[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718PFCRAXVY3V3B8CFER9H7":3,"skill-same:01M718PFCRAXVY3V3B8CFER9H7":64},{"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":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":45,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M718PFCRAXVY3V3B8CFER9H7","paid-search-ads-playbook",null,"Google and Microsoft paid search planning: structure, keywords, match types, negatives, RSAs, bidding lens, and landing-page alignment for conversion-focused spend.","narayan-metaflow\u002Fmetaflow-marketing-skills","narayan-metaflow","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F181216517?v=4","01M718MB0Y27AYKTC9ZKT16V86","narayan","01M718MB209XRYRTS3ZZFK0JXS",19,"MIT","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fpaid-search-ads-playbook\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"paid","search","ads","playbook","paid search ads playbook","landing-page","match","google","microsoft","structure","conversion","landing","page","for","planning","types","lens","alignment","2026-11-10 10:12:30",false,"---\nname: paid-search-ads-playbook\ndescription: \"Google and Microsoft paid search planning: structure, keywords, match types, negatives, RSAs, bidding lens, and landing-page alignment for conversion-focused spend.\"\n---\n\n## When to Activate\n\nUse this skill for Google Ads (Microsoft Ads analogs) planning: structure, keywords, match types, negatives, RSAs, bidding lens, and landing-page alignment. This is **paid search**, not organic SEO—intent is conversion and efficient spend, not rankings.\n\n## First Questions\n\n1. **Business & offer** — What sells, margin, and primary conversion (lead, purchase, trial)?\n2. **Geo & language** — Countries, cities, languages?\n3. **Budget & goal** — Daily\u002Fmonthly cap, target CPA\u002FROAS (or learning phase expectations)?\n4. **Funnel stage** — Brand, non-brand, competitor, remarketing?\n5. **Existing assets** — Live account structure (if any), feed\u002FSAIQ, conversion tags health?\n6. **Landing pages** — URLs per offer; mobile speed and message match?\n\n---\n\n## Account & Campaign Structure\n\n- **Separation of intent**: Brand, generic non-brand, competitor, and remarketing belong in distinct campaigns (or clear ad groups with budget control at campaign level).\n- **Naming**: Consistent convention (geo | network | intent | offer).\n- **Budget**: Avoid dilution—fewer strong campaigns beat many starved ones early.\n- **Networks**: Search vs Performance Max \u002F Display—do not mix goals blindly; call out when PMax or Display changes assumptions.\n\n---\n\n## Keywords & Match Types\n\n- **Match types** (modern Google): Exact and Phrase are precision tools; Broad requires strong negatives and monitoring.\n- **Themes**: One tight theme per ad group (5–15 closely related keywords, not kitchen-sink lists).\n- **Cannibalization**: Same keyword in multiple ad groups\u002Fcampaigns causes auction confusion—consolidate or use negatives to sculpt.\n- **Expansion**: Mine search terms reports (conceptually); add winners as keywords, losers as negatives.\n\n---\n\n## Negative Keywords\n\n- Build **shared negative lists** (geo exclusions, job seekers, free-only intent, irrelevant industries).\n- **Ad-group-level negatives** to sculpt traffic between sibling groups.\n- Review weekly in early phases; stale negatives hide volume.\n\n---\n\n## Responsive Search Ads (RSAs)\n\n- **15 headlines \u002F 4 descriptions** where possible; pin only when legally or brand-required (pinning reduces combinations).\n- **Coverage**: At least 3 distinct value props, 2 CTAs, 1 keyword-rich headline, 1 trust\u002Fcredibility.\n- **Policy**: Avoid superlatives without proof; medical\u002Ffinancial sensitivity.\n\n---\n\n## Bidding & Goals (Framing, Not Live Optimization)\n\n- **Learning**: Major changes reset learning; batch edits.\n- **tCPA \u002F tROAS**: Need conversion volume; suggest thresholds honestly.\n- **Manual \u002F eCPC**: When automation lacks data.\n- **Portfolio**: Align bid strategy with actual conversion lag and value rules.\n\n---\n\n## Conversion Tracking & Quality\n\n- **Primary vs secondary** conversions—avoid optimizing on weak proxies.\n- **Enhanced conversions** \u002F consent—note when relevant.\n- **Landing page**: Message match headline ↔ ad; one clear CTA; speed and mobile UX.\n\n---\n\n## Tools and data sources (keyword\u002FAPI + page read)\n\n- **Volume & CPC signals**: `seo_search_volume`, `seo_keyword_ideas`, `seo_related_keywords`, `seo_keyword_suggestions` for seeds and expansion.\n- **Paid estimates**: `seo_ad_traffic_by_keywords` for impression\u002Fclick\u002Fcost scenarios when comparing bid levels.\n- **SERP context**: `seo_serp_analysis` when auction insight helps (ads + organic layout).\n- **Landing pages**: `web_scrape` to audit copy, CTAs, and offer clarity—never guess page content without a URL.\n\nBe explicit: tool outputs are **estimates**; validate in the ad platform.\n\n---\n\n## Quality Gate (before final deliverable)\n\n- [ ] Campaign\u002Fad group map matches intent separation (brand vs non-brand vs competitor vs RLSA)\n- [ ] Each ad group has a tight theme; no duplicate keyword conflicts across groups\n- [ ] Match type choices justified; broad groups paired with negative strategy\n- [ ] Negative keyword lists proposed (shared + ad-group sculpting)\n- [ ] RSAs: enough unpinned assets; pins only where required\n- [ ] Bidding lens matches data volume and business goal\n- [ ] Conversion tracking assumptions stated; gaps flagged\n- [ ] Landing page message match called out per major ad group\n- [ ] Next 7-day action list for the user (what to build first in-platform)\n","## When to Activate\n\nUse this skill for Google Ads (Microsoft Ads analogs) planning: structure, keywords, match types, negatives, RSAs, bidding lens, and landing-page alignment. This is **paid search**, not organic SEO—intent is conversion and efficient spend, not rankings.\n\n## First Questions\n\n1. **Business & offer** — What sells, margin, and primary conversion (lead, purchase, trial)?\n2. **Geo & language** — Countries, cities, languages?\n3. **Budget & goal** — Daily\u002Fmonthly cap, target CPA\u002FROAS (or learning phase expectations)?\n4. **Funnel stage** — Brand, non-brand, competitor, remarketing?\n5. **Existing assets** — Live account structure (if any), feed\u002FSAIQ, conversion tags health?\n6. **Landing pages** — URLs per offer; mobile speed and message match?\n\n---\n\n## Account & Campaign Structure\n\n- **Separation of intent**: Brand, generic non-brand, competitor, and remarketing belong in distinct campaigns (or clear ad groups with budget control at campaign level).\n- **Naming**: Consistent convention (geo | network | intent | offer).\n- **Budget**: Avoid dilution—fewer strong campaigns beat many starved ones early.\n- **Networks**: Search vs Performance Max \u002F Display—do not mix goals blindly; call out when PMax or Display changes assumptions.\n\n---\n\n## Keywords & Match Types\n\n- **Match types** (modern Google): Exact and Phrase are precision tools; Broad requires strong negatives and monitoring.\n- **Themes**: One tight theme per ad group (5–15 closely related keywords, not kitchen-sink lists).\n- **Cannibalization**: Same keyword in multiple ad groups\u002Fcampaigns causes auction confusion—consolidate or use negatives to sculpt.\n- **Expansion**: Mine search terms reports (conceptually); add winners as keywords, losers as negatives.\n\n---\n\n## Negative Keywords\n\n- Build **shared negative lists** (geo exclusions, job seekers, free-only intent, irrelevant industries).\n- **Ad-group-level negatives** to sculpt traffic between sibling groups.\n- Review weekly in early phases; stale negatives hide volume.\n\n---\n\n## Responsive Search Ads (RSAs)\n\n- **15 headlines \u002F 4 descriptions** where possible; pin only when legally or brand-required (pinning reduces combinations).\n- **Coverage**: At least 3 distinct value props, 2 CTAs, 1 keyword-rich headline, 1 trust\u002Fcredibility.\n- **Policy**: Avoid superlatives without proof; medical\u002Ffinancial sensitivity.\n\n---\n\n## Bidding & Goals (Framing, Not Live Optimization)\n\n- **Learning**: Major changes reset learning; batch edits.\n- **tCPA \u002F tROAS**: Need conversion volume; suggest thresholds honestly.\n- **Manual \u002F eCPC**: When automation lacks data.\n- **Portfolio**: Align bid strategy with actual conversion lag and value rules.\n\n---\n\n## Conversion Tracking & Quality\n\n- **Primary vs secondary** conversions—avoid optimizing on weak proxies.\n- **Enhanced conversions** \u002F consent—note when relevant.\n- **Landing page**: Message match headline ↔ ad; one clear CTA; speed and mobile UX.\n\n---\n\n## Tools and data sources (keyword\u002FAPI + page read)\n\n- **Volume & CPC signals**: `seo_search_volume`, `seo_keyword_ideas`, `seo_related_keywords`, `seo_keyword_suggestions` for seeds and expansion.\n- **Paid estimates**: `seo_ad_traffic_by_keywords` for impression\u002Fclick\u002Fcost scenarios when comparing bid levels.\n- **SERP context**: `seo_serp_analysis` when auction insight helps (ads + organic layout).\n- **Landing pages**: `web_scrape` to audit copy, CTAs, and offer clarity—never guess page content without a URL.\n\nBe explicit: tool outputs are **estimates**; validate in the ad platform.\n\n---\n\n## Quality Gate (before final deliverable)\n\n- [ ] Campaign\u002Fad group map matches intent separation (brand vs non-brand vs competitor vs RLSA)\n- [ ] Each ad group has a tight theme; no duplicate keyword conflicts across groups\n- [ ] Match type choices justified; broad groups paired with negative strategy\n- [ ] Negative keyword lists proposed (shared + ad-group sculpting)\n- [ ] RSAs: enough unpinned assets; pins only where required\n- [ ] Bidding lens matches data volume and business goal\n- [ ] Conversion tracking assumptions stated; gaps flagged\n- [ ] Landing page message match called out per major ad group\n- [ ] Next 7-day action list for the user (what to build first in-platform)\n",{"name":5,"description":7},"main","c137446804eb85b2cc0ba009c62068253a7c47be","skills\u002Fpaid-search-ads-playbook",2,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow","engineer by education, marketer by profession, building rn","2026-09-16 13:41:44",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill paid-search-ads-playbook",{"tool":56,"command":57},"bunx","bunx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill paid-search-ads-playbook",{"tool":59,"command":60},"pnpm","pnpm dlx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill paid-search-ads-playbook","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fpaid-search-ads-playbook\u002FSKILL.md",true,[],[65,99,128,148,176,203],{"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":81,"publishedAt":37},"01M718Q16XB7D7ZMGNNA1226G5","technical-seo-ai-crawler-audit","Diagnose and prioritize the technical issues blocking a site from being crawled, indexed, ranked, and cited. Modern technical SEO has two audiences: Google's crawler\u002Findexer and AI answer engines (GPTBot, PerplexityBot, ClaudeBot, etc.). Both need HTML-visible content and a clean crawl path.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Ftechnical-seo-ai-crawler-audit\u002FSKILL.md",[71,76],{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[82,83,84,85,86,87,88,89,90,91,92,93,26,94,95,96,97,98],"seo","technical","technical seo","technical seo ai crawler audit","audit","crawler","crawl","diagnose","answer","issues","clean","indexer","prioritize","site","content","html","path",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":110,"publishedAt":37},"01M718PY2PKA7BYS5KNJ2A9DRT","seo-reporting-measurement","Build reports that stakeholders trust, rooted in data sources that are actually reliable. Modern SEO measurement has to reckon with a very specific constraint: **the primary data source (GSC) is ~75% incomplete**, and the AI visibility layer is **probabilistic**, not deterministic. Building reports that ignore these realities produces false confidence.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-reporting-measurement\u002FSKILL.md",[105,106,109],{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},{"categoryNo":107,"categoryName":108,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000033","数据分析师",{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},[111,112,113,114,82,115,116,117,118,119,120,121,122,123,124,125,126,127],"seo reporting","seo reporting measurement","reporting","measurement","sources","gsc","false","reports","data sources","data","layer","source","deterministic","trust","with","build","constraint",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":136,"publishedAt":37},"01M718PVF0SP35J6S9EHRGQZQ1","seo-max-kt","Long-form SEO and AEO methodology bundle; read SEO_AND_AEO_KT.md for the full corpus.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-max-kt\u002FSKILL.md",[134,135],{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},[137,82,138,139,140,141,142,143,32,144,145,146,147],"kt","seo max kt","max","aeo","long-form","methodology","form","bundle","full","read","long",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":159,"publishedAt":37},"01M718PRQNX2JF71GBFTH3MV19","seo-keyword-research-intent-mapping","Build a keyword universe that reflects how both humans *and* AI answer engines search. Traditional keyword research looks at standalone terms; modern research also maps the **fan-out sub-queries** AI tools generate when breaking down a user prompt.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-keyword-research-intent-mapping\u002FSKILL.md",[154,155,158],{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},{"categoryNo":156,"categoryName":157,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},[82,160,161,162,163,164,165,166,167,168,90,20,169,170,171,172,173,126,174,175],"seo keyword research","seo keyword research intent mapping","research","intent","keyword research","mapping","ai tools","standalone","down","prompt","when","maps","queries","generate","how","at",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":190,"publishedAt":37},"01M718PNDT3A19SV7KVWC9083P","seo-aeo-content-strategy","Design content systems that earn both Google rankings *and* AI citations. The core insight: AI answer engines read content differently than humans — they extract the highest-information-gain sentences and treat H2 headings as prompts. The \"ski ramp\" structure optimizes for both.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-aeo-content-strategy\u002FSKILL.md",[182,185,186,187],{"categoryNo":183,"categoryName":184,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":72,"categoryName":73,"taxonomyType":74,"taxonomyTypeDesc":75},{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},{"categoryNo":188,"categoryName":189,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000036","内容创作者",[82,140,191,192,193,96,194,195,196,32,90,197,198,199,28,146,200,201,26,202],"strategy","seo aeo content strategy","content strategy","Citations","as","core","design","information","earn","systems","ramp","prompts",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":212,"publishedAt":37},"01M718PJM103BE7581G6NRCC7T","prompt-picker","Build a prompt library that reveals how a brand shows up across the full buyer journey in AI search systems. The library is designed for actual LLM visibility tracking, not theoretical SEO keyword research.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fprompt-picker\u002FSKILL.md",[209],{"categoryNo":210,"categoryName":211,"taxonomyType":74,"taxonomyTypeDesc":75},"01JBSEEDCAT000000000000023","Skill与智能体开发",[213,169,214,215,82,32,160,20,216,217,218,219,162,126,174,200,164,220,221,145],"prompt picker","llm visibility","brand","up","journey","prompt library","across","library","tracking"]