[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CQRHSXT02PXRAQCS2MDZXK":3,"skill-same:01M7CQRHSXT02PXRAQCS2MDZXK":50},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":27,"commitSha":28,"skillDirectory":29,"forksCount":30,"repositoryHtmlUrl":31,"repositoryHomepageUrl":32,"ownerHtmlUrl":33,"ownerBlogUrl":6,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M7CQRHSXT02PXRAQCS2MDZXK","market-researcher",null,"Market research specialist focused on comprehensive market analysis, consumer behavior insights, and market opportunity identification. Excels at quantitative market sizing (TAM\u002FSAM\u002FSOM), qualitative consumer research, and strategic market positioning analysis.","YYC-Cube\u002FYYC3-Knowledge-Base","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79J2PHVK3F6ZBYT8K2SCZCW",1,"MIT","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-code-skills\u002Fskills\u002Fmarket-researcher\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: market-researcher\ndescription: \"Market research specialist focused on comprehensive market analysis, consumer behavior insights, and market opportunity identification. Excels at quantitative market sizing (TAM\u002FSAM\u002FSOM), qualitative consumer research, and strategic market positioning analysis.\"\ndescription_zh: \"市场调研专家，提供市场分析、消费者洞察与机会评估\"\ndescription_en: \"Market research specialist for analysis, consumer insights, and sizing\"\nversion: 1.0.0\n---\n\n# Market Researcher\n\n## Purpose\n\nProvides comprehensive market research expertise specializing in market sizing, consumer behavior analysis, and strategic opportunity identification. Excels at quantitative market analysis, qualitative consumer insights, and strategic market positioning for business decision-making.\n\n## When to Use\n\n- Sizing markets (TAM\u002FSAM\u002FSOM calculations)\n- Analyzing consumer behavior and purchase decisions\n- Conducting competitive market analysis\n- Identifying market opportunities and white spaces\n- Validating product-market fit or positioning strategies\n\n## Quick Start\n\n**Invoke this skill when:**\n- Sizing markets (TAM\u002FSAM\u002FSOM calculations)\n- Analyzing consumer behavior and purchase decisions\n- Conducting competitive market analysis\n- Identifying market opportunities and white spaces\n- Validating product-market fit or positioning strategies\n\n**Do NOT invoke when:**\n- Analyzing direct competitors only (use competitive-analyst instead)\n- Pure data analysis without market context (use data-analyst)\n- Sales forecasting from existing data (use data-scientist)\n- Marketing campaign execution (use content-marketer or seo-specialist)\n\n---\n---\n\n## Core Workflows\n\n### Workflow 1: Calculate TAM, SAM, SOM\n\n**Use case:** Sizing addressable market for new product or investment decision\n\n**Step 1: Define Market Scope**\n```\nMarket Definition Template:\n- Product\u002FService: [Specific offering]\n- Geography: [Target regions]\n- Customer Segment: [Who specifically?]\n- Time Frame: [Current year or 5-year projection?]\n\nExample:\n- Product: AI-powered customer service chatbot for e-commerce\n- Geography: United States\n- Customer Segment: E-commerce companies with \\u003e$10M revenue\n- Time Frame: 2024-2029\n```\n\n**Step 2: Calculate TAM (Top-Down Approach)**\n```\nTAM = Total market demand if 100% market share\n\nData sources:\n1. Industry reports (Gartner, Forrester, IBISWorld)\n2. Government statistics (Census Bureau, BLS)\n3. Trade associations\n\nExample calculation:\nTotal US e-commerce market: $1.1T (2024)\n× % needing customer service: 80%\n× Average customer service spend: 2.5% of revenue\nTAM = $1.1T × 80% × 2.5% = $22B\n```\n\n**Step 3: Calculate SAM (Serviceable Addressable Market)**\n```\nSAM = Portion of TAM you can realistically serve\n\nFilters to apply:\n- Geographic constraints (if only operating in US)\n- Product limitations (if only for e-commerce, not all retail)\n- Customer size constraints (if targeting $10M+ companies)\n\nExample:\nE-commerce companies \\u003e$10M revenue: 15,000 companies\n× Average annual customer service budget: $500K\nSAM = 15,000 × $500K = $7.5B\n```\n\n**Step 4: Calculate SOM (Serviceable Obtainable Market)**\n```\nSOM = Realistic market share you can capture in near term (1-3 years)\n\nFactors:\n- Competitive landscape (how many competitors?)\n- Your differentiation (unique value prop strength)\n- Sales \\u0026 marketing capacity (realistic reach)\n- Growth trajectory (realistic penetration rate)\n\nConservative SOM:\nYear 1: 0.1-0.5% of SAM\nYear 2: 0.5-2% of SAM\nYear 3: 1-5% of SAM\n\nExample (Year 3):\nSOM = $7.5B × 2% = $150M\n```\n\n**Step 5: Bottom-Up Validation**\n```\nValidate top-down sizing with bottom-up:\n\nUnit Economics Approach:\n- Target customers: 15,000 e-commerce companies\n- Realistic conversion rate: 5% (industry benchmark)\n- Customers acquired: 750\n- Average contract value: $50K\u002Fyear\n- Bottom-up market capture: 750 × $50K = $37.5M\n\nCompare: Top-down SOM ($150M) vs Bottom-up ($37.5M)\nIf gap \\u003e3x → revisit assumptions\n```\n\n---\n---\n\n### Workflow 3: Competitive Market Analysis\n\n**Use case:** Understanding competitive landscape and positioning opportunities\n\n**Step 1: Identify Competitors**\n```\nCompetitor Categories:\n1. Direct: Same product, same target customer\n2. Indirect: Different product, solves same problem\n3. Substitute: Alternative way to address need\n4. Potential: Could enter market easily\n\nExample (Project Management Software):\n- Direct: Asana, Monday.com, ClickUp\n- Indirect: Excel\u002FSheets (for simple tracking)\n- Substitute: Consultants (outsource instead of software)\n- Potential: Microsoft, Google (have adjacent products)\n```\n\n**Step 2: Competitive Intelligence Gathering**\n```\nData Sources Matrix:\n\nPublic Information:\n- Company websites (pricing, features, positioning)\n- App store reviews (4.2★ rating, \"easy to use\" appears 45%)\n- Social media (follower count, engagement rate)\n- Job postings (hiring for X roles = growing that area)\n\nIndustry Sources:\n- Gartner Magic Quadrant (market position)\n- G2 Crowd reviews (feature comparison, user satisfaction)\n- Crunchbase (funding, valuation, investor profiles)\n- LinkedIn (employee count trends, key hires)\n\nCompetitive Metrics Template:\n| Competitor | Pricing | Features | Market Share | Customer Satisfaction |\n|------------|---------|----------|--------------|----------------------|\n| Asana | $10-25\u002Fuser\u002Fmo | 85% feature parity | ~20% | 4.5\u002F5 (G2) |\n| Monday.com | $8-16\u002Fuser\u002Fmo | 90% feature parity | ~15% | 4.6\u002F5 (G2) |\n```\n\n**Step 3: Positioning Map**\n```\nCreate 2D positioning map:\nX-axis: Price (Low → High)\nY-axis: Feature Complexity (Simple → Advanced)\n\n┌─────────────────────────────────┐\n│ Advanced                        │\n│                    [Enterprise] │\n│                                 │\n│  [Our Product]         [Leader] │\n│                                 │\n│                        [Asana]  │\n│  [Budget Option]                │\n│ Simple                          │\n└─────────────────────────────────┘\n  Low Price            High Price\n\nInsight: Gap in \"Simple but Premium\" quadrant = opportunity\n```\n\n---\n---\n\n### Pattern 2: Van Westendorp Price Sensitivity Analysis\n\n**When to use:** Determining optimal pricing\n\n```\nSurvey Questions (ask in this order):\n1. At what price would you consider this product to be so expensive \n   that you would not consider buying it? (Too Expensive)\n\n2. At what price would you consider this product to be priced so low \n   that you would feel the quality couldn't be very good? (Too Cheap)\n\n3. At what price would you consider this product starting to get \n   expensive, so that it is not out of the question, but you would \n   have to give some thought to buying it? (Expensive\u002FHigh Side)\n\n4. At what price would you consider this product to be a bargain—a \n   great buy for the money? (Cheap\u002FGood Value)\n\nAnalysis:\n- Plot cumulative % for each price point\n- Optimal Price Point (OPP) = intersection of \"Too Expensive\" and \"Too Cheap\"\n- Acceptable Price Range = between \"Too Cheap\" and \"Too Expensive\" intersections\n\nExample Results:\nOPP: $49\u002Fmonth\nRange: $35-$75\u002Fmonth\nRecommendation: Price at $49-$59 for maximum acceptance\n```\n\n---\n---\n\n### ❌ Anti-Pattern 2: Survey Leading Questions\n\n**What it looks like:**\n```\n\"Don't you think our innovative new product would solve your problems better than competitors?\"\n\nAnswer options:\n[ ] Yes, absolutely!\n[ ] Yes, somewhat\n[ ] Maybe\n```\n\n**Why it fails:**\n- Leading language (\"innovative\", \"better\")\n- No negative options (biased toward \"yes\")\n- Worthless data (everyone says yes)\n\n**Correct approach:**\n```\n\"How well does [our product] solve [specific problem] compared to alternatives you've used?\"\n\n[ ] Much better\n[ ] Somewhat better\n[ ] About the same\n[ ] Somewhat worse\n[ ] Much worse\n[ ] Haven't used alternatives\n```\n\n---\n---\n\n## Quality Checklist\n\n### Research Design\n- [ ] Clear, measurable research objectives defined\n- [ ] Sample size calculated for statistical significance\n- [ ] Survey\u002Finterview questions tested with pilot group\n- [ ] No leading or biased questions\n- [ ] Mix of qualitative and quantitative methods (if appropriate)\n\n### Data Collection\n- [ ] Representative sample (demographics match target market)\n- [ ] Response rate \\u003e25% for surveys (higher is better)\n- [ ] Data quality checks during collection\n- [ ] Respondent privacy protected (GDPR\u002FCCPA compliant)\n\n### Analysis \\u0026 Insights\n- [ ] Statistical significance tested (p-values, confidence intervals)\n- [ ] Outliers identified and handled appropriately\n- [ ] Multiple hypotheses tested (not just confirmation bias)\n- [ ] Insights validated with multiple data points\n\n### Reporting\n- [ ] Findings actionable (not just \"interesting facts\")\n- [ ] Visualizations clear and accurate\n- [ ] Limitations acknowledged\n- [ ] Recommendations prioritized by impact\n\n---\n","# Market Researcher\n\n## Purpose\n\nProvides comprehensive market research expertise specializing in market sizing, consumer behavior analysis, and strategic opportunity identification. Excels at quantitative market analysis, qualitative consumer insights, and strategic market positioning for business decision-making.\n\n## When to Use\n\n- Sizing markets (TAM\u002FSAM\u002FSOM calculations)\n- Analyzing consumer behavior and purchase decisions\n- Conducting competitive market analysis\n- Identifying market opportunities and white spaces\n- Validating product-market fit or positioning strategies\n\n## Quick Start\n\n**Invoke this skill when:**\n- Sizing markets (TAM\u002FSAM\u002FSOM calculations)\n- Analyzing consumer behavior and purchase decisions\n- Conducting competitive market analysis\n- Identifying market opportunities and white spaces\n- Validating product-market fit or positioning strategies\n\n**Do NOT invoke when:**\n- Analyzing direct competitors only (use competitive-analyst instead)\n- Pure data analysis without market context (use data-analyst)\n- Sales forecasting from existing data (use data-scientist)\n- Marketing campaign execution (use content-marketer or seo-specialist)\n\n---\n---\n\n## Core Workflows\n\n### Workflow 1: Calculate TAM, SAM, SOM\n\n**Use case:** Sizing addressable market for new product or investment decision\n\n**Step 1: Define Market Scope**\n```\nMarket Definition Template:\n- Product\u002FService: [Specific offering]\n- Geography: [Target regions]\n- Customer Segment: [Who specifically?]\n- Time Frame: [Current year or 5-year projection?]\n\nExample:\n- Product: AI-powered customer service chatbot for e-commerce\n- Geography: United States\n- Customer Segment: E-commerce companies with \\u003e$10M revenue\n- Time Frame: 2024-2029\n```\n\n**Step 2: Calculate TAM (Top-Down Approach)**\n```\nTAM = Total market demand if 100% market share\n\nData sources:\n1. Industry reports (Gartner, Forrester, IBISWorld)\n2. Government statistics (Census Bureau, BLS)\n3. Trade associations\n\nExample calculation:\nTotal US e-commerce market: $1.1T (2024)\n× % needing customer service: 80%\n× Average customer service spend: 2.5% of revenue\nTAM = $1.1T × 80% × 2.5% = $22B\n```\n\n**Step 3: Calculate SAM (Serviceable Addressable Market)**\n```\nSAM = Portion of TAM you can realistically serve\n\nFilters to apply:\n- Geographic constraints (if only operating in US)\n- Product limitations (if only for e-commerce, not all retail)\n- Customer size constraints (if targeting $10M+ companies)\n\nExample:\nE-commerce companies \\u003e$10M revenue: 15,000 companies\n× Average annual customer service budget: $500K\nSAM = 15,000 × $500K = $7.5B\n```\n\n**Step 4: Calculate SOM (Serviceable Obtainable Market)**\n```\nSOM = Realistic market share you can capture in near term (1-3 years)\n\nFactors:\n- Competitive landscape (how many competitors?)\n- Your differentiation (unique value prop strength)\n- Sales \\u0026 marketing capacity (realistic reach)\n- Growth trajectory (realistic penetration rate)\n\nConservative SOM:\nYear 1: 0.1-0.5% of SAM\nYear 2: 0.5-2% of SAM\nYear 3: 1-5% of SAM\n\nExample (Year 3):\nSOM = $7.5B × 2% = $150M\n```\n\n**Step 5: Bottom-Up Validation**\n```\nValidate top-down sizing with bottom-up:\n\nUnit Economics Approach:\n- Target customers: 15,000 e-commerce companies\n- Realistic conversion rate: 5% (industry benchmark)\n- Customers acquired: 750\n- Average contract value: $50K\u002Fyear\n- Bottom-up market capture: 750 × $50K = $37.5M\n\nCompare: Top-down SOM ($150M) vs Bottom-up ($37.5M)\nIf gap \\u003e3x → revisit assumptions\n```\n\n---\n---\n\n### Workflow 3: Competitive Market Analysis\n\n**Use case:** Understanding competitive landscape and positioning opportunities\n\n**Step 1: Identify Competitors**\n```\nCompetitor Categories:\n1. Direct: Same product, same target customer\n2. Indirect: Different product, solves same problem\n3. Substitute: Alternative way to address need\n4. Potential: Could enter market easily\n\nExample (Project Management Software):\n- Direct: Asana, Monday.com, ClickUp\n- Indirect: Excel\u002FSheets (for simple tracking)\n- Substitute: Consultants (outsource instead of software)\n- Potential: Microsoft, Google (have adjacent products)\n```\n\n**Step 2: Competitive Intelligence Gathering**\n```\nData Sources Matrix:\n\nPublic Information:\n- Company websites (pricing, features, positioning)\n- App store reviews (4.2★ rating, \"easy to use\" appears 45%)\n- Social media (follower count, engagement rate)\n- Job postings (hiring for X roles = growing that area)\n\nIndustry Sources:\n- Gartner Magic Quadrant (market position)\n- G2 Crowd reviews (feature comparison, user satisfaction)\n- Crunchbase (funding, valuation, investor profiles)\n- LinkedIn (employee count trends, key hires)\n\nCompetitive Metrics Template:\n| Competitor | Pricing | Features | Market Share | Customer Satisfaction |\n|------------|---------|----------|--------------|----------------------|\n| Asana | $10-25\u002Fuser\u002Fmo | 85% feature parity | ~20% | 4.5\u002F5 (G2) |\n| Monday.com | $8-16\u002Fuser\u002Fmo | 90% feature parity | ~15% | 4.6\u002F5 (G2) |\n```\n\n**Step 3: Positioning Map**\n```\nCreate 2D positioning map:\nX-axis: Price (Low → High)\nY-axis: Feature Complexity (Simple → Advanced)\n\n┌─────────────────────────────────┐\n│ Advanced                        │\n│                    [Enterprise] │\n│                                 │\n│  [Our Product]         [Leader] │\n│                                 │\n│                        [Asana]  │\n│  [Budget Option]                │\n│ Simple                          │\n└─────────────────────────────────┘\n  Low Price            High Price\n\nInsight: Gap in \"Simple but Premium\" quadrant = opportunity\n```\n\n---\n---\n\n### Pattern 2: Van Westendorp Price Sensitivity Analysis\n\n**When to use:** Determining optimal pricing\n\n```\nSurvey Questions (ask in this order):\n1. At what price would you consider this product to be so expensive \n   that you would not consider buying it? (Too Expensive)\n\n2. At what price would you consider this product to be priced so low \n   that you would feel the quality couldn't be very good? (Too Cheap)\n\n3. At what price would you consider this product starting to get \n   expensive, so that it is not out of the question, but you would \n   have to give some thought to buying it? (Expensive\u002FHigh Side)\n\n4. At what price would you consider this product to be a bargain—a \n   great buy for the money? (Cheap\u002FGood Value)\n\nAnalysis:\n- Plot cumulative % for each price point\n- Optimal Price Point (OPP) = intersection of \"Too Expensive\" and \"Too Cheap\"\n- Acceptable Price Range = between \"Too Cheap\" and \"Too Expensive\" intersections\n\nExample Results:\nOPP: $49\u002Fmonth\nRange: $35-$75\u002Fmonth\nRecommendation: Price at $49-$59 for maximum acceptance\n```\n\n---\n---\n\n### ❌ Anti-Pattern 2: Survey Leading Questions\n\n**What it looks like:**\n```\n\"Don't you think our innovative new product would solve your problems better than competitors?\"\n\nAnswer options:\n[ ] Yes, absolutely!\n[ ] Yes, somewhat\n[ ] Maybe\n```\n\n**Why it fails:**\n- Leading language (\"innovative\", \"better\")\n- No negative options (biased toward \"yes\")\n- Worthless data (everyone says yes)\n\n**Correct approach:**\n```\n\"How well does [our product] solve [specific problem] compared to alternatives you've used?\"\n\n[ ] Much better\n[ ] Somewhat better\n[ ] About the same\n[ ] Somewhat worse\n[ ] Much worse\n[ ] Haven't used alternatives\n```\n\n---\n---\n\n## Quality Checklist\n\n### Research Design\n- [ ] Clear, measurable research objectives defined\n- [ ] Sample size calculated for statistical significance\n- [ ] Survey\u002Finterview questions tested with pilot group\n- [ ] No leading or biased questions\n- [ ] Mix of qualitative and quantitative methods (if appropriate)\n\n### Data Collection\n- [ ] Representative sample (demographics match target market)\n- [ ] Response rate \\u003e25% for surveys (higher is better)\n- [ ] Data quality checks during collection\n- [ ] Respondent privacy protected (GDPR\u002FCCPA compliant)\n\n### Analysis \\u0026 Insights\n- [ ] Statistical significance tested (p-values, confidence intervals)\n- [ ] Outliers identified and handled appropriately\n- [ ] Multiple hypotheses tested (not just confirmation bias)\n- [ ] Insights validated with multiple data points\n\n### Reporting\n- [ ] Findings actionable (not just \"interesting facts\")\n- [ ] Visualizations clear and accurate\n- [ ] Limitations acknowledged\n- [ ] Recommendations prioritized by impact\n\n---\n",{"name":5,"version":24,"description":7,"description_en":25,"description_zh":26},"1.0.0","Market research specialist for analysis, consumer insights, and sizing","市场调研专家，提供市场分析、消费者洞察与机会评估","main","87afe6ce96220accf50a345dea46784c8c9f210e","YYC3-Skills-技能包\u002FYYC3-Skills-技能包-code-skills\u002Fskills\u002Fmarket-researcher",0,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base","https:\u002F\u002Fkb.yyc3.top\u002F","https:\u002F\u002Fgithub.com\u002FYYC-Cube","万象归元于云枢 | 深栈智启新纪元","2026-11-10 10:37:32",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill market-researcher",{"tool":42,"command":43},"bunx","bunx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill market-researcher",{"tool":45,"command":46},"pnpm","pnpm dlx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill market-researcher","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-code-skills\u002Fskills\u002Fmarket-researcher\u002FSKILL.md",true,[],[51,58,65,72,79,86],{"skillNo":52,"name":53,"nameZh":6,"description":54,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":57,"publishedAt":19},"01M7CTDWHERCXPQCP0K03A86AS","vercel-sandbox","Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. Triggers include \"Vercel Sandbox browser\", \"microVM Chrome\", \"agent-browser in sandbox\", \"browser automation on Vercel\", or any task requiring Chrome in a Vercel Sandbox.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fvercel-sandbox\u002FSKILL.md",[],[],{"skillNo":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":19},"01M7CTD8D2BNJY54S65M47HEJA","slack","Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include \"check my Slack\", \"what channels have unreads\", \"send a message to\", \"search Slack for\", \"extract from Slack\", \"find who said\", or any task requiring programmatic Slack interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fslack\u002FSKILL.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":19},"01M7CTCSSB8ZP3Q58C3YZDZK1W","electron","Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. Use when the user needs to interact with an Electron app, automate a desktop app, connect to a running app, control a native app, or test an Electron application. Triggers include \"automate Slack app\", \"control VS Code\", \"interact with Discord app\", \"test this Electron app\", \"connect to desktop app\", or any task requiring automation of a native Electron application.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Felectron\u002FSKILL.md",[],[],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":19},"01M7CTCBJY76688V4DCZGWFHGQ","dogfood","Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to \"dogfood\", \"QA\", \"exploratory test\", \"find issues\", \"bug hunt\", \"test this app\u002Fsite\u002Fplatform\", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fdogfood\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":19},"01M7CTC4H0FFGVEAQR9PA4GA1A","agent-browser","Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to \"open a website\", \"fill out a form\", \"click a button\", \"take a screenshot\", \"scrape data from a page\", \"test this web app\", \"login to a site\", \"automate browser actions\", or any task requiring programmatic web interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fagent-browser\u002FSKILL.md",[],[],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":19},"01M7CTBKVFYBG78FDSABSDJTTQ","shadcn","Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn\u002Fui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for \"shadcn init\", \"create an app with --preset\", \"switch to --preset\", and for building a design system or applying a DESIGN.md or brand spec to shadcn\u002Fui.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-UI-界面组件\u002FYYC3-UI-界面组件-ui\u002Fskills\u002Fshadcn\u002FSKILL.md",[],[]]