[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PSNDN9STT71KJDHX47XNWN":3,"skill-same:01M6PSNDN9STT71KJDHX47XNWN":95},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":14,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6PSNDN9STT71KJDHX47XNWN","tao-train-single-step",null,"Standard single-step train\u002Feval\u002Fexport workflow for any TAO model. Use when training a TAO model on a dataset without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include \"single train run\", \"train then evaluate then export\", \"plain TAO training\", \"normal training\", \"no AutoML\", \"skip the loop\". Routes through the per-model SKILL.md for action specifics and through `tao-launch-workflow` for platform\u002Fcredentials\u002Fdataset intake.","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-train-single-step\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"tao train single step","single","tao","train","container","generic","single-step","for","trigger","platform","docker","use","eval","model","loop","NVIDIA","intake","training","workflows","workflow","2026-11-10 10:12:30",false,"---\nname: tao-train-single-step\ndescription: Standard single-step train\u002Feval\u002Fexport workflow for any TAO model. Use when training a TAO model on a dataset\n  without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include \"single train run\", \"train then evaluate\n  then export\", \"plain TAO training\", \"normal training\", \"no AutoML\", \"skip the loop\". Routes through the per-model SKILL.md\n  for action specifics and through `tao-launch-workflow` for platform\u002Fcredentials\u002Fdataset intake.\nlicense: Apache-2.0\ncompatibility: Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.\nmetadata:\n  author: NVIDIA Corporation\n  version: \"0.1.0\"\nallowed-tools: Read Bash Write\ntags:\n- training\n- single-step\n- generic\n---\n\n# Normal Train\n\nStandard supervised fine-tuning: train a model on a labeled dataset, optionally evaluate, then optionally export. The most common TAO workflow for adapting a pretrained model to a new dataset.\n\n## Steps\n\n1. **train** — executed through AutoML when the selected model has\n   `automl_enabled: true` and `automl_policy` is `auto`; set\n   `automl_policy=off` for a plain single training run\n2. **eval** — executed if `eval_dataset_uri` is resolved\n3. **export** — optional, on user request after training\n\n## Prerequisites\n\n### Required\n- **model**: A compatible TAO model (e.g., clip, nvdinov2, grounding_dino)\n- **train_dataset_uri**: URI of the training dataset (e.g., `s3:\u002F\u002Fbucket\u002Ftrain\u002F`)\n- **platform**: Ask from the generated supported-platform list:\n  `${TAO_SKILL_BANK_PATH:-~\u002Ftao-skills-external}\u002Fscripts\u002Flist_tao_platforms.py --format text`\n- **container image confirmation**: resolve the default image from the selected\n  model\u002Faction config, show it to the user, and require confirmation or\n  `image=\u003Coverride>` before creating runner files or submitting training.\n\n### Optional\n- **eval_dataset_uri**: Some model skills mark this as required — check the resolved model skill before treating it as optional.\n- **base_checkpoint**: If not provided, defaults to the NGC pretrained checkpoint listed in the model skill, or trains from scratch if no NGC checkpoint exists.\n- **automl_policy**: `auto` by default; set `off` to bypass model-level AutoML for this run while leaving model metadata unchanged.\n- **image override**: Use `image=\u003Coverride>` to pin a specific TAO toolkit build\n  after reviewing the resolved default.\n\n## Launch Intake\n\nAfter the user confirms they want this standard train\u002Feval\u002Fexport workflow,\nask which supported platform they intend to run on. Generate the choices with\n`scripts\u002Flist_tao_platforms.py --format text`; do not scan platform docs or\nfolders.\n\nBefore creating a plain train runner, inspect the selected model's metadata\nwith `scripts\u002Flist_tao_models.py --scope automl --format json` or read\n`skills\u002Fmodels\u002F\u003Cnetwork>\u002Freferences\u002Fskill_info.yaml`. If `automl_enabled` is true and\nthe helper reports a valid train schema for that model, route the train stage\nthrough `skills\u002Fapplications\u002Ftao-run-automl` by default. Only stay on the plain train path\nwhen `automl_policy=off`, the user explicitly asks for no HPO\u002FAutoML, or AutoML\nis enabled but not runnable because the model's train schema is not packaged\nyet.\n\nAlso ask whether long-running monitoring should stay enabled and how many\nminutes between status updates. Defaults: enabled, 5 minutes.\n\nAfter the model\u002Faction are known, run `scripts\u002Fresolve_tao_image.py --model\n\u003Cnetwork> --action train --format text` and ask whether to use the resolved\nimage or an `image=\u003Coverride>`. Do not create the tao-train-single-step runner until the\nimage is confirmed.\n\nAfter platform selection, run\n`scripts\u002Flist_tao_platforms.py --platform \u003Cplatform> --format text` and ask\nonly for credentials relevant to that platform, plus any selected-model\ncredentials. Do not ask for unrelated platform credentials.\n","# Normal Train\n\nStandard supervised fine-tuning: train a model on a labeled dataset, optionally evaluate, then optionally export. The most common TAO workflow for adapting a pretrained model to a new dataset.\n\n## Steps\n\n1. **train** — executed through AutoML when the selected model has\n   `automl_enabled: true` and `automl_policy` is `auto`; set\n   `automl_policy=off` for a plain single training run\n2. **eval** — executed if `eval_dataset_uri` is resolved\n3. **export** — optional, on user request after training\n\n## Prerequisites\n\n### Required\n- **model**: A compatible TAO model (e.g., clip, nvdinov2, grounding_dino)\n- **train_dataset_uri**: URI of the training dataset (e.g., `s3:\u002F\u002Fbucket\u002Ftrain\u002F`)\n- **platform**: Ask from the generated supported-platform list:\n  `${TAO_SKILL_BANK_PATH:-~\u002Ftao-skills-external}\u002Fscripts\u002Flist_tao_platforms.py --format text`\n- **container image confirmation**: resolve the default image from the selected\n  model\u002Faction config, show it to the user, and require confirmation or\n  `image=\u003Coverride>` before creating runner files or submitting training.\n\n### Optional\n- **eval_dataset_uri**: Some model skills mark this as required — check the resolved model skill before treating it as optional.\n- **base_checkpoint**: If not provided, defaults to the NGC pretrained checkpoint listed in the model skill, or trains from scratch if no NGC checkpoint exists.\n- **automl_policy**: `auto` by default; set `off` to bypass model-level AutoML for this run while leaving model metadata unchanged.\n- **image override**: Use `image=\u003Coverride>` to pin a specific TAO toolkit build\n  after reviewing the resolved default.\n\n## Launch Intake\n\nAfter the user confirms they want this standard train\u002Feval\u002Fexport workflow,\nask which supported platform they intend to run on. Generate the choices with\n`scripts\u002Flist_tao_platforms.py --format text`; do not scan platform docs or\nfolders.\n\nBefore creating a plain train runner, inspect the selected model's metadata\nwith `scripts\u002Flist_tao_models.py --scope automl --format json` or read\n`skills\u002Fmodels\u002F\u003Cnetwork>\u002Freferences\u002Fskill_info.yaml`. If `automl_enabled` is true and\nthe helper reports a valid train schema for that model, route the train stage\nthrough `skills\u002Fapplications\u002Ftao-run-automl` by default. Only stay on the plain train path\nwhen `automl_policy=off`, the user explicitly asks for no HPO\u002FAutoML, or AutoML\nis enabled but not runnable because the model's train schema is not packaged\nyet.\n\nAlso ask whether long-running monitoring should stay enabled and how many\nminutes between status updates. Defaults: enabled, 5 minutes.\n\nAfter the model\u002Faction are known, run `scripts\u002Fresolve_tao_image.py --model\n\u003Cnetwork> --action train --format text` and ask whether to use the resolved\nimage or an `image=\u003Coverride>`. Do not create the tao-train-single-step runner until the\nimage is confirmed.\n\nAfter platform selection, run\n`scripts\u002Flist_tao_platforms.py --platform \u003Cplatform> --format text` and ask\nonly for credentials relevant to that platform, plus any selected-model\ncredentials. Do not ask for unrelated platform credentials.\n",{"name":5,"tags":44,"license":15,"metadata":45,"description":7,"allowed-tools":48,"compatibility":49},[36,25,24],{"author":46,"version":47},"NVIDIA Corporation","0.1.0","Read Bash Write","Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".claude\u002Fskills\u002Ftao-train-single-step","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui","https:\u002F\u002Fneuroads.com.br","2026-08-12 00:42:44",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-train-single-step",{"tool":63,"command":64},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-train-single-step",{"tool":66,"command":67},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-train-single-step","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-train-single-step\u002FSKILL.md",true,[71,77,81,85,89],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"BENCHMARK.md","REFERENCE","参考文档",4379,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-train-single-step\u002FBENCHMARK.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"evals\u002Fevals.json",803,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-train-single-step\u002Fevals\u002Fevals.json",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fskill_info.yaml",569,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-train-single-step\u002Freferences\u002Fskill_info.yaml",{"path":86,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":87,"sourceHtmlUrl":88},"skill-card.md",3713,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-train-single-step\u002Fskill-card.md",{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"skill.oms.sig","OTHER","其他",4833,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-train-single-step\u002Fskill.oms.sig",[96,122,146,172,190,224],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":111,"publishedAt":39},"01M6PTDCZTXTGSTX4RFK901YSZ","xunit-skill","Generates xUnit.net tests in C#. Covers Fact\u002FTheory, constructor injection, IClassFixture, and FluentAssertions. Use when user mentions \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal\", \"C# xUnit\". Triggers on: \"xUnit\", \"[Fact]\", \"[Theory]\", \"Assert.Equal C#\", \"xUnit.net\".","MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[103,108],{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000023","Skill与智能体开发",[112,113,114,115,116,30,117,118,119,120,121],"xunit","xunit skill","testing","injection","user","tests","triggers","unit","unit-testing","C#",{"skillNo":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":130,"publishedAt":39},"01M6PTCB59ZTG5FTMWGPYEMMBP","xcuitest-skill","Generates XCUITest UI tests for iOS\u002FiPadOS apps in Swift. Apple's native testing framework for reliable, fast UI automation. Supports local simulators and TestMu AI cloud real devices. Use when user mentions \"XCUITest\", \"XCTest\", \"iOS UI test\", \"Swift test\", \"XCUIApplication\". Triggers on: \"XCUITest\", \"XCTest UI\", \"iOS UI test\", \"Swift UI test\", \"XCUIApplication\", \"TestMu\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxcuitest-skill\u002FSKILL.md",[128,129],{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[131,132,114,133,134,135,136,137,30,138,139,140,141,142,117,118,143,144,26,145],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","objective",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":156,"publishedAt":39},"01M6PTATMNTY9NF4J7KSR76YK6","world-builder","Create and manage generative game zones for Dorothy's Pokemon-style world. Use this skill when creating, updating, or designing game worlds via MCP tools.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fworld-builder\u002FSKILL.md",[152,155],{"categoryNo":153,"categoryName":154,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[157,158,159,160,161,162,163,164,30,165,166,167,168,169,170,26,171],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":180,"publishedAt":39},"01M6PT9Z92JTSQEHE1QR0NNW5F","webdriverio-skill","Generates WebdriverIO (WDIO) automation tests in JavaScript or TypeScript. Supports local and TestMu AI cloud. Use when user mentions \"WebdriverIO\", \"WDIO\", \"wdio.conf\", \"browser.url\", \"$\", \"$$\". Triggers on: \"WebdriverIO\", \"WDIO\", \"wdio\", \"browser.$\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwebdriverio-skill\u002FSKILL.md",[178,179],{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[181,114,182,133,183,184,185,186,116,30,187,141,117,118,188,189],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":194,"categories":195,"keywords":205,"publishedAt":39},"01M6PT9NVK36ZWCQ69YXK3FR4K","web-design-guidelines","Web platform design and accessibility guidelines. Use when building web interfaces, auditing accessibility, implementing responsive layouts, or reviewing web UI code. Triggers on tasks involving HTML, CSS, web components, WCAG compliance, responsive design, or web performance.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fweb\u002FSKILL.md",[196,199,200],{"categoryNo":197,"categoryName":198,"taxonomyType":106,"taxonomyTypeDesc":107},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":153,"categoryName":154,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":201,"categoryName":202,"taxonomyType":203,"taxonomyTypeDesc":204},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,28,118,223],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","implementing",{"skillNo":225,"name":226,"nameZh":6,"description":227,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":100,"licenseFileUrl":6,"sourceHtmlUrl":228,"categories":229,"keywords":232,"publishedAt":39},"01M6PT9A3JKRE3X34276F2FW5R","watchos-design-guidelines","Apple Human Interface Guidelines for Apple Watch. Use when building watchOS apps, complications, or workout features. Triggers on tasks involving Watch UI, Digital Crown, glanceable interfaces, or wrist-based interactions.","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fwatchos\u002FSKILL.md",[230,231],{"categoryNo":153,"categoryName":154,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":201,"categoryName":202,"taxonomyType":203,"taxonomyTypeDesc":204},[206,208,233,215,218,30,234,235,142,28,236,118,143,237,26,238,239],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]