[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C2TP3Y62KXRN3M3AJNEHPJ":3,"skill-same:01M6C2TP3Y62KXRN3M3AJNEHPJ":98},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":29,"publishedAt":56,"linkOnly":57,"markdownContent":58,"markdownBody":59,"frontMatter":60,"refName":67,"commitSha":68,"skillDirectory":69,"forksCount":14,"repositoryHtmlUrl":70,"repositoryHomepageUrl":6,"ownerHtmlUrl":71,"ownerBlogUrl":6,"ownerDescription":72,"updatedAt":73,"install":74,"resources":86},"01M6C2TP3Y62KXRN3M3AJNEHPJ","seo-page",null,"Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says \"analyze this page\", \"check page SEO\", \"single URL\", \"check this page\", \"page analysis\", or provides a single URL for review.","noirelab\u002Flinux-setup","noirelab","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142925921?v=4","01M6C29R3FEDGFAXAT0KXY28QX","Kaíque M. Lima","01M6C29R811WX04B0GYHTH1XV6",0,"MIT","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode\u002Fskills\u002Fseo-page\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode\u002Fskills\u002Fseo-page\u002FSKILL.md",[19,24],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":25,"categoryName":26,"taxonomyType":27,"taxonomyTypeDesc":28},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"seo","page","seo page","use","when","single","page seo analysis","for","true","URL","seo analysis","elements","deep","images","schema","review","user","check","technical","quality","analyze","Meta Tags","analysis","Meta","content","performance","2026-11-10 10:12:30",false,"---\nname: seo-page\ndescription: >\n  Deep single-page SEO analysis covering on-page elements, content quality,\n  technical meta tags, schema, images, and performance. Use when user says\n  \"analyze this page\", \"check page SEO\", \"single URL\", \"check this page\",\n  \"page analysis\", or provides a single URL for review.\nuser-invocable: true\nargument-hint: \"[url]\"\nlicense: MIT\nmetadata:\n  author: AgriciDaniel\n  version: \"2.2.4\"\n  category: seo\n---\n\n# Single Page Analysis\n\n## What to Analyze\n\n### On-Page SEO\n- Title tag: 50-60 characters, includes primary keyword, unique\n- Meta description: 150-160 characters, compelling, includes keyword\n- H1: exactly one, matches page intent, includes keyword\n- H2-H6: logical hierarchy (no skipped levels), descriptive\n- URL: short, descriptive, hyphenated, no parameters\n- Internal links: sufficient, relevant anchor text, no orphan pages\n- External links: to authoritative sources, reasonable count\n\n### Content Quality\n- Word count vs page type minimums (see quality-gates.md)\n- Readability: Flesch Reading Ease score, grade level\n- Keyword density: natural (1-3%), semantic variations present\n- E-E-A-T signals: author bio, credentials, first-hand experience markers\n- Content freshness: publication date, last updated date\n\n### Technical Elements\n- Canonical tag: present, self-referencing or correct\n- Meta robots: index\u002Ffollow unless intentionally blocked\n- Open Graph: og:title, og:description, og:image, og:url\n- Twitter Card: twitter:card, twitter:title, twitter:description\n- Hreflang: if multi-language, correct implementation\n\n### Schema Markup\n- Detect all types (JSON-LD preferred)\n- Validate required properties\n- Identify missing opportunities\n- Never recommend HowTo (deprecated) or FAQ for rich results (retired May 2026); existing FAQPage need not be removed, use QAPage for genuine Q&A\n\n### Images\n- Alt text: present, descriptive, includes keywords where natural\n- File size: flag >200KB (warning), >500KB (critical)\n- Format: recommend WebP\u002FAVIF over JPEG\u002FPNG\n- Dimensions: width\u002Fheight set for CLS prevention\n- Lazy loading: report `lazy_method` per image (native | perfmatters | ewww | js-generic | none). Do not flag \"not lazy-loaded\" when JS lazy-loaders (Perfmatters, EWWW, lazysizes) are detected, they intentionally strip the native `loading=\"lazy\"` attribute and use `data-src` placeholders\n\n### Core Web Vitals (reference only, not measurable from HTML alone)\n- Flag potential LCP issues (huge hero images, render-blocking resources)\n- Flag potential INP issues (heavy JS, no async\u002Fdefer)\n- Flag potential CLS issues (missing image dimensions, injected content)\n\n## Output\n\n### Page Score Card\n```\nOverall Score: XX\u002F100\n\nOn-Page SEO:     XX\u002F100  ████████░░\nContent Quality: XX\u002F100  ██████████\nTechnical:       XX\u002F100  ███████░░░\nSchema:          XX\u002F100  █████░░░░░\nImages:          XX\u002F100  ████████░░\n```\n\n### Issues Found\nOrganized by priority: Critical -> High -> Medium -> Low\n\n### Recommendations\nSpecific, actionable improvements with expected impact\n\n### Schema Suggestions\nReady-to-use JSON-LD code for detected opportunities\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `serp_organic_live_advanced` for real SERP positions and `backlinks_summary` for backlink data and spam scores.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again. |\n| Page requires authentication (401\u002F403) | Report that the page is behind authentication. Suggest the user provide the rendered HTML directly or a publicly accessible URL. |\n| JavaScript-rendered content (empty body in HTML) | Note that key content may be rendered client-side. Analyze the available HTML and flag that results may be incomplete. Suggest using a browser-rendered snapshot if available. |\n","# Single Page Analysis\n\n## What to Analyze\n\n### On-Page SEO\n- Title tag: 50-60 characters, includes primary keyword, unique\n- Meta description: 150-160 characters, compelling, includes keyword\n- H1: exactly one, matches page intent, includes keyword\n- H2-H6: logical hierarchy (no skipped levels), descriptive\n- URL: short, descriptive, hyphenated, no parameters\n- Internal links: sufficient, relevant anchor text, no orphan pages\n- External links: to authoritative sources, reasonable count\n\n### Content Quality\n- Word count vs page type minimums (see quality-gates.md)\n- Readability: Flesch Reading Ease score, grade level\n- Keyword density: natural (1-3%), semantic variations present\n- E-E-A-T signals: author bio, credentials, first-hand experience markers\n- Content freshness: publication date, last updated date\n\n### Technical Elements\n- Canonical tag: present, self-referencing or correct\n- Meta robots: index\u002Ffollow unless intentionally blocked\n- Open Graph: og:title, og:description, og:image, og:url\n- Twitter Card: twitter:card, twitter:title, twitter:description\n- Hreflang: if multi-language, correct implementation\n\n### Schema Markup\n- Detect all types (JSON-LD preferred)\n- Validate required properties\n- Identify missing opportunities\n- Never recommend HowTo (deprecated) or FAQ for rich results (retired May 2026); existing FAQPage need not be removed, use QAPage for genuine Q&A\n\n### Images\n- Alt text: present, descriptive, includes keywords where natural\n- File size: flag >200KB (warning), >500KB (critical)\n- Format: recommend WebP\u002FAVIF over JPEG\u002FPNG\n- Dimensions: width\u002Fheight set for CLS prevention\n- Lazy loading: report `lazy_method` per image (native | perfmatters | ewww | js-generic | none). Do not flag \"not lazy-loaded\" when JS lazy-loaders (Perfmatters, EWWW, lazysizes) are detected, they intentionally strip the native `loading=\"lazy\"` attribute and use `data-src` placeholders\n\n### Core Web Vitals (reference only, not measurable from HTML alone)\n- Flag potential LCP issues (huge hero images, render-blocking resources)\n- Flag potential INP issues (heavy JS, no async\u002Fdefer)\n- Flag potential CLS issues (missing image dimensions, injected content)\n\n## Output\n\n### Page Score Card\n```\nOverall Score: XX\u002F100\n\nOn-Page SEO:     XX\u002F100  ████████░░\nContent Quality: XX\u002F100  ██████████\nTechnical:       XX\u002F100  ███████░░░\nSchema:          XX\u002F100  █████░░░░░\nImages:          XX\u002F100  ████████░░\n```\n\n### Issues Found\nOrganized by priority: Critical -> High -> Medium -> Low\n\n### Recommendations\nSpecific, actionable improvements with expected impact\n\n### Schema Suggestions\nReady-to-use JSON-LD code for detected opportunities\n\n## DataForSEO Integration (Optional)\n\nIf DataForSEO MCP tools are available, use `serp_organic_live_advanced` for real SERP positions and `backlinks_summary` for backlink data and spam scores.\n\n## Error Handling\n\n| Scenario | Action |\n|----------|--------|\n| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again. |\n| Page requires authentication (401\u002F403) | Report that the page is behind authentication. Suggest the user provide the rendered HTML directly or a publicly accessible URL. |\n| JavaScript-rendered content (empty body in HTML) | Note that key content may be rendered client-side. Analyze the available HTML and flag that results may be incomplete. Suggest using a browser-rendered snapshot if available. |\n",{"name":5,"license":15,"metadata":61,"description":64,"argument-hint":65,"user-invocable":66},{"author":62,"version":63,"category":30},"AgriciDaniel","2.2.4","Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says \"analyze this page\", \"check page SEO\", \"single URL\", \"check this page\", \"page analysis\", or provides a single URL for review.\n","[url]",true,"main","d8378e461d60315bd1ca8a6e71716df39b6abe4a","configs\u002Fcommon\u002Fopencode\u002Fskills\u002Fseo-page","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup","https:\u002F\u002Fgithub.com\u002Fnoirelab","Artificial Intelligence Researcher |\r\nComputer Science majoring at UTFPR","2026-11-11 09:12:51",{"source":8,"skillName":5,"commands":75,"prompt":85,"zipAvailable":66},[76,79,82],{"tool":77,"command":78},"npx","npx skills add noirelab\u002Flinux-setup --skill seo-page",{"tool":80,"command":81},"bunx","bunx skills add noirelab\u002Flinux-setup --skill seo-page",{"tool":83,"command":84},"pnpm","pnpm dlx skills add noirelab\u002Flinux-setup --skill seo-page","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode\u002Fskills\u002Fseo-page\u002FSKILL.md",[87,92],{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":16},"LICENSE.txt","LICENSE","许可证",143,{"path":93,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fdetailed-guide.md","REFERENCE","参考文档",12899,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-javascript-mastery\u002Freferences\u002Fdetailed-guide.md",[99,127,162,187,207,223],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":104,"sourceHtmlUrl":105,"categories":106,"keywords":110,"publishedAt":56},"01M6C2Q1S6HCS7Y9TBQ2PJ2NAM","skill-creator","Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fsynced\u002Ffcfa9261-0073-49cb-8312-19eea68f0c58_391f3767-a35d-4280-81a1-363cd861a8e6\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fclaude\u002Fskills\u002Fsynced\u002Ffcfa9261-0073-49cb-8312-19eea68f0c58_391f3767-a35d-4280-81a1-363cd861a8e6\u002Fskill-creator\u002FSKILL.md",[107],{"categoryNo":108,"categoryName":109,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000023","Skill与智能体开发",[111,112,55,113,114,115,116,117,118,52,119,120,33,121,122,123,37,124,125,34,126],"skill creator","creator","run evals","test","variance analysis","create","run","benchmark","improve","edit","evals","optimize","existing skill","accuracy","scratch","new",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":133,"publishedAt":56},"01M6C2ZRGJ9HN4N9BP24DPNCR4","returns-reverse-logistics","Codified expertise for returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Informed by returns operations managers with 15+ years experience. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes. Use when handling product returns, reverse logistics, refund decisions, return fraud detection, or warranty claims.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Freturns-reverse-logistics\u002FSKILL.md",[],[134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,33,150,151,152,153,154,155,156,157,158,37,159,160,34,161],"returns","returns reverse logistics","reverse logistics","reverse","github","code","product","https","authorization","detection","fraud","recovery","decisions","management","operations","vendor","claims management","frameworks","fraud detection","pattern","grading","processing","economics","managers","claims","expertise","refund","inspection",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":168,"publishedAt":56},"01M6C2ZGWSNZGZZQ5VCFWHD2QT","quality-nonconformance","Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Fquality-nonconformance\u002FSKILL.md",[],[49,169,138,170,171,139,172,141,173,174,175,176,52,177,147,33,178,179,180,181,182,183,184,185,37,159,186,34],"quality nonconformance","root cause analysis","interpretation","issues","audit","data","methodology","lifecycle","investigation","fda","across","manufacturing","environments","corrective action","supplier quality management","action","conformance","root cause",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":193,"publishedAt":56},"01M6C2Z8VQ9AYZYKPZW1KD9DX5","production-scheduling","Codified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC\u002Fdrum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP\u002FMES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Fproduction-scheduling\u002FSKILL.md",[],[194,195,196,138,139,197,141,198,52,199,200,33,201,202,151,203,204,205,180,206,37,159,34],"Production","production scheduling","scheduling","batch","Optimization","erp","bottleneck","toc","patterns","line","interaction","sequencing","buffer",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":103,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":213,"publishedAt":56},"01M6C2YR01NNN2Z1G18RN7F9MP","logistics-exception-management","Codified expertise for handling freight exceptions, shipment delays, damages, losses, and carrier disputes. Informed by logistics professionals with 15+ years operational experience. Includes escalation protocols, carrier-specific behaviors, claims procedures, and judgment frameworks. Use when handling shipping exceptions, freight claims, delivery issues, or carrier disputes.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Flogistics-exception-management\u002FSKILL.md",[],[147,214,138,139,215,172,216,141,217,33,151,218,219,220,221,158,37,159,222,34],"logistics exception management","shipping","protocols","escalation","delivery","claude-code","judgment","with","freight",{"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":103,"licenseFileUrl":6,"sourceHtmlUrl":227,"categories":228,"keywords":229,"publishedAt":56},"01M6C2YC5PXTEJYBJJQZVXPQS0","inventory-demand-planning","Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC\u002FXYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.","https:\u002F\u002Fgithub.com\u002Fnoirelab\u002Flinux-setup\u002Fblob\u002Fmain\u002Fconfigs\u002Fcommon\u002Fopencode_home\u002Fskills\u002Finventory-demand-planning\u002FSKILL.md",[],[230,231,232,233,138,139,141,234,198,235,52,147,149,236,33,237,151,238,239,240,241,242,37,159,243,34],"planning","inventory","inventory demand planning","demand planning","negotiation","Safety","forecasting","selection","estimation","stock","seasonal","lift","at","transition"]