[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718P4JF2G5XZZYTZ3YT8905":3,"skill-same:01M718P4JF2G5XZZYTZ3YT8905":67},{"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":23,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":51,"updatedAt":52,"install":53,"resources":66},"01M718P4JF2G5XZZYTZ3YT8905","meta-ads-playbook",null,"Meta ads planning playbook: objectives, structure, audiences, creative, testing cadence, Pixel\u002FCAPI measurement. Does not assume live Marketing API access.","narayan-metaflow\u002Fmetaflow-marketing-skills","narayan-metaflow","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F181216517?v=4","01M718MB0Y27AYKTC9ZKT16V86","narayan","01M718MB209XRYRTS3ZZFK0JXS",20,"MIT","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmeta-ads-playbook\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"Meta","playbook","ads","meta ads","meta ads playbook","testing","planning","api","access","marketing","creative","pixel","structure","live","cadence","measurement","2026-11-10 10:12:30",false,"---\nname: meta-ads-playbook\ndescription: \"Meta ads planning playbook: objectives, structure, audiences, creative, testing cadence, Pixel\u002FCAPI measurement. Does not assume live Marketing API access.\"\n---\n\n## When to Activate\n\nUse this skill for Meta ads planning: objective selection, account structure, audiences, creative formats, copy, testing cadence, and measurement concepts (pixel, CAPI). **Live Meta Marketing API access is not assumed** unless the user has connected integrations—use `llm_call` (e.g. Perplexity) for policy and trend research and `web_scrape` for URLs the user provides.\n\n## First Questions\n\n1. **Offer & conversion** — Purchase, lead, app install, engagement? Value per conversion?\n2. **Geo & placements** — Countries; Feed, Stories, Reels, RHS; any placement exclusions?\n3. **Budget & duration** — Daily cap, flight dates, learning phase tolerance?\n4. **Creative assets** — Video UGC, static, catalog\u002FDPAs, existing hooks?\n5. **Data** — Pixel\u002FCAPI status, offline conversions, lists (customer file, engagement)?\n6. **Brand constraints** — Disclaimers, restricted categories, tone?\n\n---\n\n## Objectives & Structure\n\n- Match **campaign objective** to true outcome (Sales \u002F Leads \u002F Traffic \u002F Awareness)—avoid optimizing for clicks when you need purchases.\n- **ASC \u002F Advantage+ shopping** vs manual: spell out tradeoffs (creative control vs automation).\n- **Consolidate** when learning is fragmented; **separate** when budgets or offers truly differ.\n\n---\n\n## Audiences\n\n- **Prospecting**: Broad + creative feed, or interest\u002Fstacked signals when niche.\n- **Remarketing**: Site, engagers, customer lists—frequency caps and creative refresh.\n- **Lookalikes** (where available): Seed quality matters more than percentage size.\n- **Exclusions**: Purchasers, low-intent site sections, employees.\n\n---\n\n## Creative\n\n- **Formats**: Single image, carousel, collection, Reels, short video—match platform norms (safe zones, thumb-stop in first 2s).\n- **Angles**: Pain, outcome, social proof, objection handling, urgency (honest)—rotate 3–5 concepts.\n- **Copy**: Primary text variants, headline, description; CTA button alignment with landing promise.\n- **Fatigue**: Plan refresh cadence and naming for tests.\n\n---\n\n## Testing\n\n- **Hypothesis**: One clear variable (audience vs creative vs landing)—avoid multivariate soup.\n- **Duration & spend**: Enough conversions per cell; don’t call winners on noise.\n- **Document**: What was tested, result, next iteration.\n\n---\n\n## Measurement & Privacy\n\n- **Pixel + CAPI**: Redundancy for web events; server-side helps iOS\u002Fsignal loss—conceptual checklist only unless user provides integration status.\n- **Attribution**: Call out that Meta reporting ≠ last-click GA; compare definitions when user has both.\n- **Consent**: Mention when ads touch EU\u002FUK and consent mode matters.\n\n---\n\n## Tools and data sources\n\n- **`llm_call`**: Current policy questions, format specs, competitor **public** positioning research—do not fabricate metrics.\n- **`web_scrape`**: User-supplied landing pages, help articles, public policy pages.\n\nIf the user connects Meta via app integrations, Composio tools may apply—**never guess** tool slugs; follow integration search workflow when available.\n\n---\n\n## Quality Gate (before final deliverable)\n\n- [ ] Objective matches business outcome and conversion definition\n- [ ] Campaign structure maps to prospecting vs retargeting (or catalog) clearly\n- [ ] Audience strategy stated with exclusions\n- [ ] Creative matrix: ≥3 angles × stated formats; hooks for video\n- [ ] Primary text \u002F headline variants provided or templated\n- [ ] Test plan: hypothesis, budget split, minimum runtime guidance\n- [ ] Measurement checklist (pixel\u002FCAPI\u002Foffline) with gaps flagged\n- [ ] Limitations stated: no live account data unless user provides exports or integrations\n","## When to Activate\n\nUse this skill for Meta ads planning: objective selection, account structure, audiences, creative formats, copy, testing cadence, and measurement concepts (pixel, CAPI). **Live Meta Marketing API access is not assumed** unless the user has connected integrations—use `llm_call` (e.g. Perplexity) for policy and trend research and `web_scrape` for URLs the user provides.\n\n## First Questions\n\n1. **Offer & conversion** — Purchase, lead, app install, engagement? Value per conversion?\n2. **Geo & placements** — Countries; Feed, Stories, Reels, RHS; any placement exclusions?\n3. **Budget & duration** — Daily cap, flight dates, learning phase tolerance?\n4. **Creative assets** — Video UGC, static, catalog\u002FDPAs, existing hooks?\n5. **Data** — Pixel\u002FCAPI status, offline conversions, lists (customer file, engagement)?\n6. **Brand constraints** — Disclaimers, restricted categories, tone?\n\n---\n\n## Objectives & Structure\n\n- Match **campaign objective** to true outcome (Sales \u002F Leads \u002F Traffic \u002F Awareness)—avoid optimizing for clicks when you need purchases.\n- **ASC \u002F Advantage+ shopping** vs manual: spell out tradeoffs (creative control vs automation).\n- **Consolidate** when learning is fragmented; **separate** when budgets or offers truly differ.\n\n---\n\n## Audiences\n\n- **Prospecting**: Broad + creative feed, or interest\u002Fstacked signals when niche.\n- **Remarketing**: Site, engagers, customer lists—frequency caps and creative refresh.\n- **Lookalikes** (where available): Seed quality matters more than percentage size.\n- **Exclusions**: Purchasers, low-intent site sections, employees.\n\n---\n\n## Creative\n\n- **Formats**: Single image, carousel, collection, Reels, short video—match platform norms (safe zones, thumb-stop in first 2s).\n- **Angles**: Pain, outcome, social proof, objection handling, urgency (honest)—rotate 3–5 concepts.\n- **Copy**: Primary text variants, headline, description; CTA button alignment with landing promise.\n- **Fatigue**: Plan refresh cadence and naming for tests.\n\n---\n\n## Testing\n\n- **Hypothesis**: One clear variable (audience vs creative vs landing)—avoid multivariate soup.\n- **Duration & spend**: Enough conversions per cell; don’t call winners on noise.\n- **Document**: What was tested, result, next iteration.\n\n---\n\n## Measurement & Privacy\n\n- **Pixel + CAPI**: Redundancy for web events; server-side helps iOS\u002Fsignal loss—conceptual checklist only unless user provides integration status.\n- **Attribution**: Call out that Meta reporting ≠ last-click GA; compare definitions when user has both.\n- **Consent**: Mention when ads touch EU\u002FUK and consent mode matters.\n\n---\n\n## Tools and data sources\n\n- **`llm_call`**: Current policy questions, format specs, competitor **public** positioning research—do not fabricate metrics.\n- **`web_scrape`**: User-supplied landing pages, help articles, public policy pages.\n\nIf the user connects Meta via app integrations, Composio tools may apply—**never guess** tool slugs; follow integration search workflow when available.\n\n---\n\n## Quality Gate (before final deliverable)\n\n- [ ] Objective matches business outcome and conversion definition\n- [ ] Campaign structure maps to prospecting vs retargeting (or catalog) clearly\n- [ ] Audience strategy stated with exclusions\n- [ ] Creative matrix: ≥3 angles × stated formats; hooks for video\n- [ ] Primary text \u002F headline variants provided or templated\n- [ ] Test plan: hypothesis, budget split, minimum runtime guidance\n- [ ] Measurement checklist (pixel\u002FCAPI\u002Foffline) with gaps flagged\n- [ ] Limitations stated: no live account data unless user provides exports or integrations\n",{"name":5,"description":7},"main","c137446804eb85b2cc0ba009c62068253a7c47be","skills\u002Fmeta-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":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill meta-ads-playbook",{"tool":59,"command":60},"bunx","bunx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill meta-ads-playbook",{"tool":62,"command":63},"pnpm","pnpm dlx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill meta-ads-playbook","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmeta-ads-playbook\u002FSKILL.md",true,[],[68,101,129,150,179,206],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":82,"publishedAt":40},"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",[74,77],{"categoryNo":75,"categoryName":76,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],"seo","technical","technical seo","technical seo ai crawler audit","audit","crawler","crawl","diagnose","answer","issues","clean","indexer","google","prioritize","site","content","html","path",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":112,"publishedAt":40},"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",[107,108,111],{"categoryNo":75,"categoryName":76,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":109,"categoryName":110,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000033","数据分析师",{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},[113,114,115,39,83,116,117,118,119,120,121,122,123,124,125,126,127,128],"seo reporting","seo reporting measurement","reporting","sources","gsc","false","reports","data sources","data","layer","source","deterministic","trust","with","build","constraint",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":137,"publishedAt":40},"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",[135,136],{"categoryNo":75,"categoryName":76,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},[138,83,139,140,141,142,143,144,145,146,147,148,149],"kt","seo max kt","max","aeo","long-form","methodology","form","for","bundle","full","read","long",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":161,"publishedAt":40},"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",[156,157,160],{"categoryNo":75,"categoryName":76,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":158,"categoryName":159,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},[83,162,163,164,165,166,167,168,169,170,91,171,172,173,174,175,176,127,177,178],"seo keyword research","seo keyword research intent mapping","research","intent","keyword research","mapping","ai tools","standalone","down","search","prompt","when","maps","queries","generate","how","at",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":193,"publishedAt":40},"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",[185,188,189,190],{"categoryNo":186,"categoryName":187,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":75,"categoryName":76,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},{"categoryNo":191,"categoryName":192,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000036","内容创作者",[83,141,194,195,196,98,197,198,199,145,91,200,201,202,36,148,203,204,95,205],"strategy","seo aeo content strategy","content strategy","Citations","as","core","design","information","earn","systems","ramp","prompts",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":215,"publishedAt":40},"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",[212],{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[216,172,217,218,83,145,162,171,219,220,221,222,164,127,177,203,166,223,224,147],"prompt picker","llm visibility","brand","up","journey","prompt library","across","library","tracking"]