[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PSBFVB2RGCNP2F6Y6ZPG5M":3,"skill-same:01M6PSBFVB2RGCNP2F6Y6ZPG5M":93},{"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":52,"commitSha":53,"skillDirectory":54,"forksCount":14,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6PSBFVB2RGCNP2F6Y6ZPG5M","tao-convert-dataset-format",null,"Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data. Use when the user asks to convert a DAFT dataset, change DAFT format, change a TAO dataset format, or run `tao-daft convert`.","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-convert-dataset-format\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"tao convert dataset format","tao","convert","format","dataset","conversion","nvidia-tao","cosmos-reason","for","pip","cosmos","do","python","use","Bash","NVIDIA","package","run","change","sdk","2026-11-10 10:12:30",false,"---\nname: tao-convert-dataset-format\ndescription: Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data.\n  Use when the user asks to convert a DAFT dataset, change DAFT format, change a TAO dataset format, or run `tao-daft convert`.\nlicense: Apache-2.0\ncompatibility: Requires Python 3.10+ and the nvidia-tao-sdk package (pip install nvidia-tao-daft).\nmetadata:\n  author: NVIDIA Corporation\n  version: \"1.0.0\"\nallowed-tools: Read Bash\ntags:\n- tao-daft\n- dataset\n- conversion\n- vlm\n- cosmos-reason\n---\n\n# Convert a TAO DAFT Dataset\n\n## Quick start\n\n```bash\ntao-daft convert \u003Csource-format> \u003Ctarget-format> --path \u003Cinput> --output \u003Coutput>\n```\n\nSource and target are positional subcommands; `--path` and `--output` are flags.\nDiscover the supported formats and per-pair flags from the leaf `--help`\n(see \"CLI conventions\" below).\n\n## Preflight\n```bash\npython -c \"import nvidia_tao_daft\" 2>\u002Fdev\u002Fnull || {\n  echo \"MISSING: tao-daft not installed. Run:\"\n  echo \"  pip install nvidia-tao-daft\"\n  exit 1\n}\n```\n\n## Quick Start\n\nDiscover the installed CLI surface before choosing format slugs, then run the\nleaf conversion command with explicit `--path` and `--output` flags:\n\n```bash\ntao-daft --version\ntao-daft convert --help\ntao-daft convert \u003Csource-format> --help\ntao-daft convert \u003Csource-format> \u003Ctarget-format> --path \u002Fpath\u002Fto\u002Fdaft --output \u002Fpath\u002Fto\u002Fconverted\n```\n\n## Purpose\n\nDrives `tao-daft convert` to transform a DAFT dataset (or a tree of\nthem) between supported formats. The CLI does the real work; the\nskill picks the right source\u002Ftarget pair and flags, then explains the\nresult.\n\nTrigger on: converting a DAFT dataset, packaging DAFT QA \u002F\nsummarization \u002F temporal tasks for VLM training, producing a\n`meta.json`-style training set, or the command `tao-daft convert`. Do\n**not** trigger for non-DAFT → DAFT conversion (COCO, YOLO, Data\nFactory JSONL) — redirect to the upstream `nvidia-tao-daft` repo's\nconverter skills.\n\nIf the user opens ambiguously, run a few `--help` calls first.\n\n## Prerequisites\n\n- `nvidia-tao-daft` installed (wheel only, not the source repo).\n  Confirm with `tao-daft --version`.\n- A DAFT dataset, or a parent directory containing many, on local\n  disk.\n\n## Instructions\n\n### CLI conventions\n\n`tao-daft` is nested argparse subcommands. The conventions below are\nstable across versions even when format names or flags change, so\n**always discover the current surface from `--help`** rather than\nrelying on names this doc happens to mention.\n\n1. **Source and target are both positional subcommands**, not\n   `--from`\u002F`--to`: `tao-daft convert \u003Csource> \u003Ctarget> [flags]`.\n   Format slugs are versioned, lowercase, dot-separated\n   (`metropolis-v3.0`, `cosmos-reason-v1.0`, ...).\n2. **Path and output are flags** — `--path PATH` (source),\n   `--output OUTPUT` (destination). Both required at the leaf;\n   passing positionally fails.\n3. **`--path` accepts both granularities** — a single scene\u002Fdataset\n   or a parent directory; the converter walks the tree.\n4. **Per-pair flags live at the leaf** — flag sets differ between\n   targets (e.g. media-handling). Always check the leaf `--help`.\n\n**Operating procedure:**\n\n1. `tao-daft --version` — confirm install, pin version in any report.\n2. `tao-daft convert --help` — list supported source formats.\n3. `tao-daft convert \u003Csource> --help` — list valid targets for that\n   source.\n4. Infer source from layout (same directory markers as the\n   `tao-validate-dataset-format` skill's \"Format inference\"). If you cannot infer\n   or the target is unspecified, ask.\n5. `tao-daft convert \u003Csource> \u003Ctarget> --help` — pick flags for the\n   user's intent (task subset, media copy vs reference, metadata).\n6. Execute, then interpret (see below).\n\n### Reading output\n\nPer-scene progress prints to stdout; non-zero exit on failure. The\nconverted dataset is written under `--output` — spot-check it with\nthe `tao-validate-dataset-format` skill before training. For large trees, capture\nthe full output and partial-read if huge.\n\n## Limitations\n\n- DAFT-supported source formats only. For non-DAFT layouts use the\n  upstream repo's converter skills.\n- Supported pairs are whatever `--help` reports for the installed\n  version — don't pass an unconfirmed pair.\n- Source and target are positional; `--path` \u002F `--output` are flags.\n- `convert` only — `validate` and `info` have their own skills.\n- Do not reimplement conversion in Python; the CLI is the spec.\n\n## Troubleshooting\n\n- **`tao-daft: command not found`** — wheel not installed; `pip\n  install nvidia-tao-daft`, verify with `tao-daft --version`.\n- **`error: argument --path\u002F--output is required`** — passed\n  positionally; move behind the flag.\n- **`invalid choice: '\u003Cformat>'`** — slug not wired up in this\n  version. Re-run the relevant `--help`.\n- **Output rejected by `tao-daft validate`** — re-check per-pair\n  flags (media handling, task subset) via leaf `--help`; a misset\n  flag often produces a structurally valid but semantically wrong\n  target.\n","# Convert a TAO DAFT Dataset\n\n## Quick start\n\n```bash\ntao-daft convert \u003Csource-format> \u003Ctarget-format> --path \u003Cinput> --output \u003Coutput>\n```\n\nSource and target are positional subcommands; `--path` and `--output` are flags.\nDiscover the supported formats and per-pair flags from the leaf `--help`\n(see \"CLI conventions\" below).\n\n## Preflight\n```bash\npython -c \"import nvidia_tao_daft\" 2>\u002Fdev\u002Fnull || {\n  echo \"MISSING: tao-daft not installed. Run:\"\n  echo \"  pip install nvidia-tao-daft\"\n  exit 1\n}\n```\n\n## Quick Start\n\nDiscover the installed CLI surface before choosing format slugs, then run the\nleaf conversion command with explicit `--path` and `--output` flags:\n\n```bash\ntao-daft --version\ntao-daft convert --help\ntao-daft convert \u003Csource-format> --help\ntao-daft convert \u003Csource-format> \u003Ctarget-format> --path \u002Fpath\u002Fto\u002Fdaft --output \u002Fpath\u002Fto\u002Fconverted\n```\n\n## Purpose\n\nDrives `tao-daft convert` to transform a DAFT dataset (or a tree of\nthem) between supported formats. The CLI does the real work; the\nskill picks the right source\u002Ftarget pair and flags, then explains the\nresult.\n\nTrigger on: converting a DAFT dataset, packaging DAFT QA \u002F\nsummarization \u002F temporal tasks for VLM training, producing a\n`meta.json`-style training set, or the command `tao-daft convert`. Do\n**not** trigger for non-DAFT → DAFT conversion (COCO, YOLO, Data\nFactory JSONL) — redirect to the upstream `nvidia-tao-daft` repo's\nconverter skills.\n\nIf the user opens ambiguously, run a few `--help` calls first.\n\n## Prerequisites\n\n- `nvidia-tao-daft` installed (wheel only, not the source repo).\n  Confirm with `tao-daft --version`.\n- A DAFT dataset, or a parent directory containing many, on local\n  disk.\n\n## Instructions\n\n### CLI conventions\n\n`tao-daft` is nested argparse subcommands. The conventions below are\nstable across versions even when format names or flags change, so\n**always discover the current surface from `--help`** rather than\nrelying on names this doc happens to mention.\n\n1. **Source and target are both positional subcommands**, not\n   `--from`\u002F`--to`: `tao-daft convert \u003Csource> \u003Ctarget> [flags]`.\n   Format slugs are versioned, lowercase, dot-separated\n   (`metropolis-v3.0`, `cosmos-reason-v1.0`, ...).\n2. **Path and output are flags** — `--path PATH` (source),\n   `--output OUTPUT` (destination). Both required at the leaf;\n   passing positionally fails.\n3. **`--path` accepts both granularities** — a single scene\u002Fdataset\n   or a parent directory; the converter walks the tree.\n4. **Per-pair flags live at the leaf** — flag sets differ between\n   targets (e.g. media-handling). Always check the leaf `--help`.\n\n**Operating procedure:**\n\n1. `tao-daft --version` — confirm install, pin version in any report.\n2. `tao-daft convert --help` — list supported source formats.\n3. `tao-daft convert \u003Csource> --help` — list valid targets for that\n   source.\n4. Infer source from layout (same directory markers as the\n   `tao-validate-dataset-format` skill's \"Format inference\"). If you cannot infer\n   or the target is unspecified, ask.\n5. `tao-daft convert \u003Csource> \u003Ctarget> --help` — pick flags for the\n   user's intent (task subset, media copy vs reference, metadata).\n6. Execute, then interpret (see below).\n\n### Reading output\n\nPer-scene progress prints to stdout; non-zero exit on failure. The\nconverted dataset is written under `--output` — spot-check it with\nthe `tao-validate-dataset-format` skill before training. For large trees, capture\nthe full output and partial-read if huge.\n\n## Limitations\n\n- DAFT-supported source formats only. For non-DAFT layouts use the\n  upstream repo's converter skills.\n- Supported pairs are whatever `--help` reports for the installed\n  version — don't pass an unconfirmed pair.\n- Source and target are positional; `--path` \u002F `--output` are flags.\n- `convert` only — `validate` and `info` have their own skills.\n- Do not reimplement conversion in Python; the CLI is the spec.\n\n## Troubleshooting\n\n- **`tao-daft: command not found`** — wheel not installed; `pip\n  install nvidia-tao-daft`, verify with `tao-daft --version`.\n- **`error: argument --path\u002F--output is required`** — passed\n  positionally; move behind the flag.\n- **`invalid choice: '\u003Cformat>'`** — slug not wired up in this\n  version. Re-run the relevant `--help`.\n- **Output rejected by `tao-daft validate`** — re-check per-pair\n  flags (media handling, task subset) via leaf `--help`; a misset\n  flag often produces a structurally valid but semantically wrong\n  target.\n",{"name":5,"tags":44,"license":15,"metadata":47,"description":7,"allowed-tools":50,"compatibility":51},[45,23,24,46,26],"tao-daft","vlm",{"author":48,"version":49},"NVIDIA Corporation","1.0.0","Read Bash","Requires Python 3.10+ and the nvidia-tao-sdk package (pip install nvidia-tao-daft).","main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".claude\u002Fskills\u002Ftao-convert-dataset-format","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":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-convert-dataset-format",{"tool":65,"command":66},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-convert-dataset-format",{"tool":68,"command":69},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill tao-convert-dataset-format","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-convert-dataset-format\u002FSKILL.md",true,[73,79,83,87],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"BENCHMARK.md","REFERENCE","参考文档",4213,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-convert-dataset-format\u002FBENCHMARK.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"evals\u002Fevals.json",814,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-convert-dataset-format\u002Fevals\u002Fevals.json",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"skill-card.md",3682,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-convert-dataset-format\u002Fskill-card.md",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"skill.oms.sig","OTHER","其他",4601,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Ftao-convert-dataset-format\u002Fskill.oms.sig",[94,120,144,170,188,223],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":109,"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",[101,106],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000023","Skill与智能体开发",[110,111,112,113,114,32,115,116,117,118,119],"xunit","xunit skill","testing","injection","user","tests","triggers","unit","unit-testing","C#",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":128,"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",[126,127],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},[129,130,112,131,132,133,134,135,32,136,137,138,139,140,115,116,141,142,27,143],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","objective",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":154,"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",[150,153],{"categoryNo":151,"categoryName":152,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},[155,156,157,158,159,160,161,162,32,163,164,165,166,167,168,27,169],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":178,"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",[176,177],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},{"categoryNo":107,"categoryName":108,"taxonomyType":104,"taxonomyTypeDesc":105},[179,112,180,131,181,182,183,184,114,32,185,139,115,116,186,187],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","URL","conf",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":203,"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",[194,197,198],{"categoryNo":195,"categoryName":196,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":151,"categoryName":152,"taxonomyType":104,"taxonomyTypeDesc":105},{"categoryNo":199,"categoryName":200,"taxonomyType":201,"taxonomyTypeDesc":202},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,116,222],"design","web","guidelines","web design guidelines","web design","css","performance","components","web components","tasks","code","web performance","ui","platform design","html","responsive design","responsive","platform","implementing",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":227,"categories":228,"keywords":231,"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",[229,230],{"categoryNo":151,"categoryName":152,"taxonomyType":104,"taxonomyTypeDesc":105},{"categoryNo":199,"categoryName":200,"taxonomyType":201,"taxonomyTypeDesc":202},[204,206,232,213,216,32,233,234,140,221,235,116,141,236,27,237,238],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]