[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-omniverse-cad-to-simready":3,"mdc--37h8g8-key":33,"related-org-openai-omniverse-cad-to-simready":1438,"related-repo-openai-omniverse-cad-to-simready":1643},{"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-cad-to-simready","convert CAD assets to SimReady format","Coordinate the end-to-end CAD\u002Fsource-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material\u002Fphysics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.",{"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},"Automation","automation","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-cad-to-simready","---\nname: omniverse-cad-to-simready\ndescription: \"Coordinate the end-to-end CAD\u002Fsource-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material\u002Fphysics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.\"\nversion: \"0.1.0\"\nlicense: Apache-2.0\ntools:\n  - Read\n  - Shell\ncompatibility: >\n  Orchestrator skill. Managed Content Agents deployment requires NVIDIA_API_KEY\n  (build.nvidia.com), Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and\n  an upstream checkout of nvidia-omniverse\u002Fcontent-agents on branch\n  main. Reused\u002Fprovided endpoints may instead use explicit endpoint and\n  usage-token environment variables. Linux\u002FmacOS only.\nmetadata:\n  author: Omniverse\n  tags:\n    - physical-ai\n    - simready\n    - workflow\n    - cad\n    - conversion\n  domain: ai-ml\n  languages:\n    - python\n---\n\n# CAD to SimReady\n\n## When to Use\n\nUse this workflow skill when the user wants an end-to-end pipeline from a\nsource asset to a SimReady asset or package. This skill coordinates existing\nconversion, authoring, validation, conformance, rendering, and packaging\nreferences directly. Do not replace the workflow with a single monolithic\nrunner command.\n\nThis skill is documentation-driven and does not ship `scripts\u002Frun.py`. It\nshould not depend on a repository checkout. When a stage needs deterministic\nexecution, run the portable script from that stage reference's installed\ndirectory. `Shell` is declared because this workflow invokes installed stage\nreference scripts directly; it still must not grow a monolithic runner.\n\n## Prerequisites\n\n- Prefer running the `preflight` reference first for deterministic setup. It\n  installs or verifies local upstream checkouts, writes a\n  `cad-to-simready-preflight.json` manifest, and exports\n  `PHYSICAL_AI_PREFLIGHT_MANIFEST` plus `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` for\n  downstream references.\n- Python 3.12 and `uv` (per repo `README.md`).\n- NVIDIA_API_KEY from `https:\u002F\u002Fbuild.nvidia.com` when local Content Agents\n  deployment will run. Already-running endpoints may instead use explicit\n  endpoint variables plus usage tokens such as `NGC_API_KEY`, `NVCF_API_KEY`,\n  or `CONTENT_AGENTS_*_TOKEN`.\n- Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and\n  OVRTX stages.\n- Local upstream checkouts under\n  `${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME\u002F.physical-ai-skill-hub\u002Fupstreams}`\n  when a downstream stage needs upstream scripts or specs.\n\n## Minimum Viable Scope\n\nConversion-only is a valid workflow request. When the user asks only to convert\nor smoke-test source asset conversion, set `property_assignment_intent=skip`,\ndo not deploy Content Agents, run `convert-to-usd`, then run\n`validate-usd-minimum` on the generated USD if conversion succeeds.\n\nDo not imply that `uv sync` installs every source converter runtime. URDF,\nMuJoCo\u002FMJCF, and the repo Python dependencies are handled by the project\nenvironment, but NVIDIA-backed source conversion requires an installed and\nvalidated `NVIDIA-Omniverse\u002Fusd-convert-cad` checkout. If that runtime is\nmissing or does not support the source, preserve the blocked conversion report\nand its `install_hint` instead of attempting an unrequested local build or\nsubstituting another converter.\n\n## First Action\n\nFor any broad CAD\u002Fsource-asset to SimReady request, assume\n`property_assignment_intent=run` unless the user explicitly asks for\nconversion-only, validation-only, or no material\u002Fphysics assignment.\n\nBefore invoking converter, validation, Content Agents, OVRTX, packaging, or FET\nhelper scripts, run the `preflight` reference or verify an existing\n`PHYSICAL_AI_PREFLIGHT_MANIFEST`. Treat preflight as the mandatory dependency\nbootstrap step, not as workflow routing. If the user explicitly asks not to\ndeploy services or asks for conversion-only\u002Fvalidation-only, use\n`--skip-content-agents`.\nWhen `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` is set and a required component is not\nready in the manifest, downstream references must block with the preflight\nguardrail instead of rediscovering upstreams or services directly.\n\nWhen `property_assignment_intent=run`, the first operational action after\nconfirming the source path and resolving intent is to verify or deploy Content\nAgents services. Do this before asset-context inspection, converter dependency\nchecks, conversion, validation, conformance, rendering, packaging, or upstream\nsource builds.\n\nUse healthy existing endpoints when available. If OVRTX, Material, or Physics\nendpoints are missing or unhealthy, run `deploy-content-agents`\nfirst and do not continue until the shared standalone OVRTX renderer plus\nindependent Material and Physics service containers are healthy and exported\nthrough `CONTENT_AGENTS_*_BASE_URL`. Deploy the Texture Agent too when texture\ngeneration is requested.\n\nIf required deployment authentication is missing, ask the user for\n`NVIDIA_API_KEY` and wait. If a provided endpoint requires usage auth, ask for\nthe appropriate usage token instead. If deployment cannot produce healthy\nservices, report Content Agents readiness as blocked instead of proceeding to\nconversion.\n\n## Instructions\n\n1. Confirm the source asset path exists, resolve `output_root`, and classify\n   the request as end-to-end, conversion-only, validation-only, or packaging.\n2. Resolve `property_assignment_intent` before running any asset inspection,\n   converter probe, conversion, validation, conformance, rendering, or\n   packaging step.\n3. Run `preflight` for the selected workflow targets, unless a ready\n   `PHYSICAL_AI_PREFLIGHT_MANIFEST` is already configured. Source the generated\n   env file before running downstream scripts. Treat preflight as dependency\n   setup only: it may use a provided `--source-asset`, `--source-format`, or\n   `--conversion-tools` value to scope dependency checks, but `convert-to-usd`\n   and the upstream converter references still decide actual conversion support.\n4. Verify or deploy Content Agents services first when\n   `property_assignment_intent=run`; block on missing authentication or\n   unhealthy services instead of continuing.\n5. Read `references\u002Fworkflow.md` and `references\u002Fcommands.md`, then run only\n   the stage references needed for the current request.\n6. Run `identify-asset-context` on the original source asset when web search is\n   available or property assignment will run.\n7. Route the source through `convert-to-usd`, or skip conversion for existing\n   USD input and treat the source path as the current USD path.\n8. Run `validate-usd-minimum` before expensive downstream work. Treat this as a\n   viability gate only: record unit\u002Fprofile issues such as `metersPerUnit !=\n   1.0`, but do not run `simready-conform-profile`, FET001, or any other FET\n   repair before Content Agents assignment when property assignment will run.\n9. Run Content Agents material, physics, and optional texture assignment on the\n   converted\u002Fminimum-valid USD when requested or required.\n10. Run `simready-conform-profile` on the latest simulation USD path after\n   property assignment and preserve every selected FET repair report.\n11. Run validation gates in order: `omni-asset-validate`,\n   `omni-asset-validate-geometry`, `omni-asset-validate-physics`, and\n   `simready-validate`.\n12. Rerun `simready-conform-profile` when `simready-validate` reports a\n    repairable requirement, then rerun profile validation on the newest authored\n    USD.\n13. Run `ovrtx-render-service` when preview, thumbnail, or inspection images\n    are requested. When package outputs are requested, run\n    `assemble-package-source` next to create the clean `deliverable\u002F` package\n    source from the final USD and thumbnail, then run `nv-core-package-sample`\n    and `nv-core-package-sample-validation` on that deliverable folder only.\n14. Emit the consolidated workflow report with the final USD path, all stage\n    reports, validation findings, rerun reasons, and next work.\n\nUse the `simready-conform-profile` reference only after property assignment\nwhen `property_assignment_intent=run`. It routes feature repair to upstream\nSimReady Foundation FET skills such\nas `simready-foundation-conform-fet-000-core`,\n`simready-foundation-conform-fet-001-minimal`,\n`simready-foundation-conform-fet-004-simulate-multi-body-physics`, and\n`simready-foundation-conform-fet-005-simulate-grasp-physics` from branch\n`main`.\n\nIf `simready-validate` reports a repairable requirement after the first\nconformance pass, feed the structured requirement IDs back into\nthe `simready-conform-profile` reference before writing the final result. In\nparticular, `GSP.001` is owned by upstream\n`simready-foundation-conform-fet-005-simulate-grasp-physics`; run that skill when a\nvision-capable agent can inspect visual evidence or explicit grasp points were\nprovided, otherwise record the FET005 step as blocked by missing vision\u002Fpoints\ninstead of treating it as an optional preview task.\nFor `RB.MB.001`, route the failure to\nupstream `simready-foundation-conform-fet-004-simulate-multi-body-physics`. Do not assume\nmultiple visual prims are multiple rigid bodies; inspect\n`UsdPhysics.RigidBodyAPI` applications. When the Physics Agent report shows\ncomposed topology optimization or the USD has existing component colliders\u002Fpart\nroots and the profile validator reports FET004\u002FRB.MB.001, FET004 should promote\nthose existing components into rigid bodies without creating geometry. Do not\nmark the gate not applicable until after confirming there are fewer than two\nreusable body candidates.\n\n## Output Format\n\nEmit a consolidated workflow report in Markdown, and include JSON when the\nworkflow writes structured artifacts. The report must include:\n\n- Overall status: `passed`, `blocked`, `failed`, or `needs_rerun`.\n- Request summary: source asset path, detected source format, output root,\n  selected SimReady profile\u002Fversion, and property assignment intent.\n- Ordered stage results: stage reference, input artifact, output USD or USDZ\n  path, report path, status, blocker reason, and rerun reason when applicable.\n- Content Agents readiness and property assignment results with service URLs,\n  tokens, and credentials redacted.\n- Conformance and validation findings grouped by gate, requirement ID, selected\n  FET repair reference, repair-loop attempt, and final disposition.\n- Final artifacts: final reported USD path, render preview path when requested,\n  package root and package validation report when packaging ran, Markdown report\n  path, JSON report path when present, and recommended next work.\n\n## Detailed References\n\nRead only the references needed for the current request:\n\n- `references\u002Fpreflight\u002FREADME.md`: deterministic local setup, manifest\u002Fenv\n  contract, Linux and Windows wrappers, Content Agents deployment opt-out, and\n  guardrail behavior.\n- `references\u002Fworkflow.md`: inputs, source routing, detailed workflow,\n  validation policy, output report fields, approval points, and next steps.\n- `references\u002Fcommands.md`: concrete portable script command patterns for each\n  stage.\n- `references\u002Fassemble-package-source\u002FREADME.md`: two-zone package source\n  assembly, canonical root USD naming, thumbnail placement, and self-contained\n  deliverable checks.\n\n## Publishing Layout Notes\n\nUse `skills\u002Fomniverse-cad-to-simready\u002F` as the source of truth for this product\nrepo's skill. The `.agents\u002Fskills` symlink is a compatibility alias for local\nagentskills.io-style discovery, and `.codex\u002Fskills` and `.claude\u002Fskills` are\nagent-specific compatibility aliases.\n\nFrontmatter keeps `version` and `tools` at top level for agentskills.io runtime\ncompatibility. NVCARPS discoverability fields live under `metadata`.\n\nThe nested `references\u002F` tree is intentional. It keeps one public catalog skill\nwhile retaining script-bearing atomic stage references, upstream handoff notes,\nand router documentation under the workflow. Do not flatten those references or\npromote nested README references to sibling `SKILL.md` files unless the repo's\npublishing model changes.\n\n## Limitations\n\n- This workflow coordinates existing conversion, property assignment,\n  conformance, validation, rendering, and packaging skills; it does not replace\n  them with a single monolithic runner command.\n- Stop at the first failing deployment, conversion, property-assignment, or\n  conformance authoring gate unless the user explicitly asks for best-effort\n  continuation.\n- Upstream `simready-foundation-conform-fet-005-simulate-grasp-physics` needs visual\n  review or explicit grasp points before it can author a meaningful grasp\n  vector.\n\n## Troubleshooting\n\n| Symptom | Cause | Fix |\n|---------|-------|-----|\n| Downstream reference reports that cad-to-simready preflight has not prepared a component | `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` is set, but the manifest is missing or the required runtime\u002Fservice is not `ready` | Run `preflight\u002Fscripts\u002Fpreflight.py`, source the generated env file, or explicitly disable service deployment with `--skip-content-agents` only when Content Agents are out of scope. |\n| Workflow stops on `GSP.001` and reports the failure as unclassified | Visual evidence or explicit grasp points were not provided to FET005 | Run upstream `simready-foundation-conform-fet-005-simulate-grasp-physics` only after a vision-capable agent has reviewed the asset, or pass explicit grasp points. Otherwise report the FET005 step as `blocked`, not failed. |\n| Validation fails after a meaningful USD artifact already exists | Workflow stopped at the first validation finding | Continue remaining diagnostic gates and mark the result `needs_rerun`. Do not stop at validation findings once a USD artifact has been produced. |\n| Property-assignment stage fails with a missing service endpoint | Content Agents service was not deployed before conversion | Run `deploy-content-agents` first. Do not start asset inspection, conversion, validation, conformance, rendering, or packaging before Content Agents readiness when property assignment will run. |\n| Material Agent reports that rendering produced `0 images` after unit or profile repair | A FET repair, commonly FET001 unit normalization, was applied before Material Agent and changed the USD layering\u002Fscene state consumed by the service | Rerun assignment from the converted\u002Fminimum-valid USD: Material Agent first, then Physics Agent, then run `simready-conform-profile` and FET repairs on the latest service-authored USD. |\n| Material or Physics Agent local optimized path reports `Permission denied: '\u002Fapp\u002F.build-resources\u002Fscene_optimizer_core\u002Fpython'` | Local Docker Scene Optimizer bundle permissions prevent the non-root service user from reading the packaged SO runtime | Repair the relevant local container with `docker exec --user root content-material-agent-service chmod -R a+rX \u002Fapp\u002F.build-resources\u002Fscene_optimizer_core` or `docker exec --user root content-physics-agent-service chmod -R a+rX \u002Fapp\u002F.build-resources\u002Fscene_optimizer_core`, then rerun the same optimized agent command. Do not treat the no-optimizer fallback as the root cause for instanced\u002Fprototype assets. |\n| `RB.MB.001` fails even though the asset has many prims | The profile counts `UsdPhysics.RigidBodyAPI` prims, not visual or collider prims; Physics Agent may author one root rigid body | Route to upstream `simready-foundation-conform-fet-004-simulate-multi-body-physics`. First ensure Physics Agent used composed-topology optimization when applicable, then promote existing component colliders\u002Fpart roots when the active profile reports FET004\u002FRB.MB.001 and no geometry must be invented. |\n\n## Hard Rules\n\n- Prefer the preflight manifest for local upstream roots, converter\n  executables, SimReady validation runtime, OVRTX endpoint, and Content Agents\n  service URLs. When `PHYSICAL_AI_REQUIRE_PREFLIGHT=1` is set, do not bypass the\n  manifest with direct upstream discovery.\n- Do not run asset inspection, converter probes, local upstream builds,\n  conversion, validation, conformance, rendering, or packaging before Content\n  Agents readiness when property assignment will run.\n- Use stage-specific installed reference scripts directly. Do not add or call a\n  single `omniverse-cad-to-simready` runner command.\n- For source conversion, delegate to the `convert-to-usd` reference; do not\n  substitute another converter for CAD or mesh formats.\n- For property assignment, use Content Agents references as separate atomic steps:\n  material first, then physics, then texture only when requested.\n- When property assignment will run, do not run `simready-conform-profile` or\n  any FET helper before Content Agents. Validate minimum USD first, then run\n  Content Agents on that converted\u002Fminimum-valid USD, then apply FET repairs to\n  the latest service-authored USD.\n- When property assignment will run, do not run `simready-validate` or any\n  SimReady profile validation before Content Agents. The only validation gate\n  allowed before service calls is `validate-usd-minimum`, which is a basic USD\n  viability check.\n- Stop at the first failing deployment, conversion, property-assignment, or\n  conformance authoring gate unless the user explicitly asks for best-effort\n  continuation.\n- Do not stop at validation findings after a meaningful USD artifact exists.\n  Continue remaining diagnostic gates and mark the result `needs_rerun`.\n- Do not leave a `GSP.001` profile failure as an unclassified final finding.\n  Route it to upstream `simready-foundation-conform-fet-005-simulate-grasp-physics`; if\n  the current agent cannot inspect renders or no explicit grasp points are\n  available, report a blocked FET005 repair with the visual evidence path or\n  missing input reason.\n- Preserve every stage report and pass the concrete output USD path from each\n  report into the next stage.\n",{"data":34,"body":51},{"name":4,"description":6,"version":35,"license":25,"tools":36,"compatibility":39,"metadata":40},"0.1.0",[37,38],"Read","Shell","Orchestrator skill. Managed Content Agents deployment requires NVIDIA_API_KEY (build.nvidia.com), Docker + NVIDIA Container Toolkit + GPU, Python 3.12, and an upstream checkout of nvidia-omniverse\u002Fcontent-agents on branch main. Reused\u002Fprovided endpoints may instead use explicit endpoint and usage-token environment variables. Linux\u002FmacOS only.\n",{"author":41,"tags":42,"domain":48,"languages":49},"Omniverse",[43,44,45,46,47],"physical-ai","simready","workflow","cad","conversion","ai-ml",[50],"python",{"type":52,"children":53},"root",[54,63,70,76,97,103,221,227,256,285,291,304,338,350,371,384,390,672,727,784,790,795,857,863,868,913,919,955,982,1003,1009,1034,1040,1314,1320],{"type":55,"tag":56,"props":57,"children":59},"element","h1",{"id":58},"cad-to-simready",[60],{"type":61,"value":62},"text","CAD to SimReady",{"type":55,"tag":64,"props":65,"children":67},"h2",{"id":66},"when-to-use",[68],{"type":61,"value":69},"When to Use",{"type":55,"tag":71,"props":72,"children":73},"p",{},[74],{"type":61,"value":75},"Use this workflow skill when the user wants an end-to-end pipeline from a\nsource asset to a SimReady asset or package. This skill coordinates existing\nconversion, authoring, validation, conformance, rendering, and packaging\nreferences directly. Do not replace the workflow with a single monolithic\nrunner command.",{"type":55,"tag":71,"props":77,"children":78},{},[79,81,88,90,95],{"type":61,"value":80},"This skill is documentation-driven and does not ship ",{"type":55,"tag":82,"props":83,"children":85},"code",{"className":84},[],[86],{"type":61,"value":87},"scripts\u002Frun.py",{"type":61,"value":89},". It\nshould not depend on a repository checkout. When a stage needs deterministic\nexecution, run the portable script from that stage reference's installed\ndirectory. ",{"type":55,"tag":82,"props":91,"children":93},{"className":92},[],[94],{"type":61,"value":38},{"type":61,"value":96}," is declared because this workflow invokes installed stage\nreference scripts directly; it still must not grow a monolithic runner.",{"type":55,"tag":64,"props":98,"children":100},{"id":99},"prerequisites",[101],{"type":61,"value":102},"Prerequisites",{"type":55,"tag":104,"props":105,"children":106},"ul",{},[107,145,166,203,208],{"type":55,"tag":108,"props":109,"children":110},"li",{},[111,113,119,121,127,129,135,137,143],{"type":61,"value":112},"Prefer running the ",{"type":55,"tag":82,"props":114,"children":116},{"className":115},[],[117],{"type":61,"value":118},"preflight",{"type":61,"value":120}," reference first for deterministic setup. It\ninstalls or verifies local upstream checkouts, writes a\n",{"type":55,"tag":82,"props":122,"children":124},{"className":123},[],[125],{"type":61,"value":126},"cad-to-simready-preflight.json",{"type":61,"value":128}," manifest, and exports\n",{"type":55,"tag":82,"props":130,"children":132},{"className":131},[],[133],{"type":61,"value":134},"PHYSICAL_AI_PREFLIGHT_MANIFEST",{"type":61,"value":136}," plus ",{"type":55,"tag":82,"props":138,"children":140},{"className":139},[],[141],{"type":61,"value":142},"PHYSICAL_AI_REQUIRE_PREFLIGHT=1",{"type":61,"value":144}," for\ndownstream references.",{"type":55,"tag":108,"props":146,"children":147},{},[148,150,156,158,164],{"type":61,"value":149},"Python 3.12 and ",{"type":55,"tag":82,"props":151,"children":153},{"className":152},[],[154],{"type":61,"value":155},"uv",{"type":61,"value":157}," (per repo ",{"type":55,"tag":82,"props":159,"children":161},{"className":160},[],[162],{"type":61,"value":163},"README.md",{"type":61,"value":165},").",{"type":55,"tag":108,"props":167,"children":168},{},[169,171,177,179,185,187,193,195,201],{"type":61,"value":170},"NVIDIA_API_KEY from ",{"type":55,"tag":82,"props":172,"children":174},{"className":173},[],[175],{"type":61,"value":176},"https:\u002F\u002Fbuild.nvidia.com",{"type":61,"value":178}," when local Content Agents\ndeployment will run. Already-running endpoints may instead use explicit\nendpoint variables plus usage tokens such as ",{"type":55,"tag":82,"props":180,"children":182},{"className":181},[],[183],{"type":61,"value":184},"NGC_API_KEY",{"type":61,"value":186},", ",{"type":55,"tag":82,"props":188,"children":190},{"className":189},[],[191],{"type":61,"value":192},"NVCF_API_KEY",{"type":61,"value":194},",\nor ",{"type":55,"tag":82,"props":196,"children":198},{"className":197},[],[199],{"type":61,"value":200},"CONTENT_AGENTS_*_TOKEN",{"type":61,"value":202},".",{"type":55,"tag":108,"props":204,"children":205},{},[206],{"type":61,"value":207},"Docker, NVIDIA Container Toolkit, and an NVIDIA GPU for Content Agents and\nOVRTX stages.",{"type":55,"tag":108,"props":209,"children":210},{},[211,213,219],{"type":61,"value":212},"Local upstream checkouts under\n",{"type":55,"tag":82,"props":214,"children":216},{"className":215},[],[217],{"type":61,"value":218},"${PHYSICAL_AI_SKILL_HUB_UPSTREAM_ROOT:-$HOME\u002F.physical-ai-skill-hub\u002Fupstreams}",{"type":61,"value":220},"\nwhen a downstream stage needs upstream scripts or specs.",{"type":55,"tag":64,"props":222,"children":224},{"id":223},"minimum-viable-scope",[225],{"type":61,"value":226},"Minimum Viable Scope",{"type":55,"tag":71,"props":228,"children":229},{},[230,232,238,240,246,248,254],{"type":61,"value":231},"Conversion-only is a valid workflow request. When the user asks only to convert\nor smoke-test source asset conversion, set ",{"type":55,"tag":82,"props":233,"children":235},{"className":234},[],[236],{"type":61,"value":237},"property_assignment_intent=skip",{"type":61,"value":239},",\ndo not deploy Content Agents, run ",{"type":55,"tag":82,"props":241,"children":243},{"className":242},[],[244],{"type":61,"value":245},"convert-to-usd",{"type":61,"value":247},", then run\n",{"type":55,"tag":82,"props":249,"children":251},{"className":250},[],[252],{"type":61,"value":253},"validate-usd-minimum",{"type":61,"value":255}," on the generated USD if conversion succeeds.",{"type":55,"tag":71,"props":257,"children":258},{},[259,261,267,269,275,277,283],{"type":61,"value":260},"Do not imply that ",{"type":55,"tag":82,"props":262,"children":264},{"className":263},[],[265],{"type":61,"value":266},"uv sync",{"type":61,"value":268}," installs every source converter runtime. URDF,\nMuJoCo\u002FMJCF, and the repo Python dependencies are handled by the project\nenvironment, but NVIDIA-backed source conversion requires an installed and\nvalidated ",{"type":55,"tag":82,"props":270,"children":272},{"className":271},[],[273],{"type":61,"value":274},"NVIDIA-Omniverse\u002Fusd-convert-cad",{"type":61,"value":276}," checkout. 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Do not flatten those references or\npromote nested README references to sibling ",{"type":55,"tag":82,"props":996,"children":998},{"className":997},[],[999],{"type":61,"value":1000},"SKILL.md",{"type":61,"value":1002}," files unless the repo's\npublishing model changes.",{"type":55,"tag":64,"props":1004,"children":1006},{"id":1005},"limitations",[1007],{"type":61,"value":1008},"Limitations",{"type":55,"tag":104,"props":1010,"children":1011},{},[1012,1017,1022],{"type":55,"tag":108,"props":1013,"children":1014},{},[1015],{"type":61,"value":1016},"This workflow coordinates existing conversion, property assignment,\nconformance, validation, rendering, and packaging skills; it does not replace\nthem with a single monolithic runner command.",{"type":55,"tag":108,"props":1018,"children":1019},{},[1020],{"type":61,"value":1021},"Stop at the first failing deployment, conversion, property-assignment, or\nconformance authoring gate unless the user explicitly asks for best-effort\ncontinuation.",{"type":55,"tag":108,"props":1023,"children":1024},{},[1025,1027,1032],{"type":61,"value":1026},"Upstream ",{"type":55,"tag":82,"props":1028,"children":1030},{"className":1029},[],[1031],{"type":61,"value":717},{"type":61,"value":1033}," needs visual\nreview or explicit grasp points before it can author a meaningful grasp\nvector.",{"type":55,"tag":64,"props":1035,"children":1037},{"id":1036},"troubleshooting",[1038],{"type":61,"value":1039},"Troubleshooting",{"type":55,"tag":1041,"props":1042,"children":1043},"table",{},[1044,1068],{"type":55,"tag":1045,"props":1046,"children":1047},"thead",{},[1048],{"type":55,"tag":1049,"props":1050,"children":1051},"tr",{},[1052,1058,1063],{"type":55,"tag":1053,"props":1054,"children":1055},"th",{},[1056],{"type":61,"value":1057},"Symptom",{"type":55,"tag":1053,"props":1059,"children":1060},{},[1061],{"type":61,"value":1062},"Cause",{"type":55,"tag":1053,"props":1064,"children":1065},{},[1066],{"type":61,"value":1067},"Fix",{"type":55,"tag":1069,"props":1070,"children":1071},"tbody",{},[1072,1116,1155,1180,1204,1237,1277],{"type":55,"tag":1049,"props":1073,"children":1074},{},[1075,1081,1097],{"type":55,"tag":1076,"props":1077,"children":1078},"td",{},[1079],{"type":61,"value":1080},"Downstream reference reports that cad-to-simready preflight has not prepared a component",{"type":55,"tag":1076,"props":1082,"children":1083},{},[1084,1089,1091],{"type":55,"tag":82,"props":1085,"children":1087},{"className":1086},[],[1088],{"type":61,"value":142},{"type":61,"value":1090}," is set, but the manifest is missing or the required runtime\u002Fservice is not ",{"type":55,"tag":82,"props":1092,"children":1094},{"className":1093},[],[1095],{"type":61,"value":1096},"ready",{"type":55,"tag":1076,"props":1098,"children":1099},{},[1100,1101,1107,1109,1114],{"type":61,"value":424},{"type":55,"tag":82,"props":1102,"children":1104},{"className":1103},[],[1105],{"type":61,"value":1106},"preflight\u002Fscripts\u002Fpreflight.py",{"type":61,"value":1108},", source the generated env file, or explicitly disable service deployment with ",{"type":55,"tag":82,"props":1110,"children":1112},{"className":1111},[],[1113],{"type":61,"value":328},{"type":61,"value":1115}," only when Content Agents are out of scope.",{"type":55,"tag":1049,"props":1117,"children":1118},{},[1119,1131,1136],{"type":55,"tag":1076,"props":1120,"children":1121},{},[1122,1124,1129],{"type":61,"value":1123},"Workflow stops on ",{"type":55,"tag":82,"props":1125,"children":1127},{"className":1126},[],[1128],{"type":61,"value":751},{"type":61,"value":1130}," and reports the failure as unclassified",{"type":55,"tag":1076,"props":1132,"children":1133},{},[1134],{"type":61,"value":1135},"Visual evidence or explicit grasp points were not provided to FET005",{"type":55,"tag":1076,"props":1137,"children":1138},{},[1139,1141,1146,1148,1153],{"type":61,"value":1140},"Run upstream ",{"type":55,"tag":82,"props":1142,"children":1144},{"className":1143},[],[1145],{"type":61,"value":717},{"type":61,"value":1147}," only after a vision-capable agent has reviewed the asset, or pass explicit grasp points. Otherwise report the FET005 step as ",{"type":55,"tag":82,"props":1149,"children":1151},{"className":1150},[],[1152],{"type":61,"value":815},{"type":61,"value":1154},", not failed.",{"type":55,"tag":1049,"props":1156,"children":1157},{},[1158,1163,1168],{"type":55,"tag":1076,"props":1159,"children":1160},{},[1161],{"type":61,"value":1162},"Validation fails after a meaningful USD artifact already exists",{"type":55,"tag":1076,"props":1164,"children":1165},{},[1166],{"type":61,"value":1167},"Workflow stopped at the first validation finding",{"type":55,"tag":1076,"props":1169,"children":1170},{},[1171,1173,1178],{"type":61,"value":1172},"Continue remaining diagnostic gates and mark the result ",{"type":55,"tag":82,"props":1174,"children":1176},{"className":1175},[],[1177],{"type":61,"value":830},{"type":61,"value":1179},". Do not stop at validation findings once a USD artifact has been produced.",{"type":55,"tag":1049,"props":1181,"children":1182},{},[1183,1188,1193],{"type":55,"tag":1076,"props":1184,"children":1185},{},[1186],{"type":61,"value":1187},"Property-assignment stage fails with a missing service endpoint",{"type":55,"tag":1076,"props":1189,"children":1190},{},[1191],{"type":61,"value":1192},"Content Agents service was not deployed before conversion",{"type":55,"tag":1076,"props":1194,"children":1195},{},[1196,1197,1202],{"type":61,"value":424},{"type":55,"tag":82,"props":1198,"children":1200},{"className":1199},[],[1201],{"type":61,"value":360},{"type":61,"value":1203}," first. Do not start asset inspection, conversion, validation, conformance, rendering, or packaging before Content Agents readiness when property assignment will run.",{"type":55,"tag":1049,"props":1205,"children":1206},{},[1207,1220,1225],{"type":55,"tag":1076,"props":1208,"children":1209},{},[1210,1212,1218],{"type":61,"value":1211},"Material Agent reports that rendering produced ",{"type":55,"tag":82,"props":1213,"children":1215},{"className":1214},[],[1216],{"type":61,"value":1217},"0 images",{"type":61,"value":1219}," after unit or profile repair",{"type":55,"tag":1076,"props":1221,"children":1222},{},[1223],{"type":61,"value":1224},"A FET repair, commonly FET001 unit normalization, was applied before Material Agent and changed the USD layering\u002Fscene state consumed by the service",{"type":55,"tag":1076,"props":1226,"children":1227},{},[1228,1230,1235],{"type":61,"value":1229},"Rerun assignment from the converted\u002Fminimum-valid USD: Material Agent first, then Physics Agent, then run ",{"type":55,"tag":82,"props":1231,"children":1233},{"className":1232},[],[1234],{"type":61,"value":550},{"type":61,"value":1236}," and FET repairs on the latest service-authored USD.",{"type":55,"tag":1049,"props":1238,"children":1239},{},[1240,1251,1256],{"type":55,"tag":1076,"props":1241,"children":1242},{},[1243,1245],{"type":61,"value":1244},"Material or Physics Agent local optimized path reports ",{"type":55,"tag":82,"props":1246,"children":1248},{"className":1247},[],[1249],{"type":61,"value":1250},"Permission denied: '\u002Fapp\u002F.build-resources\u002Fscene_optimizer_core\u002Fpython'",{"type":55,"tag":1076,"props":1252,"children":1253},{},[1254],{"type":61,"value":1255},"Local Docker Scene Optimizer bundle permissions prevent the non-root service user from reading the packaged SO runtime",{"type":55,"tag":1076,"props":1257,"children":1258},{},[1259,1261,1267,1269,1275],{"type":61,"value":1260},"Repair the relevant local container with ",{"type":55,"tag":82,"props":1262,"children":1264},{"className":1263},[],[1265],{"type":61,"value":1266},"docker exec --user root content-material-agent-service chmod -R a+rX \u002Fapp\u002F.build-resources\u002Fscene_optimizer_core",{"type":61,"value":1268}," or ",{"type":55,"tag":82,"props":1270,"children":1272},{"className":1271},[],[1273],{"type":61,"value":1274},"docker exec --user root content-physics-agent-service chmod -R a+rX \u002Fapp\u002F.build-resources\u002Fscene_optimizer_core",{"type":61,"value":1276},", then rerun the same optimized agent command. Do not treat the no-optimizer fallback as the root cause for instanced\u002Fprototype assets.",{"type":55,"tag":1049,"props":1278,"children":1279},{},[1280,1290,1302],{"type":55,"tag":1076,"props":1281,"children":1282},{},[1283,1288],{"type":55,"tag":82,"props":1284,"children":1286},{"className":1285},[],[1287],{"type":61,"value":766},{"type":61,"value":1289}," fails even though the asset has many prims",{"type":55,"tag":1076,"props":1291,"children":1292},{},[1293,1295,1300],{"type":61,"value":1294},"The profile counts ",{"type":55,"tag":82,"props":1296,"children":1298},{"className":1297},[],[1299],{"type":61,"value":781},{"type":61,"value":1301}," prims, not visual or collider prims; Physics Agent may author one root rigid body",{"type":55,"tag":1076,"props":1303,"children":1304},{},[1305,1307,1312],{"type":61,"value":1306},"Route to upstream ",{"type":55,"tag":82,"props":1308,"children":1310},{"className":1309},[],[1311],{"type":61,"value":710},{"type":61,"value":1313},". First ensure Physics Agent used composed-topology optimization when applicable, then promote existing component colliders\u002Fpart roots when the active profile reports FET004\u002FRB.MB.001 and no geometry must be invented.",{"type":55,"tag":64,"props":1315,"children":1317},{"id":1316},"hard-rules",[1318],{"type":61,"value":1319},"Hard Rules",{"type":55,"tag":104,"props":1321,"children":1322},{},[1323,1335,1340,1352,1364,1369,1381,1399,1403,1414,1433],{"type":55,"tag":108,"props":1324,"children":1325},{},[1326,1328,1333],{"type":61,"value":1327},"Prefer the preflight manifest for local upstream roots, converter\nexecutables, SimReady validation runtime, OVRTX endpoint, and Content Agents\nservice URLs. When ",{"type":55,"tag":82,"props":1329,"children":1331},{"className":1330},[],[1332],{"type":61,"value":142},{"type":61,"value":1334}," is set, do not bypass the\nmanifest with direct upstream discovery.",{"type":55,"tag":108,"props":1336,"children":1337},{},[1338],{"type":61,"value":1339},"Do not run asset inspection, converter probes, local upstream builds,\nconversion, validation, conformance, rendering, or packaging before Content\nAgents readiness when property assignment will run.",{"type":55,"tag":108,"props":1341,"children":1342},{},[1343,1345,1350],{"type":61,"value":1344},"Use stage-specific installed reference scripts directly. Do not add or call a\nsingle ",{"type":55,"tag":82,"props":1346,"children":1348},{"className":1347},[],[1349],{"type":61,"value":4},{"type":61,"value":1351}," runner command.",{"type":55,"tag":108,"props":1353,"children":1354},{},[1355,1357,1362],{"type":61,"value":1356},"For source conversion, delegate to the ",{"type":55,"tag":82,"props":1358,"children":1360},{"className":1359},[],[1361],{"type":61,"value":245},{"type":61,"value":1363}," reference; do not\nsubstitute another converter for CAD or mesh formats.",{"type":55,"tag":108,"props":1365,"children":1366},{},[1367],{"type":61,"value":1368},"For property assignment, use Content Agents references as separate atomic steps:\nmaterial first, then physics, then texture only when requested.",{"type":55,"tag":108,"props":1370,"children":1371},{},[1372,1374,1379],{"type":61,"value":1373},"When property assignment will run, do not run ",{"type":55,"tag":82,"props":1375,"children":1377},{"className":1376},[],[1378],{"type":61,"value":550},{"type":61,"value":1380}," or\nany FET helper before Content Agents. Validate minimum USD first, then run\nContent Agents on that converted\u002Fminimum-valid USD, then apply FET repairs to\nthe latest service-authored USD.",{"type":55,"tag":108,"props":1382,"children":1383},{},[1384,1385,1390,1392,1397],{"type":61,"value":1373},{"type":55,"tag":82,"props":1386,"children":1388},{"className":1387},[],[1389],{"type":61,"value":602},{"type":61,"value":1391}," or any\nSimReady profile validation before Content Agents. The only validation gate\nallowed before service calls is ",{"type":55,"tag":82,"props":1393,"children":1395},{"className":1394},[],[1396],{"type":61,"value":253},{"type":61,"value":1398},", which is a basic USD\nviability check.",{"type":55,"tag":108,"props":1400,"children":1401},{},[1402],{"type":61,"value":1021},{"type":55,"tag":108,"props":1404,"children":1405},{},[1406,1408,1413],{"type":61,"value":1407},"Do not stop at validation findings after a meaningful USD artifact exists.\nContinue remaining diagnostic gates and mark the result ",{"type":55,"tag":82,"props":1409,"children":1411},{"className":1410},[],[1412],{"type":61,"value":830},{"type":61,"value":202},{"type":55,"tag":108,"props":1415,"children":1416},{},[1417,1419,1424,1426,1431],{"type":61,"value":1418},"Do not leave a ",{"type":55,"tag":82,"props":1420,"children":1422},{"className":1421},[],[1423],{"type":61,"value":751},{"type":61,"value":1425}," profile failure as an unclassified final finding.\nRoute it to upstream ",{"type":55,"tag":82,"props":1427,"children":1429},{"className":1428},[],[1430],{"type":61,"value":717},{"type":61,"value":1432},"; if\nthe current agent cannot inspect renders or no explicit grasp points are\navailable, report a blocked FET005 repair with the visual evidence path or\nmissing input reason.",{"type":55,"tag":108,"props":1434,"children":1435},{},[1436],{"type":61,"value":1437},"Preserve every stage report and pass the concrete output USD path from each\nreport into the next stage.",{"items":1439,"total":1642},[1440,1461,1484,1501,1517,1536,1555,1569,1585,1599,1611,1626],{"slug":1441,"name":1441,"fn":1442,"description":1443,"org":1444,"tags":1445,"stars":1458,"repoUrl":1459,"updatedAt":1460},"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},[1446,1449,1452,1455],{"name":1447,"slug":1448,"type":15},"Documents","documents",{"name":1450,"slug":1451,"type":15},"Healthcare","healthcare",{"name":1453,"slug":1454,"type":15},"Insurance","insurance",{"name":1456,"slug":1457,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":1462,"name":1462,"fn":1463,"description":1464,"org":1465,"tags":1466,"stars":1481,"repoUrl":1482,"updatedAt":1483},"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},[1467,1470,1472,1475,1478],{"name":1468,"slug":1469,"type":15},".NET","dotnet",{"name":1471,"slug":1462,"type":15},"ASP.NET Core",{"name":1473,"slug":1474,"type":15},"Blazor","blazor",{"name":1476,"slug":1477,"type":15},"C#","csharp",{"name":1479,"slug":1480,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":1485,"name":1485,"fn":1486,"description":1487,"org":1488,"tags":1489,"stars":1481,"repoUrl":1482,"updatedAt":1500},"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},[1490,1493,1496,1499],{"name":1491,"slug":1492,"type":15},"Apps SDK","apps-sdk",{"name":1494,"slug":1495,"type":15},"ChatGPT","chatgpt",{"name":1497,"slug":1498,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":1502,"name":1502,"fn":1503,"description":1504,"org":1505,"tags":1506,"stars":1481,"repoUrl":1482,"updatedAt":1516},"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},[1507,1510,1513],{"name":1508,"slug":1509,"type":15},"API Development","api-development",{"name":1511,"slug":1512,"type":15},"CLI","cli",{"name":1514,"slug":1515,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":1518,"name":1518,"fn":1519,"description":1520,"org":1521,"tags":1522,"stars":1481,"repoUrl":1482,"updatedAt":1535},"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},[1523,1526,1529,1532],{"name":1524,"slug":1525,"type":15},"Cloudflare","cloudflare",{"name":1527,"slug":1528,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":1530,"slug":1531,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":1533,"slug":1534,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":1537,"name":1537,"fn":1538,"description":1539,"org":1540,"tags":1541,"stars":1481,"repoUrl":1482,"updatedAt":1554},"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},[1542,1545,1548,1551],{"name":1543,"slug":1544,"type":15},"Productivity","productivity",{"name":1546,"slug":1547,"type":15},"Project Management","project-management",{"name":1549,"slug":1550,"type":15},"Strategy","strategy",{"name":1552,"slug":1553,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":1556,"name":1556,"fn":1557,"description":1558,"org":1559,"tags":1560,"stars":1481,"repoUrl":1482,"updatedAt":1568},"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},[1561,1562,1564,1567],{"name":20,"slug":21,"type":15},{"name":1563,"slug":1556,"type":15},"Figma",{"name":1565,"slug":1566,"type":15},"Frontend","frontend",{"name":1497,"slug":1498,"type":15},"2026-04-12T05:06:47.939943",{"slug":1570,"name":1570,"fn":1571,"description":1572,"org":1573,"tags":1574,"stars":1481,"repoUrl":1482,"updatedAt":1584},"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},[1575,1576,1579,1580,1581],{"name":20,"slug":21,"type":15},{"name":1577,"slug":1578,"type":15},"Design System","design-system",{"name":1563,"slug":1556,"type":15},{"name":1565,"slug":1566,"type":15},{"name":1582,"slug":1583,"type":15},"UI Components","ui-components","2026-05-10T05:59:52.971881",{"slug":1586,"name":1586,"fn":1587,"description":1588,"org":1589,"tags":1590,"stars":1481,"repoUrl":1482,"updatedAt":1598},"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},[1591,1592,1593,1596,1597],{"name":20,"slug":21,"type":15},{"name":1577,"slug":1578,"type":15},{"name":1594,"slug":1595,"type":15},"Documentation","documentation",{"name":1563,"slug":1556,"type":15},{"name":1565,"slug":1566,"type":15},"2026-05-16T06:07:47.821474",{"slug":1600,"name":1600,"fn":1601,"description":1602,"org":1603,"tags":1604,"stars":1481,"repoUrl":1482,"updatedAt":1610},"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},[1605,1606,1607,1608,1609],{"name":20,"slug":21,"type":15},{"name":1563,"slug":1556,"type":15},{"name":1565,"slug":1566,"type":15},{"name":1582,"slug":1583,"type":15},{"name":1479,"slug":1480,"type":15},"2026-05-16T06:07:40.583615",{"slug":1612,"name":1612,"fn":1613,"description":1614,"org":1615,"tags":1616,"stars":1481,"repoUrl":1482,"updatedAt":1625},"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},[1617,1620,1621,1624],{"name":1618,"slug":1619,"type":15},"Animation","animation",{"name":1514,"slug":1515,"type":15},{"name":1622,"slug":1623,"type":15},"Creative","creative",{"name":20,"slug":21,"type":15},"2026-05-02T05:31:48.48485",{"slug":1627,"name":1627,"fn":1628,"description":1629,"org":1630,"tags":1631,"stars":1481,"repoUrl":1482,"updatedAt":1641},"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},[1632,1633,1634,1637,1640],{"name":1622,"slug":1623,"type":15},{"name":20,"slug":21,"type":15},{"name":1635,"slug":1636,"type":15},"Image Generation","image-generation",{"name":1638,"slug":1639,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675,{"items":1644,"total":1757},[1645,1661,1677,1689,1707,1725,1745],{"slug":1646,"name":1646,"fn":1647,"description":1648,"org":1649,"tags":1650,"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},[1651,1654,1657,1660],{"name":1652,"slug":1653,"type":15},"Accessibility","accessibility",{"name":1655,"slug":1656,"type":15},"Charts","charts",{"name":1658,"slug":1659,"type":15},"Data Visualization","data-visualization",{"name":20,"slug":21,"type":15},{"slug":1662,"name":1662,"fn":1663,"description":1664,"org":1665,"tags":1666,"stars":22,"repoUrl":23,"updatedAt":1676},"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},[1667,1670,1673],{"name":1668,"slug":1669,"type":15},"Agents","agents",{"name":1671,"slug":1672,"type":15},"Browser Automation","browser-automation",{"name":1674,"slug":1675,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":1678,"name":1678,"fn":1679,"description":1680,"org":1681,"tags":1682,"stars":22,"repoUrl":23,"updatedAt":1688},"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},[1683,1684,1687],{"name":1671,"slug":1672,"type":15},{"name":1685,"slug":1686,"type":15},"Local Development","local-development",{"name":1674,"slug":1675,"type":15},"2026-04-06T18:41:17.526867",{"slug":1690,"name":1690,"fn":1691,"description":1692,"org":1693,"tags":1694,"stars":22,"repoUrl":23,"updatedAt":1706},"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},[1695,1696,1697,1700,1703],{"name":1668,"slug":1669,"type":15},{"name":1530,"slug":1531,"type":15},{"name":1698,"slug":1699,"type":15},"SDK","sdk",{"name":1701,"slug":1702,"type":15},"Serverless","serverless",{"name":1704,"slug":1705,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":1708,"name":1708,"fn":1709,"description":1710,"org":1711,"tags":1712,"stars":22,"repoUrl":23,"updatedAt":1724},"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},[1713,1714,1717,1720,1721],{"name":1565,"slug":1566,"type":15},{"name":1715,"slug":1716,"type":15},"React","react",{"name":1718,"slug":1719,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":1582,"slug":1583,"type":15},{"name":1722,"slug":1723,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":1726,"name":1726,"fn":1727,"description":1728,"org":1729,"tags":1730,"stars":22,"repoUrl":23,"updatedAt":1744},"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},[1731,1734,1737,1740,1743],{"name":1732,"slug":1733,"type":15},"AI Infrastructure","ai-infrastructure",{"name":1735,"slug":1736,"type":15},"Cost Optimization","cost-optimization",{"name":1738,"slug":1739,"type":15},"LLM","llm",{"name":1741,"slug":1742,"type":15},"Performance","performance",{"name":1722,"slug":1723,"type":15},"2026-04-06T18:40:44.377464",{"slug":1746,"name":1746,"fn":1747,"description":1748,"org":1749,"tags":1750,"stars":22,"repoUrl":23,"updatedAt":1756},"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},[1751,1752,1755],{"name":1735,"slug":1736,"type":15},{"name":1753,"slug":1754,"type":15},"Database","database",{"name":1738,"slug":1739,"type":15},"2026-04-06T18:41:08.513425",600]