[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71Q24B9EGTHH28TFMGHSFE1":3,"skill-same:01M71Q24B9EGTHH28TFMGHSFE1":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":54,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M71Q24B9EGTHH28TFMGHSFE1","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.","pattanation\u002FNorvik","pattanation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235419268?v=4","01M71M5THHYYB15ME8R2E4FCZG","Patta","01M71M5TJM1NDM992M8PDKYR2K",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\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],"single","tao","train","tao train single step","workflows","loop","use","container","eval","intake","trigger","docker","training","for","normal","model","single-step","platform","generic","NVIDIA","2026-11-10 13:02:02",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 `on`; 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**: `on` by default; set `off` to bypass model-level AutoML for this run while leaving model metadata unchanged. Use only `on` \u002F `off` in new launch settings.\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 `on`; 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**: `on` by default; set `off` to bypass model-level AutoML for this run while leaving model metadata unchanged. Use only `on` \u002F `off` in new launch settings.\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},[31,35,37],{"author":46,"version":47},"NVIDIA Corporation","0.1.0","Read Bash Write","Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.","claude\u002Finstall-gsd-build-foQdV","d86cd127f527124b5fd0fd0bc679a4d79b7d01d7",".agents\u002Fskills\u002Ftao-train-single-step","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik","https:\u002F\u002Fnorvik-orpin.vercel.app","https:\u002F\u002Fgithub.com\u002Fpattanation","2026-07-17 11:17:04",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add pattanation\u002FNorvik --skill tao-train-single-step",{"tool":63,"command":64},"bunx","bunx skills add pattanation\u002FNorvik --skill tao-train-single-step",{"tool":66,"command":67},"pnpm","pnpm dlx skills add pattanation\u002FNorvik --skill tao-train-single-step","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\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",3990,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Ftao-train-single-step\u002FBENCHMARK.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"evals\u002Fevals.json",814,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Ftao-train-single-step\u002Fevals\u002Fevals.json",{"path":82,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fskill_info.yaml",561,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Ftao-train-single-step\u002Freferences\u002Fskill_info.yaml",{"path":86,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":87,"sourceHtmlUrl":88},"skill-card.md",3588,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Ftao-train-single-step\u002Fskill-card.md",{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"skill.oms.sig","OTHER","其他",4829,"https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Ftao-train-single-step\u002Fskill.oms.sig",[96,128,152,176,198,213],{"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":15,"licenseFileUrl":100,"sourceHtmlUrl":101,"categories":102,"keywords":111,"publishedAt":39},"01M71QDV4DM8T19HEHWATEF1K4","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fwebapp-testing\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},"01JBSEEDCAT000000000000009","前端开发",[112,113,114,32,115,116,117,118,119,120,121,122,123,124,125,126,127],"webapp","testing","webapp testing","applications","local","frontend","debugging","toolkit","logs","browser","with","playwright","web","behavior","ui","screenshots",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":132,"sourceHtmlUrl":133,"categories":134,"keywords":136,"publishedAt":39},"01M71QDH2GGMXGA9105C9W1N7B","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fweb-artifacts-builder\u002FSKILL.md",[135],{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[137,138,139,124,140,141,142,32,143,144,117,19,25,145,146,147,148,149,150,151],"builder","artifacts builder","artifacts","web artifacts builder","single-file","css","Tailwind","ui components","file","components","react","multi","state","routing","state management",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":156,"licenseFileUrl":157,"sourceHtmlUrl":158,"categories":159,"keywords":161,"publishedAt":39},"01M71QD9T2XC3EWMQNN9ACNWQ1","vueuse-functions","Apply VueUse composables where appropriate to build concise, maintainable Vue.js \u002F Nuxt features.","MIT","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvueuse-functions\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvueuse-functions\u002FSKILL.md",[160],{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[162,163,164,165,166,167,168,169,170,171,172,173,174,175],"functions","vueuse","vueuse functions","project","nuxt","apply","github","concise","build","https","features","vue","js","composables",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":156,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":185,"publishedAt":39},"01M71QD51HWSGYMCMB7RW7PGK8","vue-testing-best-practices","Use for Vue.js testing. Covers Vitest, Vue Test Utils, component testing, mocking, testing patterns, and Playwright for E2E testing.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-testing-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-testing-best-practices\u002FSKILL.md",[183,184],{"categoryNo":104,"categoryName":105,"taxonomyType":106,"taxonomyTypeDesc":107},{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[186,113,187,173,188,123,189,190,191,168,192,32,193,194,195,25,196,174,197],"vue testing best practices","testing best practices","best practices","test","testing patterns","component","vitest","mocking","e2e","utils","patterns","e2e testing",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":156,"licenseFileUrl":202,"sourceHtmlUrl":203,"categories":204,"keywords":206,"publishedAt":39},"01M71QD358H2DJ9THJRBP77T3T","vue-router-best-practices","Vue Router 4 patterns, navigation guards, route params, and route-component lifecycle interactions.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-router-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-router-best-practices\u002FSKILL.md",[205],{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[207,173,208,188,209,191,210,168,196,211,212],"router","vue router best practices","route","guards","navigation","lifecycle",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":156,"licenseFileUrl":217,"sourceHtmlUrl":218,"categories":219,"keywords":221,"publishedAt":39},"01M71QCYPNWDE2MQZ1TF3ED5S4","vue-best-practices","MUST be used for Vue.js tasks. Strongly recommends Composition API with `\u003Cscript setup>` and TypeScript as the standard approach. Covers Vue 3, SSR, Volar, vue-tsc. Load for any Vue, .vue files, Vue Router, Pinia, or Vite with Vue work. ALWAYS use Composition API unless the project explicitly requires Options API.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-best-practices\u002FSKILL.md",[220],{"categoryNo":109,"categoryName":110,"taxonomyType":106,"taxonomyTypeDesc":107},[222,173,188,223,165,224,225,226,207,227,228,229,230,231,232,32,233,25,234,235],"vue best practices","Composition","files","tasks","api","pinia","work","options","ssr","typescript","as","vite","load","standard"]