[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718MWJJR1Z52P3AS00Q6SWZ":3,"skill-same:01M718MWJJR1Z52P3AS00Q6SWZ":81},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":55,"updatedAt":56,"install":57,"resources":70},"01M718MWJJR1Z52P3AS00Q6SWZ","content-effort-audit",null,"Score visible effort evidence on specified URLs. Use when the user asks for a Content Effort audit, rater-style effort scoring, low-effort AI content review, or whether a page would look Lowest under QRG §3.2. Distinct from library keep\u002Frefresh\u002Fprune (seo-aeo-content-strategy) and from on-page ski-ramp implementation (on-page-seo-aeo-optimization).","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\u002Fcontent-effort-audit\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"audit","content","content effort audit","ramp","style","content-strategy","ai content","refresh","review","scoring","keep","when","use","strategy","aeo","Optimization","page","for","seo","evidence","2026-11-10 10:12:30",false,"---\nname: content-effort-audit\ndescription: \"Score visible effort evidence on specified URLs. Use when the user asks for a Content Effort audit, rater-style effort scoring, low-effort AI content review, or whether a page would look Lowest under QRG §3.2. Distinct from library keep\u002Frefresh\u002Fprune (seo-aeo-content-strategy) and from on-page ski-ramp implementation (on-page-seo-aeo-optimization).\"\n---\n\n# Content Effort Audit\n\nScore **visible effort** on finished URLs. Production hours, cost, and process do not count unless they left residue a stranger can point at.\n\n## Why this matters\n\nGoogle’s Search Quality Evaluator Guidelines judge Main Content quality by **effort, originality, and talent or skill**, plus accuracy on informational\u002FYMYL pages. 2025 updates made “little to no effort, originality, and added value” a **Lowest** path, including AI paraphrase and scaled content abuse. Generative AI **alone** does not determine the rating.\n\nThree evidence layers stay separate:\n\n1. **QRG effort** — documented rater criterion (primary source).\n2. **Leaked `contentEffort`** — reported LLM estimate for article pages. Mapping it to QRG is **inference**, not a Google statement.\n3. **People-first questions** — original information, first-hand expertise, not just rewriting sources.\n\nDo not brief “Content Effort is a confirmed ranking factor.” Operate as if visible effort is industrially scored. Label the leak as speculation.\n\nCross-links: library keep\u002Frefresh\u002Fprune → `seo-aeo-content-strategy`. Implementing effort adds on the page → `on-page-seo-aeo-optimization`.\n\n## Process\n\n1. **Classify page purpose.** Recipe, calculator, review, explainer, clip, documentary — the bar is relative to purpose, not word count.\n2. **Gather visible evidence only.** First-party facts, original artifacts, synthesis, craft, curation choices, added value vs substitutes. Ignore time logs, “human review” checkboxes, and origin stories that are not on the URL.\n3. **Score six signals, 0–2 each (0–12 total).** Use `references\u002Fvee-rubric.md`. Do not invent Google’s weights.\n4. **Map to QRG language.** 0–3 Lowest-risk evidence, 4–6 Low, 7–9 High, 10–12 Highest-evidence. Then apply the purpose modifier.\n5. **Recommend specific effort adds.** A zero on first-party information is a measurement or primary document, not “write more.” See the rubric’s add column.\n6. **Flag site-level scaled low-effort** only after sampling 3+ URLs that share a skeleton. One thin URL is a page problem.\n7. **Hand off implementation** to on-page SEO\u002FAEO for ski-ramp, titles, and schema. This skill stops at the effort verdict.\n\n## Output\n\nPer URL:\n\n| Field | Required |\n|---|---|\n| Purpose class | Yes |\n| Six-signal table (0–2) | Yes |\n| Total \u002F12 and band | Yes |\n| QRG-aligned risk (Lowest \u002F Low \u002F High evidence) | Yes |\n| Missing-evidence list | Yes |\n| Prioritized effort adds | Yes |\n| Leak column labeled inference | Yes |\n| Scaled low-effort flag | Optional, after 3+ URL sample |\n\nWorked pair: `references\u002Fworked-examples.md`.\n\n## Rules\n\n- Visible effort ≠ production effort.\n- Origin (human vs AI) ≠ rating.\n- Word count is not a signal.\n- Do not coin a fake “Google Content Effort score.”\n- Do not collapse this audit into E-E-A-T or keep\u002Frefresh\u002Fprune.\n","# Content Effort Audit\n\nScore **visible effort** on finished URLs. Production hours, cost, and process do not count unless they left residue a stranger can point at.\n\n## Why this matters\n\nGoogle’s Search Quality Evaluator Guidelines judge Main Content quality by **effort, originality, and talent or skill**, plus accuracy on informational\u002FYMYL pages. 2025 updates made “little to no effort, originality, and added value” a **Lowest** path, including AI paraphrase and scaled content abuse. Generative AI **alone** does not determine the rating.\n\nThree evidence layers stay separate:\n\n1. **QRG effort** — documented rater criterion (primary source).\n2. **Leaked `contentEffort`** — reported LLM estimate for article pages. Mapping it to QRG is **inference**, not a Google statement.\n3. **People-first questions** — original information, first-hand expertise, not just rewriting sources.\n\nDo not brief “Content Effort is a confirmed ranking factor.” Operate as if visible effort is industrially scored. Label the leak as speculation.\n\nCross-links: library keep\u002Frefresh\u002Fprune → `seo-aeo-content-strategy`. Implementing effort adds on the page → `on-page-seo-aeo-optimization`.\n\n## Process\n\n1. **Classify page purpose.** Recipe, calculator, review, explainer, clip, documentary — the bar is relative to purpose, not word count.\n2. **Gather visible evidence only.** First-party facts, original artifacts, synthesis, craft, curation choices, added value vs substitutes. Ignore time logs, “human review” checkboxes, and origin stories that are not on the URL.\n3. **Score six signals, 0–2 each (0–12 total).** Use `references\u002Fvee-rubric.md`. Do not invent Google’s weights.\n4. **Map to QRG language.** 0–3 Lowest-risk evidence, 4–6 Low, 7–9 High, 10–12 Highest-evidence. Then apply the purpose modifier.\n5. **Recommend specific effort adds.** A zero on first-party information is a measurement or primary document, not “write more.” See the rubric’s add column.\n6. **Flag site-level scaled low-effort** only after sampling 3+ URLs that share a skeleton. One thin URL is a page problem.\n7. **Hand off implementation** to on-page SEO\u002FAEO for ski-ramp, titles, and schema. This skill stops at the effort verdict.\n\n## Output\n\nPer URL:\n\n| Field | Required |\n|---|---|\n| Purpose class | Yes |\n| Six-signal table (0–2) | Yes |\n| Total \u002F12 and band | Yes |\n| QRG-aligned risk (Lowest \u002F Low \u002F High evidence) | Yes |\n| Missing-evidence list | Yes |\n| Prioritized effort adds | Yes |\n| Leak column labeled inference | Yes |\n| Scaled low-effort flag | Optional, after 3+ URL sample |\n\nWorked pair: `references\u002Fworked-examples.md`.\n\n## Rules\n\n- Visible effort ≠ production effort.\n- Origin (human vs AI) ≠ rating.\n- Word count is not a signal.\n- Do not coin a fake “Google Content Effort score.”\n- Do not collapse this audit into E-E-A-T or keep\u002Frefresh\u002Fprune.\n",{"name":5,"description":7},"main","c137446804eb85b2cc0ba009c62068253a7c47be","skills\u002Fcontent-effort-audit",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":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill content-effort-audit",{"tool":63,"command":64},"bunx","bunx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill content-effort-audit",{"tool":66,"command":67},"pnpm","pnpm dlx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill content-effort-audit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontent-effort-audit\u002FSKILL.md",true,[71,77],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"references\u002Fvee-rubric.md","REFERENCE","参考文档",2447,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontent-effort-audit\u002Freferences\u002Fvee-rubric.md",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fworked-examples.md",1911,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fcontent-effort-audit\u002Freferences\u002Fworked-examples.md",[82,112,141,160,188,212],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":96,"publishedAt":44},"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",[88,93],{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000035","市场营销",[42,97,98,99,24,100,101,102,103,104,105,106,107,108,109,25,110,111],"technical","technical seo","technical seo ai crawler audit","crawler","crawl","diagnose","answer","issues","clean","indexer","google","prioritize","site","html","path",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":123,"publishedAt":44},"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",[118,119,122],{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},{"categoryNo":120,"categoryName":121,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师",{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},[124,125,126,127,42,128,129,130,131,132,133,134,135,136,137,138,139,140],"seo reporting","seo reporting measurement","reporting","measurement","sources","gsc","false","reports","data sources","data","layer","source","deterministic","trust","with","build","constraint",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":149,"publishedAt":44},"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",[147,148],{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},[150,42,151,152,38,153,154,155,41,156,157,158,159],"kt","seo max kt","max","long-form","methodology","form","bundle","full","read","long",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":171,"publishedAt":44},"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",[166,167,170],{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},{"categoryNo":168,"categoryName":169,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},[42,172,173,174,175,176,177,178,179,180,103,181,182,35,183,184,185,139,186,187],"seo keyword research","seo keyword research intent mapping","research","intent","keyword research","mapping","ai tools","standalone","down","search","prompt","maps","queries","generate","how","at",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":200,"publishedAt":44},"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",[194,197,198,199],{"categoryNo":195,"categoryName":196,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":89,"categoryName":90,"taxonomyType":91,"taxonomyTypeDesc":92},{"categoryNo":94,"categoryName":95,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[42,38,37,201,202,25,203,204,205,41,103,206,207,208,209,158,210,27,107,211],"seo aeo content strategy","content strategy","Citations","as","core","design","information","earn","structure","systems","prompts",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":221,"publishedAt":44},"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",[218],{"categoryNo":219,"categoryName":220,"taxonomyType":91,"taxonomyTypeDesc":92},"01JBSEEDCAT000000000000023","Skill与智能体开发",[222,182,223,224,42,41,172,181,225,226,227,228,174,139,186,210,176,229,230,157],"prompt picker","llm visibility","brand","up","journey","prompt library","across","library","tracking"]