[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PJTYXE02KYN3BWFPN2VGCH":3,"skill-same:01M6PJTYXE02KYN3BWFPN2VGCH":246},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":14,"repositoryHtmlUrl":63,"repositoryHomepageUrl":6,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":6,"updatedAt":66,"install":67,"resources":79},"01M6PJTYXE02KYN3BWFPN2VGCH","seo-flow",null,"FLOW framework integration — evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says \"FLOW\", \"FLOW framework\", \"seo flow\", \"evidence-led SEO\", \"find leverage optimize win\", or wants stage-specific SEO prompts.","headmktlucca-ui\u002Fneuroads-lp","headmktlucca-ui","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F247379838?v=4","01M6PDVAV6M1W96D1SR1PDF8CS","NeuroAds","01M6PDVAZ4P281GNDKD45CYTF5",0,"MIT","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"flow","seo","seo flow","true","integration","URL","leverage","stage","knowledge base","base","optimize","cc","surfaces","prompts","evidence","use","user","find","loop","knowledge","2026-11-10 10:12:30",false,"---\nname: seo-flow\ndescription: >\n  FLOW framework integration — evidence-led SEO using the Find → Leverage →\n  Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW\n  knowledge base (41 prompts, CC BY 4.0). Use when user says \"FLOW\", \"FLOW\n  framework\", \"seo flow\", \"evidence-led SEO\", \"find leverage optimize win\",\n  or wants stage-specific SEO prompts.\nuser-invocable: true\nargument-hint: \"[stage] [url|topic]\"\nlicense: MIT\nmetadata:\n  author: AgriciDaniel\n  version: \"2.2.0\"\n  category: seo\n---\n\n# FLOW Framework — Find · Leverage · Optimize · Win\n\n> Framework and prompts © Daniel Agrici, CC BY 4.0 — github.com\u002FAgriciDaniel\u002Fflow\n\nFLOW is an evidence-led SEO operating model built for the AI-search era. Claude SEO\nintegrates the FLOW prompt library (41 prompts across 5 stages) so every analysis can\nbe driven by structured, evidence-backed AI prompts rather than improvised queries.\n\n**Runtime context:** Load `references\u002Fflow-framework.md` on every `\u002Fseo flow` activation.\nLoad prompt files on demand — only for the stage the user requests.\n\n---\n\n## Commands\n\n| Command | What it does |\n|---------|-------------|\n| `\u002Fseo flow` | Show FLOW overview + stage menu |\n| `\u002Fseo flow find [url\\|topic]` | Find-stage: keyword research, gap analysis, SERP intent mapping (5 prompts) |\n| `\u002Fseo flow leverage [url]` | Leverage-stage: backlink strategy, off-site authority (1 prompt) |\n| `\u002Fseo flow optimize [url]` | Optimize-stage: select 2-3 most relevant of 21 prompts based on context |\n| `\u002Fseo flow win [url]` | Win-stage: BOFU, conversion rate, dual-surface scorecard (3 prompts) |\n| `\u002Fseo flow local [url]` | Local-stage: GBP optimization, meta, title tags, local audits (11 prompts) |\n| `\u002Fseo flow prompts` | Full index of all 41 prompts — stage, name, trigger conditions |\n| `\u002Fseo flow sync` | Pull latest prompt files from github.com\u002FAgriciDaniel\u002Fflow |\n\n---\n\n## Orchestration Logic\n\n### On `\u002Fseo flow` (no sub-command)\n1. Read `references\u002Fflow-framework.md`\n2. Show the FLOW stage overview with a one-line description of each stage\n3. Ask: which stage matches the user's current situation?\n\n### On `\u002Fseo flow find [url|topic]`\n1. Read all files in `references\u002Fprompts\u002Ffind\u002F`\n2. Apply each prompt to the URL or topic\n3. Cross-reference: \"For deeper SERP clustering, see `\u002Fseo cluster \u003Cseed-keyword>`\"\n\n### On `\u002Fseo flow leverage [url]`\n1. Read the file in `references\u002Fprompts\u002Fleverage\u002F`\n2. Apply to the URL's current backlink context\n3. Cross-reference: \"For raw backlink data, see `\u002Fseo backlinks \u003Curl>`\"\n\n### On `\u002Fseo flow optimize [url]`\n1. Read all file names in `references\u002Fprompts\u002Foptimize\u002F`\n2. Read prior analysis context (URL, industry vertical, any prior skill output in conversation)\n3. Select 2-3 most relevant prompts; load only those files\n4. Apply selected prompts; note the others are accessible via `\u002Fseo flow prompts`\n5. Cross-reference: \"For full content quality analysis, see `\u002Fseo content \u003Curl>` and `\u002Fseo geo \u003Curl>`\"\n\n### On `\u002Fseo flow win [url]`\n1. Read all files in `references\u002Fprompts\u002Fwin\u002F`\n2. Apply each prompt to the URL's conversion and BOFU context\n3. Cross-reference: \"For SXO persona scoring, see `\u002Fseo sxo \u003Curl>`\"\n\n### On `\u002Fseo flow local [url]`\n1. Read all files in `references\u002Fprompts\u002Flocal\u002F`\n2. Apply to the URL's local SEO context\n3. Cross-reference: \"For full local SEO analysis, see `\u002Fseo local \u003Curl>` and `\u002Fseo maps [command]`\"\n\n### On `\u002Fseo flow prompts`\n1. Read `references\u002Fprompts\u002FREADME.md`\n2. Display the full index: all 41 prompts with stage, name, trigger conditions\n\n### On `\u002Fseo flow sync`\n1. Run: `python3 scripts\u002Fsync_flow.py`\n2. Display the JSON summary (files added, updated, unchanged)\n3. Show attribution notice after sync completes\n\n---\n\n## Context Matching (Optimize stage)\n\nThe optimize stage has 21 prompts. Dumping all 21 is noise. Select by priority:\n\n1. **Industry vertical** (SaaS → on-page + technical; local → citations + GBP; publisher → E-E-A-T + freshness)\n2. **Prior skill output** (seo-technical flagged crawl issues → technical optimize prompts; seo-content flagged E-E-A-T gaps → content optimize prompts)\n3. **URL signals** (product pages → conversion; blog → freshness + authority)\n\nAlways surface exactly 2-3 prompts. State which prompts you chose and why.\n\n---\n\n## Reference Files\n\nLoad on-demand — do NOT load all at startup:\n- `references\u002Fflow-framework.md` — FLOW operating model (load on every `\u002Fseo flow` activation)\n- `references\u002Fbibliography.md` — Evidence sources; load when citing studies or statistics\n- `references\u002Fprompts\u002FREADME.md` — Prompt index; load for `\u002Fseo flow prompts`\n- `references\u002Fprompts\u002Ffind\u002F` — 5 prompts; load for `\u002Fseo flow find`\n- `references\u002Fprompts\u002Fleverage\u002F` — 1 prompt; load for `\u002Fseo flow leverage`\n- `references\u002Fprompts\u002Foptimize\u002F` — 21 prompts; load selectively for `\u002Fseo flow optimize`\n- `references\u002Fprompts\u002Fwin\u002F` — 3 prompts; load for `\u002Fseo flow win`\n- `references\u002Fprompts\u002Flocal\u002F` — 11 prompts; load for `\u002Fseo flow local`\n\n---\n\n## Attribution\n\nEvery `\u002Fseo flow` activation (any sub-command) outputs before analysis:\n\n```\nFramework and prompts © Daniel Agrici, CC BY 4.0 — github.com\u002FAgriciDaniel\u002Fflow\n```\n\nDo not omit or modify the attribution.\n\n---\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| `references\u002Fflow-framework.md` missing | \"FLOW reference files not synced. Run: `\u002Fseo flow sync`\" |\n| Prompt file missing | \"Run `\u002Fseo flow sync` to pull the latest prompts from the FLOW repo.\" |\n| `sync_flow.py` network error | Display the script's stderr. Check rate limits: `gh api rate_limit`. |\n| `sync_flow.py` auth error | Run `gh auth login` then retry. |\n","# FLOW Framework — Find · Leverage · Optimize · Win\n\n> Framework and prompts © Daniel Agrici, CC BY 4.0 — github.com\u002FAgriciDaniel\u002Fflow\n\nFLOW is an evidence-led SEO operating model built for the AI-search era. Claude SEO\nintegrates the FLOW prompt library (41 prompts across 5 stages) so every analysis can\nbe driven by structured, evidence-backed AI prompts rather than improvised queries.\n\n**Runtime context:** Load `references\u002Fflow-framework.md` on every `\u002Fseo flow` activation.\nLoad prompt files on demand — only for the stage the user requests.\n\n---\n\n## Commands\n\n| Command | What it does |\n|---------|-------------|\n| `\u002Fseo flow` | Show FLOW overview + stage menu |\n| `\u002Fseo flow find [url\\|topic]` | Find-stage: keyword research, gap analysis, SERP intent mapping (5 prompts) |\n| `\u002Fseo flow leverage [url]` | Leverage-stage: backlink strategy, off-site authority (1 prompt) |\n| `\u002Fseo flow optimize [url]` | Optimize-stage: select 2-3 most relevant of 21 prompts based on context |\n| `\u002Fseo flow win [url]` | Win-stage: BOFU, conversion rate, dual-surface scorecard (3 prompts) |\n| `\u002Fseo flow local [url]` | Local-stage: GBP optimization, meta, title tags, local audits (11 prompts) |\n| `\u002Fseo flow prompts` | Full index of all 41 prompts — stage, name, trigger conditions |\n| `\u002Fseo flow sync` | Pull latest prompt files from github.com\u002FAgriciDaniel\u002Fflow |\n\n---\n\n## Orchestration Logic\n\n### On `\u002Fseo flow` (no sub-command)\n1. Read `references\u002Fflow-framework.md`\n2. Show the FLOW stage overview with a one-line description of each stage\n3. Ask: which stage matches the user's current situation?\n\n### On `\u002Fseo flow find [url|topic]`\n1. Read all files in `references\u002Fprompts\u002Ffind\u002F`\n2. Apply each prompt to the URL or topic\n3. Cross-reference: \"For deeper SERP clustering, see `\u002Fseo cluster \u003Cseed-keyword>`\"\n\n### On `\u002Fseo flow leverage [url]`\n1. Read the file in `references\u002Fprompts\u002Fleverage\u002F`\n2. Apply to the URL's current backlink context\n3. Cross-reference: \"For raw backlink data, see `\u002Fseo backlinks \u003Curl>`\"\n\n### On `\u002Fseo flow optimize [url]`\n1. Read all file names in `references\u002Fprompts\u002Foptimize\u002F`\n2. Read prior analysis context (URL, industry vertical, any prior skill output in conversation)\n3. Select 2-3 most relevant prompts; load only those files\n4. Apply selected prompts; note the others are accessible via `\u002Fseo flow prompts`\n5. Cross-reference: \"For full content quality analysis, see `\u002Fseo content \u003Curl>` and `\u002Fseo geo \u003Curl>`\"\n\n### On `\u002Fseo flow win [url]`\n1. Read all files in `references\u002Fprompts\u002Fwin\u002F`\n2. Apply each prompt to the URL's conversion and BOFU context\n3. Cross-reference: \"For SXO persona scoring, see `\u002Fseo sxo \u003Curl>`\"\n\n### On `\u002Fseo flow local [url]`\n1. Read all files in `references\u002Fprompts\u002Flocal\u002F`\n2. Apply to the URL's local SEO context\n3. Cross-reference: \"For full local SEO analysis, see `\u002Fseo local \u003Curl>` and `\u002Fseo maps [command]`\"\n\n### On `\u002Fseo flow prompts`\n1. Read `references\u002Fprompts\u002FREADME.md`\n2. Display the full index: all 41 prompts with stage, name, trigger conditions\n\n### On `\u002Fseo flow sync`\n1. Run: `python3 scripts\u002Fsync_flow.py`\n2. Display the JSON summary (files added, updated, unchanged)\n3. Show attribution notice after sync completes\n\n---\n\n## Context Matching (Optimize stage)\n\nThe optimize stage has 21 prompts. Dumping all 21 is noise. Select by priority:\n\n1. **Industry vertical** (SaaS → on-page + technical; local → citations + GBP; publisher → E-E-A-T + freshness)\n2. **Prior skill output** (seo-technical flagged crawl issues → technical optimize prompts; seo-content flagged E-E-A-T gaps → content optimize prompts)\n3. **URL signals** (product pages → conversion; blog → freshness + authority)\n\nAlways surface exactly 2-3 prompts. State which prompts you chose and why.\n\n---\n\n## Reference Files\n\nLoad on-demand — do NOT load all at startup:\n- `references\u002Fflow-framework.md` — FLOW operating model (load on every `\u002Fseo flow` activation)\n- `references\u002Fbibliography.md` — Evidence sources; load when citing studies or statistics\n- `references\u002Fprompts\u002FREADME.md` — Prompt index; load for `\u002Fseo flow prompts`\n- `references\u002Fprompts\u002Ffind\u002F` — 5 prompts; load for `\u002Fseo flow find`\n- `references\u002Fprompts\u002Fleverage\u002F` — 1 prompt; load for `\u002Fseo flow leverage`\n- `references\u002Fprompts\u002Foptimize\u002F` — 21 prompts; load selectively for `\u002Fseo flow optimize`\n- `references\u002Fprompts\u002Fwin\u002F` — 3 prompts; load for `\u002Fseo flow win`\n- `references\u002Fprompts\u002Flocal\u002F` — 11 prompts; load for `\u002Fseo flow local`\n\n---\n\n## Attribution\n\nEvery `\u002Fseo flow` activation (any sub-command) outputs before analysis:\n\n```\nFramework and prompts © Daniel Agrici, CC BY 4.0 — github.com\u002FAgriciDaniel\u002Fflow\n```\n\nDo not omit or modify the attribution.\n\n---\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| `references\u002Fflow-framework.md` missing | \"FLOW reference files not synced. Run: `\u002Fseo flow sync`\" |\n| Prompt file missing | \"Run `\u002Fseo flow sync` to pull the latest prompts from the FLOW repo.\" |\n| `sync_flow.py` network error | Display the script's stderr. Check rate limits: `gh api rate_limit`. |\n| `sync_flow.py` auth error | Run `gh auth login` then retry. |\n",{"name":5,"license":15,"metadata":54,"description":57,"argument-hint":58,"user-invocable":59},{"author":55,"version":56,"category":30},"AgriciDaniel","2.2.0","FLOW framework integration — evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says \"FLOW\", \"FLOW framework\", \"seo flow\", \"evidence-led SEO\", \"find leverage optimize win\", or wants stage-specific SEO prompts.\n","[stage] [url|topic]",true,"main","7a940b8e5af16c95e5c190a524421d32fc7445a9",".agent\u002Fskills\u002Fseo-flow","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":68,"prompt":78,"zipAvailable":59},[69,72,75],{"tool":70,"command":71},"npx","npx skills add headmktlucca-ui\u002Fneuroads-lp --skill seo-flow",{"tool":73,"command":74},"bunx","bunx skills add headmktlucca-ui\u002Fneuroads-lp --skill seo-flow",{"tool":76,"command":77},"pnpm","pnpm dlx skills add headmktlucca-ui\u002Fneuroads-lp --skill seo-flow","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002FSKILL.md",[80,86,90,94,98,102,106,110,113,117,121,125,129,133,136,139,143,147,150,154,158,162,165,169,173,176,180,183,186,189,192,195,199,202,206,210,213,217,221,225,228,231,235,239,243],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fbibliography.md","REFERENCE","参考文档",8051,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fbibliography.md",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fflow-framework.md",4896,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fflow-framework.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fflow-prompts.lock",6165,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fflow-prompts.lock",{"path":95,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fprompts\u002Ffind\u002Fcontent-planning-for-topical-relevance-prompt.md",2172,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Ffind\u002Fcontent-planning-for-topical-relevance-prompt.md",{"path":99,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Fprompts\u002Ffind\u002Fcontent-prioritization-prompt.md",2124,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Ffind\u002Fcontent-prioritization-prompt.md",{"path":103,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fprompts\u002Ffind\u002Fkeyword-research-prompt.md",2106,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Ffind\u002Fkeyword-research-prompt.md",{"path":107,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fprompts\u002Ffind\u002Fkeyword-variations-for-topical-relevance-prompt.md",2178,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Ffind\u002Fkeyword-variations-for-topical-relevance-prompt.md",{"path":111,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":104,"sourceHtmlUrl":112},"references\u002Fprompts\u002Ffind\u002Fprompt-audience-avatar.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Ffind\u002Fprompt-audience-avatar.md",{"path":114,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Fprompts\u002Fleverage\u002Fbacklink-competition-prompt.md",2130,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Fleverage\u002Fbacklink-competition-prompt.md",{"path":118,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":119,"sourceHtmlUrl":120},"references\u002Fprompts\u002Flocal\u002Fai-homepage-rewrite-prompt.md",2118,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fai-homepage-rewrite-prompt.md",{"path":122,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":123,"sourceHtmlUrl":124},"references\u002Fprompts\u002Flocal\u002Fclaude-deep-research-prompt.md",2127,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fclaude-deep-research-prompt.md",{"path":126,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":127,"sourceHtmlUrl":128},"references\u002Fprompts\u002Flocal\u002Fgbp-categories-prompt.md",2103,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fgbp-categories-prompt.md",{"path":130,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":131,"sourceHtmlUrl":132},"references\u002Fprompts\u002Flocal\u002Fgbp-description-claude-prompt-1.md",2133,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fgbp-description-claude-prompt-1.md",{"path":134,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":131,"sourceHtmlUrl":135},"references\u002Fprompts\u002Flocal\u002Fgbp-description-claude-prompt-2.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fgbp-description-claude-prompt-2.md",{"path":137,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":131,"sourceHtmlUrl":138},"references\u002Fprompts\u002Flocal\u002Fgbp-description-claude-prompt-3.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fgbp-description-claude-prompt-3.md",{"path":140,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":141,"sourceHtmlUrl":142},"references\u002Fprompts\u002Flocal\u002Fgbp-services-prompt.md",2097,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fgbp-services-prompt.md",{"path":144,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":145,"sourceHtmlUrl":146},"references\u002Fprompts\u002Flocal\u002Fprompt-generating-a-meta-description.md",2151,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fprompt-generating-a-meta-description.md",{"path":148,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":115,"sourceHtmlUrl":149},"references\u002Fprompts\u002Flocal\u002Fprompt-generating-a-title-tag.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fprompt-generating-a-title-tag.md",{"path":151,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":152,"sourceHtmlUrl":153},"references\u002Fprompts\u002Flocal\u002Fprompt-rewriting-existing-homepage.md",2145,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fprompt-rewriting-existing-homepage.md",{"path":155,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":156,"sourceHtmlUrl":157},"references\u002Fprompts\u002Flocal\u002Fprompt-rewriting-existing-service-page.md",2160,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Flocal\u002Fprompt-rewriting-existing-service-page.md",{"path":159,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":160,"sourceHtmlUrl":161},"references\u002Fprompts\u002Foptimize\u002Fai-detector-test-follow-up-prompt.md",2148,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fai-detector-test-follow-up-prompt.md",{"path":163,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":145,"sourceHtmlUrl":164},"references\u002Fprompts\u002Foptimize\u002Fai-supporting-pages-rewrite-prompt.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fai-supporting-pages-rewrite-prompt.md",{"path":166,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":167,"sourceHtmlUrl":168},"references\u002Fprompts\u002Foptimize\u002Fbasic-prompt.md",2085,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fbasic-prompt.md",{"path":170,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":171,"sourceHtmlUrl":172},"references\u002Fprompts\u002Foptimize\u002Fblog-post-outline-prompt.md",2121,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fblog-post-outline-prompt.md",{"path":174,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":171,"sourceHtmlUrl":175},"references\u002Fprompts\u002Foptimize\u002Fblog-post-writing-prompt.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fblog-post-writing-prompt.md",{"path":177,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":178,"sourceHtmlUrl":179},"references\u002Fprompts\u002Foptimize\u002Fclaude-prompt-1.md",2094,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fclaude-prompt-1.md",{"path":181,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":178,"sourceHtmlUrl":182},"references\u002Fprompts\u002Foptimize\u002Fclaude-prompt-2.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fclaude-prompt-2.md",{"path":184,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":141,"sourceHtmlUrl":185},"references\u002Fprompts\u002Foptimize\u002Fctr-audit-prompt.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fctr-audit-prompt.md",{"path":187,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":127,"sourceHtmlUrl":188},"references\u002Fprompts\u002Foptimize\u002Ffollow-up-prompt-1.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Ffollow-up-prompt-1.md",{"path":190,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":127,"sourceHtmlUrl":191},"references\u002Fprompts\u002Foptimize\u002Ffollow-up-prompt-2.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Ffollow-up-prompt-2.md",{"path":193,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":141,"sourceHtmlUrl":194},"references\u002Fprompts\u002Foptimize\u002Ffollow-up-prompt.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Ffollow-up-prompt.md",{"path":196,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":197,"sourceHtmlUrl":198},"references\u002Fprompts\u002Foptimize\u002Fpaa-question-rewording-prompt.md",2136,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fpaa-question-rewording-prompt.md",{"path":200,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":197,"sourceHtmlUrl":201},"references\u002Fprompts\u002Foptimize\u002Fprompt-core-30-content-audit.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fprompt-core-30-content-audit.md",{"path":203,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":204,"sourceHtmlUrl":205},"references\u002Fprompts\u002Foptimize\u002Fproperty-content-with-authority-audit-prompt.md",2181,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fproperty-content-with-authority-audit-prompt.md",{"path":207,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":208,"sourceHtmlUrl":209},"references\u002Fprompts\u002Foptimize\u002Freddit-claude-prompt.md",2109,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Freddit-claude-prompt.md",{"path":211,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":178,"sourceHtmlUrl":212},"references\u002Fprompts\u002Foptimize\u002Fschema-prompt-1.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fschema-prompt-1.md",{"path":214,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":215,"sourceHtmlUrl":216},"references\u002Fprompts\u002Foptimize\u002Fstep-1-the-chatgpt-discovery-prompt.md",2157,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fstep-1-the-chatgpt-discovery-prompt.md",{"path":218,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":219,"sourceHtmlUrl":220},"references\u002Fprompts\u002Foptimize\u002Fstep-2-the-follow-up-qualifying-prompt.md",2166,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fstep-2-the-follow-up-qualifying-prompt.md",{"path":222,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":223,"sourceHtmlUrl":224},"references\u002Fprompts\u002Foptimize\u002Ftechnical-audit-prompt.md",2115,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Ftechnical-audit-prompt.md",{"path":226,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":115,"sourceHtmlUrl":227},"references\u002Fprompts\u002Foptimize\u002Fvisibility-follow-up-prompt.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fvisibility-follow-up-prompt.md",{"path":229,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":141,"sourceHtmlUrl":230},"references\u002Fprompts\u002Foptimize\u002Fvisiblity-prompt.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Foptimize\u002Fvisiblity-prompt.md",{"path":232,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":233,"sourceHtmlUrl":234},"references\u002Fprompts\u002FREADME.md",4536,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002FREADME.md",{"path":236,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":237,"sourceHtmlUrl":238},"references\u002Fprompts\u002Fwin\u002Fbofu-page-brief-generator.md",2240,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Fwin\u002Fbofu-page-brief-generator.md",{"path":240,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":241,"sourceHtmlUrl":242},"references\u002Fprompts\u002Fwin\u002Fconversion-audit-prompt.md",2204,"https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Fwin\u002Fconversion-audit-prompt.md",{"path":244,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":131,"sourceHtmlUrl":245},"references\u002Fprompts\u002Fwin\u002Fdual-surface-content-scorecard.md","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fseo-flow\u002Freferences\u002Fprompts\u002Fwin\u002Fdual-surface-content-scorecard.md",[247,269,294,320,337,370],{"skillNo":248,"name":249,"nameZh":6,"description":250,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":251,"categories":252,"keywords":259,"publishedAt":49},"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\".","https:\u002F\u002Fgithub.com\u002Fheadmktlucca-ui\u002Fneuroads-lp\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fxunit-skill\u002FSKILL.md",[253,256],{"categoryNo":254,"categoryName":255,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":257,"categoryName":258,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[260,261,262,263,45,44,264,265,266,267,268],"xunit","xunit skill","testing","injection","tests","triggers","unit","unit-testing","C#",{"skillNo":270,"name":271,"nameZh":6,"description":272,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":273,"categories":274,"keywords":277,"publishedAt":49},"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",[275,276],{"categoryNo":254,"categoryName":255,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":257,"categoryName":258,"taxonomyType":21,"taxonomyTypeDesc":22},[278,279,262,280,281,282,283,284,44,285,286,287,288,289,264,265,290,291,292,293],"xcuitest","xcuitest skill","automation","test","testing framework","ios","devices","mobile","mobile-testing","ui test","local","apple","apps","native","for","objective",{"skillNo":295,"name":296,"nameZh":6,"description":297,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":298,"categories":299,"keywords":304,"publishedAt":49},"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",[300,303],{"categoryNo":301,"categoryName":302,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":257,"categoryName":258,"taxonomyType":21,"taxonomyTypeDesc":22},[305,306,307,308,309,310,311,312,44,313,314,315,316,317,318,292,319],"builder","world","world builder","MCP","create","mcp tools","Tools","game","pokemon","manage","style","via","theme","zone","generative",{"skillNo":321,"name":322,"nameZh":6,"description":323,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":324,"categories":325,"keywords":328,"publishedAt":49},"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",[326,327],{"categoryNo":254,"categoryName":255,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":257,"categoryName":258,"taxonomyType":21,"taxonomyTypeDesc":22},[329,262,330,280,331,332,333,334,45,44,335,288,264,265,34,336],"webdriverio skill","typescript","javascript","browser","cloud","e2e","e2e-testing","conf",{"skillNo":338,"name":339,"nameZh":6,"description":340,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":341,"categories":342,"keywords":350,"publishedAt":49},"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",[343,346,347],{"categoryNo":344,"categoryName":345,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":301,"categoryName":302,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":348,"categoryName":349,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师",[351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,265,369],"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":371,"name":372,"nameZh":6,"description":373,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":374,"categories":375,"keywords":378,"publishedAt":49},"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",[376,377],{"categoryNo":301,"categoryName":302,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":348,"categoryName":349,"taxonomyType":26,"taxonomyTypeDesc":27},[351,353,379,360,363,44,380,381,289,368,382,265,290,383,292,384,385],"watchos design guidelines","workout","human","watch","interface","platform-design","features"]