is the named entry point for broad performance work whenever the\nagent has any verified way to do that work. Runtime probing details live in\n`setup-usd-performance-tuning`; this rule only decides which skill owns the\nuser-facing performance request.\n\n- If the setup probe shows **any** verified runtime path - Kit, standalone, or\n  even a partial stack such as Asset Validator only - enter here. If the\n  user's requested tool is missing, return the specific `blocked_code`\n  (`blocked_missing_scene_optimizer`, `blocked_missing_so_operation`, etc.)\n  instead of substituting another workflow.\n- Enter at `setup-usd-performance-tuning` only when **no** runtime path is\n  verified and runtime choice\u002Fsetup is the first unresolved problem.\n- For `omniverse:\u002F\u002F` assets, enter at `omniverse-authentication` first.\n  Authentication precedes setup and triage for remote assets.\n\nThe 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.\n\n## Runtime context — session-start gate (mandatory)\n\n**Before any other tuning output**, follow the mandatory session-start gate in\n`skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fsetup-usd-performance-tuning\u002Freferences\u002Fruntime-context-header.md`.\nThat reference owns `output_path`, the canonical `setup-preflight.json`\nlocation, Format A\u002FFormat B, and the \"do not improvise a silent probe\"\nanti-pattern.\n\nRequired outcomes:\n\n- Missing or unreadable preflight: invoke `setup-usd-performance-tuning`.\n- Present preflight: print Format A and wait for the user to choose Continue,\n  Change Kit, Switch to standalone, or Re-run probe.\n- Confirmed runtime in the same session: use compact Format B for follow-up\n  status.\n\n```\n[Kit: {runtime_context.kit.application} {runtime_context.kit.version}  |  SO: {runtime_context.sceneOptimizer.version}  |  AV: {runtime_context.assetValidator.version}]\n```\n\n## Runtime artifact token budget\n\nBefore reading Kit logs, Asset Validator CSVs, Scene Optimizer logs, Tracy CSVs,\nor other runtime output, follow\n`references\u002Fruntime-artifact-token-budget.md`. Keep raw artifacts on disk, read\nsummary JSON first, and use bounded log snapshots instead of full dumps or live\nstreams.\n\n## Plan-time vs execution-time approval\n\n`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`:\n\n- **`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.\n- **`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**.\n\nThe 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.\n\nFor structured runtime-test responses and similar planning summaries:\n\n- 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.\n- 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`.\n- It is valid for `gates_observed` to include `asks_user_for_restructure_decision` while the top-level `decision` remains `ready_to_plan`.\n- 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.\n- 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.\n- 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.\n- 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\u002Finterpreter milestones appear after the restructure decision path in the structured plan contract.\n\n## Output expectation\n\nEnd-to-end optimization work should produce both an optimized USD stage, when\nmutation is executed, and a structured optimization report conforming to\nthe `optimization-report` reference's `scripts\u002Foptimization-report.schema.json`. The HTML report must be rendered\nfrom `references\u002Freport-templates\u002Foptimization-report.html.template` via\n`render_preview.py` — never hand-write HTML. Diagnosis-only work should still\nend with a report or summary that states no optimized stage was written.\n\n## Purpose\n\nRoute digital twin USD performance requests into the right diagnostic and\noptimization workflow while preserving evidence before mutation.\n\n## Prerequisites\n\n- Stage path or enough context to identify the target asset.\n- User goal: diagnosis only, validation, profiling, or processor execution.\n- Runtime availability status from `setup-usd-performance-tuning` when not already known.\n- Permission status for in-place mutation vs writing a separate optimized output.\n\n## Examples\n\n- \"This USD loads slowly; triage what to check first.\"\n- \"Route a low-FPS CAD scene through the performance workflow.\"\n\n## Triage order\n\n0. **Runtime gate.** Follow the mandatory session-start gate above before\n   validation, profiling, or optimization. Do not scan, probe, install, or pick\n   Kit\u002Fstandalone runtimes directly in this skill; `setup-usd-performance-tuning`\n   owns probe\u002Fchooser\u002Finstall dispatch and writes the preflight consumed here.\n\n1. Identify the target problem:\n   - Load time.\n   - FPS or interactivity.\n   - GPU or system memory.\n   - Crash or device lost.\n   - CAD conversion quality.\n   - Validation failure.\n\n2. Gather minimum context:\n   - Stage path and size.\n   - Whether the stage is local, mounted, or `omniverse:\u002F\u002F` remote. For remote\n     assets, route through `omniverse-authentication` before first open.\n   - Kit or USD runtime.\n   - Whether the workload is CAD, VFI, AIF, Isaac, or generic OpenUSD.\n   - Whether in-place mutation is allowed.\n   - Whether the user wants diagnosis only or processor execution.\n\n3. Route:\n   - USD composition questions: `usd-structure-assessment` (composition is now part of the SA umbrella; deeper detail in `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Fcomposition-audit.md`).\n   - Validation and content issues: `usd-validation-runner` (master router; routes to `validate-*` family or `so-run-validators` based on intent).\n   - Edit\u002Foutput decisions: `usd-edit-target-planner` (also owns variant\u002Fpayload gates).\n   - Repeated copied hierarchy or high mesh count with no instancing:\n     `usd-hierarchy-dedupe-candidates`.\n   - Restructure decision (monolithic stage, asset boundary materialization): `restructure-decision`.\n   - CAD converter settings: read `references\u002Fcad-conversion\u002FREADME.md` (niche pre-USD concern; see reference for details).\n   - Scene Optimizer: `so-run-validators`, `so-interpret-validators`, `so-run-operations`.\n\n## Optimization ordering\n\nFollow the canonical ordering in\n[workflow.md § Operation ordering invariants](references\u002Fworkflow.md#operation-ordering-invariants).\nThe high-level rule: **prototypes first → per-asset validation → stage-level\noperations last.** The workflow reference owns the full invariant list\n(meshCleanup before decimateMeshes, deduplication before decimation, never\nmerge if instanced, etc.) and the analysis-only ops catalogue.\n\n## Rules\n\n- Always run composition audit before mutation.\n- Always validate before and after processor execution.\n- Optimize prototypes before per-asset validation.\n- Do not run whole-stage mesh deduplication on very large CAD scenes before\n  checking for hierarchy-level reuse.\n- Do not recommend a fixed optimization stack without bottleneck evidence.\n- Do not invent numeric thresholds or expected percentage wins.\n- **Prefer canonical SO ops over specialty \u002F documentary ones.** The op\n  curation in `references\u002Foperations\u002F_curation.json` classifies every op\n  as `canonical`, `specialty`, `analysis`, `documentary`, or `deprecated`.\n  When more than one op could resolve the same finding, recommend the\n  canonical one first and only reach for a specialty op when the user\n  explicitly asks or the rationale warrants it. Specifically:\n  - For vertex welding, prefer canonical `meshCleanup` with explicit flags\n    over the standalone `mergeVertices` op. The standalone op is a\n    legacy\u002Fspecialty surface; use upstream `usd-optimize` for the operation\n    mechanics and local approval policy before mutating.\n  - For hierarchy dedupe, recommend `usd-hierarchy-dedupe-candidates` +\n    `apply-restructure` (the USD-authored rewrite path).\n  - For per-mesh dedupe, recommend `deduplicateGeometry` (canonical) over\n    `findCoincidingGeometry` (analysis — produces a report, not a change).\n  - Do not recommend `documentary`-status ops (e.g., `boxClip`,\n    `deletePrims`, `removeAttributes`, `removeUntypedPrims`,\n    `merge` outside its narrow non-instanced case) without an explicit\n    user request. Documentary ops survive in the per-op\n    `references\u002Foperations\u002F\u003Ckey>.md` routing stubs for completeness but are\n    excluded from agent-initiated recommendations.\n  - **Specialty ≠ documentary.** Ops classified as `specialty` in\n    `_curation.json` either (a) have validator-finding evidence that\n    wires them into the `so-interpret-validators` chain (e.g.\n    `sparseMeshes`, `optimizePrimvars`), or (b) are load-bearing escape\n    hatches needed for specific downstream contexts (e.g.\n    `primitivesToMeshes` when output must be `UsdGeomMesh`,\n    `utilityFunction` for instancing toggles and material rebinding,\n    `pythonScript` for `so-create-proxy` recipes). Recommend specialty\n    ops when their validator fires OR when their downstream context\n    applies — the suppression above only targets `documentary` ops.\n\n## Limitations\n\n- Does not replace downstream reference instructions; load each required\n  reference before executing it.\n- Does not install runtimes directly; follow setup or install references when\n  requirements are missing.\n- Does not authorize mutation when the user has not allowed writes.\n\n## Troubleshooting\n\n- If runtime status is unclear, run `setup-usd-performance-tuning` before profiling or validation.\n- If the reported problem is vague, gather stage path, workload type, and whether diagnosis or execution is requested.\n- If the workflow suggests mutation before evidence, return to baseline profiling and composition audit first.\n\n## References\n\nBefore routing, read:\n\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Foptimization-tradeoffs.md` — identify which pipeline phase the scene is in (extraction, structuring, or optimization). The right action depends on the phase.\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Ffactory-level-structuring.md` — understand the three pillars (assets, aggregation, animation) and the seven-step structuring pattern.\n\nIf you have network access, prefer the live URLs (noted in each reference file) for the most current version.\n\n## Required execution flow\n\nRead `references\u002Fworkflow.md` for the canonical Phase 0-7 flow, including\nKit\u002Fstandalone branches, validator-stack routing, operation ordering,\ntermination conditions, duration hints, and the optional iteration pattern.\nThe compact root map at `references\u002Fskill-map.md` only routes agents\ninto this workflow.\n\nDo not treat downstream phase names as plain checklist labels. Before executing\neach step, load that phase's nested `README.md` reference and follow its\ninstructions. Claude Code only exposes the public catalog skill; it does not\nrecursively inject `profile-stage`, `usd-structure-assessment`, or other nested\nreferences.\n\nThe 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.\n\nFor deeper subtopic guidance, consult the references:\n\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Fcomposition-audit.md`, `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Flayer-health.md` - subtopic detail for SA's Phase 1 checklist.\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Finstancing-readiness\u002Freferences\u002Finstancing-tradeoffs.md` - merge safety, decision tree for instancing choices.\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Fusd-edit-target-planner\u002Freferences\u002Fvariants-payloads.md` - deeper variant\u002Fpayload trade-offs (gates are inline in usd-edit-target-planner).\n- `references\u002Fcad-conversion\u002FREADME.md` - CAD converter settings.\n- `references\u002Fupstreams\u002Fusd-optimize.md` - upstream SO mechanics and prebuilt package resolution.\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-validation-runner\u002Freferences\u002Fso-run-validators\u002Freferences\u002Finfrastructure.md` - local handoff for SO validator infrastructure.\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-validation-runner\u002Freferences\u002Fvalidation-scoping.md` - tier 1\u002F2\u002F3 plan + scene-aware adjustment.\n- `skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Foptimization-report\u002Freferences\u002Foptimization-report-template.md` - the data contract every phase populates.\n\nFor full Kit runtime profiling (FPS, frame time, Hydra\u002FRTX metrics), refer to the external profiling skills at NVIDIA\u002Fomniperf.\n",{"data":34,"body":50},{"name":4,"description":6,"version":35,"license":25,"tools":36,"compatibility":40,"metadata":41},"0.1.0",[37,38,39],"Read","Shell","Write","Orchestrator skill. 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The right action depends on the phase.",{"type":54,"tag":85,"props":1595,"children":1596},{},[1597,1603],{"type":54,"tag":96,"props":1598,"children":1600},{"className":1599},[],[1601],{"type":59,"value":1602},"skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Ffactory-level-structuring.md",{"type":59,"value":1604}," — understand the three pillars (assets, aggregation, animation) and the seven-step structuring pattern.",{"type":54,"tag":69,"props":1606,"children":1607},{},[1608],{"type":59,"value":1609},"If you have network access, prefer the live URLs (noted in each reference file) for the most current version.",{"type":54,"tag":62,"props":1611,"children":1613},{"id":1612},"required-execution-flow",[1614],{"type":59,"value":1615},"Required execution flow",{"type":54,"tag":69,"props":1617,"children":1618},{},[1619,1621,1627,1629,1635],{"type":59,"value":1620},"Read ",{"type":54,"tag":96,"props":1622,"children":1624},{"className":1623},[],[1625],{"type":59,"value":1626},"references\u002Fworkflow.md",{"type":59,"value":1628}," for the canonical Phase 0-7 flow, including\nKit\u002Fstandalone branches, validator-stack routing, operation ordering,\ntermination conditions, duration hints, and the optional iteration pattern.\nThe compact root map at ",{"type":54,"tag":96,"props":1630,"children":1632},{"className":1631},[],[1633],{"type":59,"value":1634},"references\u002Fskill-map.md",{"type":59,"value":1636}," only routes agents\ninto this workflow.",{"type":54,"tag":69,"props":1638,"children":1639},{},[1640,1642,1648,1650,1655,1656,1661],{"type":59,"value":1641},"Do not treat downstream phase names as plain checklist labels. Before executing\neach step, load that phase's nested ",{"type":54,"tag":96,"props":1643,"children":1645},{"className":1644},[],[1646],{"type":59,"value":1647},"README.md",{"type":59,"value":1649}," reference and follow its\ninstructions. Claude Code only exposes the public catalog skill; it does not\nrecursively inject ",{"type":54,"tag":96,"props":1651,"children":1653},{"className":1652},[],[1654],{"type":59,"value":753},{"type":59,"value":366},{"type":54,"tag":96,"props":1657,"children":1659},{"className":1658},[],[1660],{"type":59,"value":231},{"type":59,"value":1662},", or other nested\nreferences.",{"type":54,"tag":69,"props":1664,"children":1665},{},[1666,1668,1673,1675,1681],{"type":59,"value":1667},"The final deliverable must come from ",{"type":54,"tag":96,"props":1669,"children":1671},{"className":1670},[],[1672],{"type":59,"value":122},{"type":59,"value":1674},": save both the structured JSON report and the generated Markdown summary. Do not substitute an ad hoc ",{"type":54,"tag":96,"props":1676,"children":1678},{"className":1677},[],[1679],{"type":59,"value":1680},"SUMMARY.md",{"type":59,"value":1682}," or chat-only recap for the optimization report.",{"type":54,"tag":69,"props":1684,"children":1685},{},[1686],{"type":59,"value":1687},"For deeper subtopic guidance, consult the references:",{"type":54,"tag":335,"props":1689,"children":1690},{},[1691,1708,1719,1730,1740,1751,1762,1773],{"type":54,"tag":85,"props":1692,"children":1693},{},[1694,1699,1700,1706],{"type":54,"tag":96,"props":1695,"children":1697},{"className":1696},[],[1698],{"type":59,"value":1069},{"type":59,"value":366},{"type":54,"tag":96,"props":1701,"children":1703},{"className":1702},[],[1704],{"type":59,"value":1705},"skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Flayer-health.md",{"type":59,"value":1707}," - subtopic detail for SA's Phase 1 checklist.",{"type":54,"tag":85,"props":1709,"children":1710},{},[1711,1717],{"type":54,"tag":96,"props":1712,"children":1714},{"className":1713},[],[1715],{"type":59,"value":1716},"skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Finstancing-readiness\u002Freferences\u002Finstancing-tradeoffs.md",{"type":59,"value":1718}," - merge safety, decision tree for instancing choices.",{"type":54,"tag":85,"props":1720,"children":1721},{},[1722,1728],{"type":54,"tag":96,"props":1723,"children":1725},{"className":1724},[],[1726],{"type":59,"value":1727},"skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-structure-assessment\u002Freferences\u002Fusd-edit-target-planner\u002Freferences\u002Fvariants-payloads.md",{"type":59,"value":1729}," - deeper variant\u002Fpayload trade-offs (gates are inline in usd-edit-target-planner).",{"type":54,"tag":85,"props":1731,"children":1732},{},[1733,1738],{"type":54,"tag":96,"props":1734,"children":1736},{"className":1735},[],[1737],{"type":59,"value":1145},{"type":59,"value":1739}," - CAD converter settings.",{"type":54,"tag":85,"props":1741,"children":1742},{},[1743,1749],{"type":54,"tag":96,"props":1744,"children":1746},{"className":1745},[],[1747],{"type":59,"value":1748},"references\u002Fupstreams\u002Fusd-optimize.md",{"type":59,"value":1750}," - upstream SO mechanics and prebuilt package resolution.",{"type":54,"tag":85,"props":1752,"children":1753},{},[1754,1760],{"type":54,"tag":96,"props":1755,"children":1757},{"className":1756},[],[1758],{"type":59,"value":1759},"skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-validation-runner\u002Freferences\u002Fso-run-validators\u002Freferences\u002Finfrastructure.md",{"type":59,"value":1761}," - local handoff for SO validator infrastructure.",{"type":54,"tag":85,"props":1763,"children":1764},{},[1765,1771],{"type":54,"tag":96,"props":1766,"children":1768},{"className":1767},[],[1769],{"type":59,"value":1770},"skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Fusd-validation-runner\u002Freferences\u002Fvalidation-scoping.md",{"type":59,"value":1772}," - tier 1\u002F2\u002F3 plan + scene-aware adjustment.",{"type":54,"tag":85,"props":1774,"children":1775},{},[1776,1782],{"type":54,"tag":96,"props":1777,"children":1779},{"className":1778},[],[1780],{"type":59,"value":1781},"skills\u002Fomniverse-usd-performance-tuning\u002Freferences\u002Foptimization-report\u002Freferences\u002Foptimization-report-template.md",{"type":59,"value":1783}," - the data contract every phase populates.",{"type":54,"tag":69,"props":1785,"children":1786},{},[1787],{"type":59,"value":1788},"For full Kit runtime profiling (FPS, frame time, Hydra\u002FRTX metrics), refer to the external profiling skills at NVIDIA\u002Fomniperf.",{"items":1790,"total":1993},[1791,1812,1835,1852,1868,1887,1906,1920,1936,1950,1962,1977],{"slug":1792,"name":1792,"fn":1793,"description":1794,"org":1795,"tags":1796,"stars":1809,"repoUrl":1810,"updatedAt":1811},"prior-auth-packet-builder","build healthcare prior authorization packets","Build a concise prior authorization packet from local case files and payer policy docs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1797,1800,1803,1806],{"name":1798,"slug":1799,"type":15},"Documents","documents",{"name":1801,"slug":1802,"type":15},"Healthcare","healthcare",{"name":1804,"slug":1805,"type":15},"Insurance","insurance",{"name":1807,"slug":1808,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":1813,"name":1813,"fn":1814,"description":1815,"org":1816,"tags":1817,"stars":1832,"repoUrl":1833,"updatedAt":1834},"aspnet-core","build ASP.NET Core web applications","Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1818,1821,1823,1826,1829],{"name":1819,"slug":1820,"type":15},".NET","dotnet",{"name":1822,"slug":1813,"type":15},"ASP.NET Core",{"name":1824,"slug":1825,"type":15},"Blazor","blazor",{"name":1827,"slug":1828,"type":15},"C#","csharp",{"name":1830,"slug":1831,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":1836,"name":1836,"fn":1837,"description":1838,"org":1839,"tags":1840,"stars":1832,"repoUrl":1833,"updatedAt":1851},"chatgpt-apps","build ChatGPT Apps SDK applications","Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1841,1844,1847,1850],{"name":1842,"slug":1843,"type":15},"Apps SDK","apps-sdk",{"name":1845,"slug":1846,"type":15},"ChatGPT","chatgpt",{"name":1848,"slug":1849,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":1853,"name":1853,"fn":1854,"description":1855,"org":1856,"tags":1857,"stars":1832,"repoUrl":1833,"updatedAt":1867},"cli-creator","build CLIs from API docs","Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read\u002Fwrite commands, return stable JSON, manage auth, and pair with a companion skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1858,1861,1864],{"name":1859,"slug":1860,"type":15},"API Development","api-development",{"name":1862,"slug":1863,"type":15},"CLI","cli",{"name":1865,"slug":1866,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":1869,"name":1869,"fn":1870,"description":1871,"org":1872,"tags":1873,"stars":1832,"repoUrl":1833,"updatedAt":1886},"cloudflare-deploy","deploy projects to Cloudflare","Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1874,1877,1880,1883],{"name":1875,"slug":1876,"type":15},"Cloudflare","cloudflare",{"name":1878,"slug":1879,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":1881,"slug":1882,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":1884,"slug":1885,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":1888,"name":1888,"fn":1889,"description":1890,"org":1891,"tags":1892,"stars":1832,"repoUrl":1833,"updatedAt":1905},"define-goal","define and set measurable project goals","Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1893,1896,1899,1902],{"name":1894,"slug":1895,"type":15},"Productivity","productivity",{"name":1897,"slug":1898,"type":15},"Project Management","project-management",{"name":1900,"slug":1901,"type":15},"Strategy","strategy",{"name":1903,"slug":1904,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":1907,"name":1907,"fn":1908,"description":1909,"org":1910,"tags":1911,"stars":1832,"repoUrl":1833,"updatedAt":1919},"figma","translate Figma designs into code","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1912,1913,1915,1918],{"name":20,"slug":21,"type":15},{"name":1914,"slug":1907,"type":15},"Figma",{"name":1916,"slug":1917,"type":15},"Frontend","frontend",{"name":1848,"slug":1849,"type":15},"2026-04-12T05:06:47.939943",{"slug":1921,"name":1921,"fn":1922,"description":1923,"org":1924,"tags":1925,"stars":1832,"repoUrl":1833,"updatedAt":1935},"figma-code-connect-components","connect Figma designs to code components","Connects Figma design components to code components using Code Connect mapping tools. Use when user says \"code connect\", \"connect this component to code\", \"map this component\", \"link component to code\", \"create code connect mapping\", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1926,1927,1930,1931,1932],{"name":20,"slug":21,"type":15},{"name":1928,"slug":1929,"type":15},"Design System","design-system",{"name":1914,"slug":1907,"type":15},{"name":1916,"slug":1917,"type":15},{"name":1933,"slug":1934,"type":15},"UI Components","ui-components","2026-05-10T05:59:52.971881",{"slug":1937,"name":1937,"fn":1938,"description":1939,"org":1940,"tags":1941,"stars":1832,"repoUrl":1833,"updatedAt":1949},"figma-create-design-system-rules","generate design system rules from Figma","Generates custom design system rules for the user's codebase. Use when user says \"create design system rules\", \"generate rules for my project\", \"set up design rules\", \"customize design system guidelines\", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1942,1943,1944,1947,1948],{"name":20,"slug":21,"type":15},{"name":1928,"slug":1929,"type":15},{"name":1945,"slug":1946,"type":15},"Documentation","documentation",{"name":1914,"slug":1907,"type":15},{"name":1916,"slug":1917,"type":15},"2026-05-16T06:07:47.821474",{"slug":1951,"name":1951,"fn":1952,"description":1953,"org":1954,"tags":1955,"stars":1832,"repoUrl":1833,"updatedAt":1961},"figma-implement-design","translate Figma designs into application code","Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions \"implement design\", \"generate code\", \"implement component\", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1956,1957,1958,1959,1960],{"name":20,"slug":21,"type":15},{"name":1914,"slug":1907,"type":15},{"name":1916,"slug":1917,"type":15},{"name":1933,"slug":1934,"type":15},{"name":1830,"slug":1831,"type":15},"2026-05-16T06:07:40.583615",{"slug":1963,"name":1963,"fn":1964,"description":1965,"org":1966,"tags":1967,"stars":1832,"repoUrl":1833,"updatedAt":1976},"hatch-pet","create animated pets for Codex","Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1968,1971,1972,1975],{"name":1969,"slug":1970,"type":15},"Animation","animation",{"name":1865,"slug":1866,"type":15},{"name":1973,"slug":1974,"type":15},"Creative","creative",{"name":20,"slug":21,"type":15},"2026-05-02T05:31:48.48485",{"slug":1978,"name":1978,"fn":1979,"description":1980,"org":1981,"tags":1982,"stars":1832,"repoUrl":1833,"updatedAt":1992},"imagegen","generate and edit raster images","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1983,1984,1985,1988,1991],{"name":1973,"slug":1974,"type":15},{"name":20,"slug":21,"type":15},{"name":1986,"slug":1987,"type":15},"Image Generation","image-generation",{"name":1989,"slug":1990,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675,{"items":1995,"total":2106},[1996,2012,2028,2040,2058,2076,2094],{"slug":1997,"name":1997,"fn":1998,"description":1999,"org":2000,"tags":2001,"stars":22,"repoUrl":23,"updatedAt":24},"accessibility-and-inclusive-visualization","make data visualizations accessible","Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2002,2005,2008,2011],{"name":2003,"slug":2004,"type":15},"Accessibility","accessibility",{"name":2006,"slug":2007,"type":15},"Charts","charts",{"name":2009,"slug":2010,"type":15},"Data Visualization","data-visualization",{"name":20,"slug":21,"type":15},{"slug":2013,"name":2013,"fn":2014,"description":2015,"org":2016,"tags":2017,"stars":22,"repoUrl":23,"updatedAt":2027},"agent-browser","automate browser interactions for agents","Browser automation CLI for AI agents. Use when the user needs to interact with websites, verify dev server output, test web apps, navigate pages, fill forms, click buttons, take screenshots, extract data, or automate any browser task. Also triggers when a dev server starts so you can verify it visually.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2018,2021,2024],{"name":2019,"slug":2020,"type":15},"Agents","agents",{"name":2022,"slug":2023,"type":15},"Browser Automation","browser-automation",{"name":2025,"slug":2026,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":2029,"name":2029,"fn":2030,"description":2031,"org":2032,"tags":2033,"stars":22,"repoUrl":23,"updatedAt":2039},"agent-browser-verify","verify dev server output with automated browser","Automated browser verification for dev servers. Triggers when a dev server starts to run a visual gut-check with agent-browser — verifies the page loads, checks for console errors, validates key UI elements, and reports pass\u002Ffail before continuing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2034,2035,2038],{"name":2022,"slug":2023,"type":15},{"name":2036,"slug":2037,"type":15},"Local Development","local-development",{"name":2025,"slug":2026,"type":15},"2026-04-06T18:41:17.526867",{"slug":2041,"name":2041,"fn":2042,"description":2043,"org":2044,"tags":2045,"stars":22,"repoUrl":23,"updatedAt":2057},"agents-sdk","build AI agents on Cloudflare Workers","Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2046,2047,2048,2051,2054],{"name":2019,"slug":2020,"type":15},{"name":1881,"slug":1882,"type":15},{"name":2049,"slug":2050,"type":15},"SDK","sdk",{"name":2052,"slug":2053,"type":15},"Serverless","serverless",{"name":2055,"slug":2056,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":2059,"name":2059,"fn":2060,"description":2061,"org":2062,"tags":2063,"stars":22,"repoUrl":23,"updatedAt":2075},"ai-elements","build chat UIs with AI Elements","AI Elements component library guidance — pre-built React components for AI interfaces built on shadcn\u002Fui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2064,2065,2068,2071,2072],{"name":1916,"slug":1917,"type":15},{"name":2066,"slug":2067,"type":15},"React","react",{"name":2069,"slug":2070,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":1933,"slug":1934,"type":15},{"name":2073,"slug":2074,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":2077,"name":2077,"fn":2078,"description":2079,"org":2080,"tags":2081,"stars":22,"repoUrl":23,"updatedAt":2093},"ai-gateway","configure Vercel AI Gateway","Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2082,2085,2088,2091,2092],{"name":2083,"slug":2084,"type":15},"AI Infrastructure","ai-infrastructure",{"name":2086,"slug":2087,"type":15},"Cost Optimization","cost-optimization",{"name":2089,"slug":2090,"type":15},"LLM","llm",{"name":13,"slug":14,"type":15},{"name":2073,"slug":2074,"type":15},"2026-04-06T18:40:44.377464",{"slug":2095,"name":2095,"fn":2096,"description":2097,"org":2098,"tags":2099,"stars":22,"repoUrl":23,"updatedAt":2105},"ai-generation-persistence","implement persistence patterns for AI generations","AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2100,2101,2104],{"name":2086,"slug":2087,"type":15},{"name":2102,"slug":2103,"type":15},"Database","database",{"name":2089,"slug":2090,"type":15},"2026-04-06T18:41:08.513425",600]