[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718PNDT3A19SV7KVWC9083P":3,"skill-same:01M718PNDT3A19SV7KVWC9083P":88},{"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":34,"publishedAt":55,"linkOnly":56,"markdownContent":57,"markdownBody":58,"frontMatter":59,"refName":60,"commitSha":61,"skillDirectory":62,"forksCount":63,"repositoryHtmlUrl":64,"repositoryHomepageUrl":6,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M718PNDT3A19SV7KVWC9083P","seo-aeo-content-strategy",null,"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.","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\u002Fseo-aeo-content-strategy\u002FSKILL.md",[18,23,26,31],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",{"categoryNo":32,"categoryName":33,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"aeo","strategy","seo aeo content strategy","content strategy","content","seo","prompts","google","ramp","systems","read","structure","earn","information","design","answer","for","core","as","Citations","2026-11-10 10:12:30",false,"---\nname: seo-aeo-content-strategy\ndescription: \"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.\"\n---\n\n## Why this matters\n\n- **Topic clusters drive ~30% more organic traffic and hold rankings 2.5x longer** than standalone content (industry benchmarks, 2025).\n- **44.2% of all LLM citations come from the first 30% of text** (large-sample ChatGPT citation analysis, ~1.2M pages). The intro carries disproportionate weight — front-load your answer.\n- **78.4% of citations containing questions come from H2 headings** — LLMs treat H2s as prompts and the following paragraphs as answers.\n- **Heavily-cited text has ~20.6% entity density** (brand names, tool names, proper nouns) vs. 5-8% for normal English.\n- **53% of citations come from the middle of a paragraph**, not the first sentence. LLMs extract the sentence with the highest information gain, not the opener.\n- **Content not updated quarterly is ~3x more likely to lose AI citations** (multi-model freshness benchmarks).\n- **Brands are 6.5x more likely to be cited via third-party sources** than their own domains (vendor research, Oct 2025). Distribution matters as much as publication.\n- **Earned media distribution increases AI citations up to 325%** vs publishing only on your own site (syndication research, Dec 2025).\n\nAll citations above are data-backed. The ski ramp framework has p-value 0.0 (statistically indisputable) in large-sample citation studies.\n\n## Process\n\n1. **Define business goals and audience.** What must the content produce — leads, trials, awareness, authority? Who specifically is the reader, in which intent state?\n2. **Map the topic universe.** Core topics (3-7) → subtopics (5-15 per core) → supporting questions (unlimited). This becomes the cluster architecture.\n3. **Design cluster architecture.** Pillar pages (broad, 2500+ words, link hub) → cluster pages (specific subtopics, link back to pillar + peers) → supporting content (FAQ, glossary, tools, comparisons).\n4. **Audit existing content.** Score every page on the 1-5 matrix below: **keep**, **update**, **consolidate**, **prune**, or identify as a **gap** to create.\n5. **Create content briefs with AEO structure baked in.** Use the ski ramp framework by default. See `references\u002Fcontent-brief-template.md` for the complete brief template.\n6. **Build the editorial calendar.** Mix new creation with refresh cycles. Assume quarterly review of all priority content.\n7. **Define measurement criteria.** What does success look like per content type? Rankings, traffic, AI citations, leads, revenue?\n\n## Frameworks\n\n### Topic cluster design template\n\n```\nPillar page: \"The Complete Guide to [Core Topic]\"\n├── Cluster page 1: \"[Specific subtopic]\"\n├── Cluster page 2: \"[Specific subtopic]\"\n├── Cluster page 3: \"[Specific subtopic]\"\n│   ├── Supporting: FAQ\n│   ├── Supporting: Comparison \u002F vs\n│   └── Supporting: Tool\u002Fcalculator\n└── Cluster page N: ...\n```\n\n**Linking rules:**\n- Every cluster page links back to the pillar (upward)\n- Pillar links to all clusters (downward)\n- Clusters cross-link to 2-3 topically related clusters (sideways)\n- Supporting content links to its parent cluster\n- Anchor text is descriptive, not \"click here\"\n\n**Sizing:** pillar pages 2,500-5,000 words; cluster pages 1,200-2,500 words; supporting content as needed. These are guides, not targets — write what the topic actually requires.\n\n### Content audit scoring matrix\n\n| Score | Label | Action | Criteria |\n|-------|-------|--------|----------|\n| **1** | Strong | **Keep \u002F Monitor** | Ranking top 10, driving conversions, recently updated, AI-cited |\n| **2** | Needs refresh | **Update** | Ranking 11-30, traffic declining, outdated info, structurally sound |\n| **3** | Duplicative | **Consolidate** | Multiple thin pages on same topic, cannibalizing each other |\n| **4** | Underperforming | **Prune or rewrite** | Not ranking, no traffic, no backlinks, thin content |\n| **5** | Gap | **Create** | Topic covered by competitors but missing from our site |\n\n**Pruning is not deleting.** A pruned page may be merged into another (301 redirect), rewritten entirely, noindexed (for legal\u002Fhistorical retention), or deleted with a redirect to the most relevant parent page.\n\n### The \"Ski Ramp\" — AEO content structure\n\n**This is the single most important content framework in this skill.** Derived from large-sample ChatGPT citation analysis (p-value 0.0). Apply it to every piece of content meant to earn AI citations.\n\n**1. Front-load the answer (first 30% captures 44.2% of citations).**\n- Put the key claim, definitive answer, or headline finding in the first paragraph.\n- No throat-clearing intros. No \"In this article, we'll explore…\"\n- Write the way a journalist writes a news lede: most important fact first.\n\n**2. Use question-based H2 headings (78.4% of question-containing citations come from headings).**\n- LLMs treat H2s as prompts and the following paragraphs as answers.\n- Phrase H2s the way a user would ask the question: \"How long does SEO take to show results?\" not \"SEO Timeline Overview.\"\n- One topic per H2 section.\n\n**3. Follow each H2 with a direct, definitive answer.**\n- No hedging. \"Teams that publish twice a week see 2.3x more traffic\" beats \"Publishing frequency may help traffic.\"\n- The direct answer goes in the first 1-2 sentences after the heading. Supporting detail follows.\n- LLMs penalize hedging language in the extraction pass.\n\n**4. High entity density (~20%).**\n- Use specific brand names, tool names, numbers, dates, proper nouns.\n- \"Increase budget by 15% in Q3\" beats \"increase budget in the second half of the year.\"\n- Dates and numbers are universal citation positives — include real ones whenever possible.\n\n**5. The middle of paragraphs matters.**\n- 53% of citations come from mid-paragraph, not the opening sentence.\n- Put the information gain — the specific, concrete, citable fact — somewhere in the middle of a 3-5 sentence paragraph, after context and before the transition.\n\n**6. Include a summary\u002Fconclusion section (the \"wake-up\" zone).**\n- The last 10% of content before the footer sees a citation bump.\n- A short summary or \"Key takeaways\" block captures this.\n\n**What NOT to do:**\n- Don't artificially stuff entities or brand names — modern LLMs detect this and it damages trust signals.\n- Don't build content just to be \"summarizable.\" Large-sample work: making content summarizable alone doesn't make earning citations easier. Information gain and entity clarity do.\n- Don't use clickbait H2s that don't match the question a user would actually ask. The AI matches on semantic intent, not clever copy.\n\n### Content brief template (summary)\n\nCore fields every brief should include:\n\n| Field | Notes |\n|-------|-------|\n| Target keyword cluster | Primary + 5-15 supporting |\n| Search intent | Informational \u002F commercial \u002F transactional \u002F navigational |\n| Fan-out sub-queries | From seo-keyword-research skill |\n| Target word count | Based on top-10 SERP average + 20% |\n| H1 | Primary topic, natural phrasing, includes target KW |\n| H2 structure | **Question-based**, one topic each, sequenced by the user journey |\n| First-paragraph key claim | The specific answer\u002Ffinding to front-load |\n| Key entities | Named brands, tools, people, places, dates, numbers to include |\n| Internal links | To pillar (required) + 2-3 related clusters + supporting content |\n| External citations | Primary sources, data, expert quotes |\n| Schema type | Article, FAQPage, HowTo, Product, etc. |\n| Competitor content to beat | Top 3-5 currently ranking |\n| Information gain angle | What new data\u002Finsight do we add that nothing else has? |\n| Named author + credentials | Required for E-E-A-T |\n\nSee `references\u002Fcontent-brief-template.md` for the complete version with prompts and guidance for each field.\n\n### Content freshness cadence\n\n| Content type | Refresh interval | Triggers an ad-hoc refresh |\n|--------------|------------------|----------------------------|\n| Pillar pages | Quarterly | Core update, major competitor launch, outdated stat |\n| Cluster pages (priority) | Quarterly | Drop below top 10, traffic decay >20%, factual staleness |\n| Cluster pages (secondary) | Semi-annually | Significant SERP shift |\n| News\u002Ftrend posts | As needed | Original news posts do not get refreshed; they get superseded |\n| Evergreen reference | Annually | Only when facts change |\n| Product\u002Fpricing pages | On change | Whenever pricing, features, or positioning shifts |\n\n**Why:** Content not updated quarterly is ~3x more likely to lose AI citations in published multi-model benchmarks. Freshness is a confirmed signal across several open and closed LLMs. This is a real, data-backed signal — but remember, AI citation itself is volatile (45.5% of citations change between consecutive observations), so absolute consistency is impossible.\n\n### E-E-A-T checklist\n\nApplies to Google quality rater guidelines and overlaps with LLM trust signals.\n\n- [ ] Named author with real credentials (not \"Admin\" or \"Editorial Team\")\n- [ ] Author bio \u002F about page with professional history\n- [ ] First-party expertise, original research, or direct experience cited\n- [ ] Sources linked to primary data (not other blogs citing the same source)\n- [ ] Publication date visible on-page\n- [ ] Last-updated date visible when content is refreshed\n- [ ] Clear editorial process or methodology statement\n- [ ] Organization schema with founding date, logo, social profiles\n- [ ] Author schema (`Person` type) linking to credentials\n\n### Information gain strategy\n\n\"Information gain\" = what does this page add that no other page on the topic contains? Without it, you're competing on formatting and word count, which is a losing game against established sites.\n\n**High information gain sources:**\n- Original research (surveys, studies, experiments)\n- First-party data (usage metrics, customer anonymized data, proprietary analytics)\n- Expert commentary from named practitioners\n- Proprietary methodology or framework\n- Case studies with real numbers\n- Specific screenshots, walkthroughs, or demonstrations nobody else has\n\n**Critical amplification insight:** Brands are **6.5x more likely to be cited via third-party sources** than their own domains. **Earned media distribution boosts AI citations up to 325%** vs publishing only on-site. This means original research has *more value when distributed* — a proprietary study pitched to industry publications, syndication partners, and expert podcasts will earn more AI citations than the same study published only on your blog.\n\nPlan information gain as a *distributable asset*, not a blog post. Hand off to **seo-link-building** for the distribution plan.\n\n## Output format\n\n```\n## Content Strategy — [Client\u002FProject]\n\n**Business goals:** [List]\n**Target audience:** [Persona + intent state]\n**Time horizon:** [Quarter \u002F half-year \u002F year]\n\n### Topic universe\n| Core topic | Subtopics | Pillar status | Cluster status |\n|-----------|-----------|---------------|----------------|\n\n### Cluster architecture\n[Visual tree or table showing pillar → cluster → supporting for each core topic]\n\n### Content audit summary\n| Score | Count | Action | Est. effort |\n|-------|-------|--------|-------------|\n| 1 Keep | | | |\n| 2 Update | | | |\n| 3 Consolidate | | | |\n| 4 Prune\u002Frewrite | | | |\n| 5 Gap — create | | | |\n\n### Editorial calendar (next 90 days)\n| Week | Action | Content | Type | Brief owner | Writer |\n|------|--------|---------|------|-------------|--------|\n\n### Content briefs\n[Link or reference to detailed briefs for each piece — see references\u002Fcontent-brief-template.md]\n\n### Information gain \u002F distribution plan\n[What original assets will we create? How will they be distributed beyond our site?]\n\n### Measurement\n| KPI | Baseline | Target | Review cadence |\n|-----|----------|--------|----------------|\n```\n\n## Example — SaaS analytics startup\n\n**Goals:** 3x organic leads in 6 months; establish topical authority in product analytics.\n**Audience:** Product managers and growth leads at B2B SaaS companies, 10-200 employees.\n**Time horizon:** Next 90 days (with 6-month vision).\n\n**Topic universe (core topics):**\n1. Product analytics fundamentals\n2. Event tracking and instrumentation\n3. Funnel analysis\n4. Retention and cohort analysis\n5. A\u002FB testing and experimentation\n\n**Cluster architecture for \"Product analytics fundamentals\" (example):**\n- Pillar: `\u002Fproduct-analytics-guide` (3,500 words, the definitive primer)\n  - Cluster: `\u002Fproduct-analytics-vs-web-analytics`\n  - Cluster: `\u002Fproduct-analytics-tools-comparison`\n  - Cluster: `\u002Fhow-to-choose-product-analytics-tool`\n  - Cluster: `\u002Fproduct-analytics-metrics-that-matter`\n  - Supporting: `\u002Fproduct-analytics-glossary`\n\n**Audit (36 existing posts):**\n- 8 Strong — keep as-is\n- 14 Needs refresh (mostly 2023 posts with outdated stats)\n- 6 Consolidate into 2 pillar pages\n- 5 Prune (thin, no traffic, no backlinks)\n- 12 Gaps identified against 3 competitor sites\n\n**Ski ramp brief for `\u002Fproduct-analytics-guide` pillar:**\n- **First paragraph key claim:** \"Product analytics tools capture user interactions with your product to answer three questions: what users do, why they do it, and what to change next. Teams that instrument 15-30 core events see 2-3x better retention visibility than teams tracking everything.\"\n- **H2s (all question-based):**\n  - What is product analytics?\n  - How is product analytics different from web analytics?\n  - What events should I track?\n  - Which product analytics tool is right for my team?\n  - How do I measure retention with product analytics?\n  - What does a product analytics stack look like in 2026?\n- **Key entities:** named tools in the category, GA4, event, funnel, cohort, retention, activation (adjust to your vertical).\n- **Information gain angle:** Original benchmark survey of 240 product teams on instrumentation depth vs retention visibility.\n- **Distribution plan:** Syndicate the benchmark to Mind the Product, ProductLed, and offer exclusive cut to one Tier-1 publication for launch day coverage.\n\n**Editorial calendar:** 6 new pieces \u002F month + 4 refreshes \u002F month. Benchmark survey is the Q2 tentpole asset.\n\n## Guidelines\n\n- **Apply the ski ramp to every page meant to earn AI citations.** It is the highest-confidence AEO framework we have (large-sample citation analysis, p=0.0).\n- **Never hedge in the first paragraph.** \"This may help teams understand X\" ranks and cites worse than \"Teams that do X see Y.\" Definitive language wins in both humans and LLMs.\n- **Entity density is a knob, not a dial.** Target ~20% entity density naturally — don't stuff. If it reads awkwardly to a human, it reads awkwardly to the ranking systems.\n- **Pillar pages cannot be shallow.** 2,500+ words is typical because a true pillar answers the full topic. If you can't write 2,500 words of substance, it's not a pillar topic for you yet.\n- **Audit before you build.** Most content budgets are wasted on net-new pieces when existing content would outperform with a refresh. Score before you write.\n- **Information gain is leverage.** Original research is 10x the work of rehashing; if distributed well, it's 100x the return. Budget for both creation *and* distribution.\n- **Quarterly refresh is the default cadence**, not a best-case. Build it into the calendar from day one.\n- **E-E-A-T is not a checkbox.** \"Add an author photo\" without real credentials is theater. The underlying trust signal has to be real — named expert, actual experience, verifiable history.\n- **Do not believe the \"AI content penalty\" myth.** Large-sample citation studies (~1.9M pages) — 87.8% of AI-cited pages are at least AI-assisted. Quality and information gain matter; origin does not. Use AI tools where they help, review and edit for accuracy and voice.\n- **AI citations are volatile.** 45.5% of AI Overview citations change between consecutive observations. Don't chase day-to-day citation movements; measure quarterly patterns at minimum.\n- **Do not wait for a \"3-month freshness cliff.\"** The 3-month cliff is a myth in its absolute form. Freshness is a signal; quarterly updates reduce citation loss by ~3x. That's the real data.\n\n## Reference files\n\nWhen planning content briefs or writing to the standard, read:\n\n- `references\u002Fcontent-brief-template.md` — Complete content brief template with field-by-field guidance and ski-ramp examples. Read when creating or reviewing a content brief.\n\n## Cross-skill handoffs\n\n- **← seo-keyword-research:** Receive clustered keyword map and fan-out sub-queries as input.\n- **→ seo-onpage-optimization:** Hand off content briefs for page-level ski ramp implementation.\n- **→ seo-link-building:** Hand off information gain assets (original research, data studies) for distribution plans.\n- **→ aeo-ai-search-visibility:** Hand off content for citation readiness review.\n- **→ seo-reporting:** Hand off priority pages for tracking.\n","## Why this matters\n\n- **Topic clusters drive ~30% more organic traffic and hold rankings 2.5x longer** than standalone content (industry benchmarks, 2025).\n- **44.2% of all LLM citations come from the first 30% of text** (large-sample ChatGPT citation analysis, ~1.2M pages). The intro carries disproportionate weight — front-load your answer.\n- **78.4% of citations containing questions come from H2 headings** — LLMs treat H2s as prompts and the following paragraphs as answers.\n- **Heavily-cited text has ~20.6% entity density** (brand names, tool names, proper nouns) vs. 5-8% for normal English.\n- **53% of citations come from the middle of a paragraph**, not the first sentence. LLMs extract the sentence with the highest information gain, not the opener.\n- **Content not updated quarterly is ~3x more likely to lose AI citations** (multi-model freshness benchmarks).\n- **Brands are 6.5x more likely to be cited via third-party sources** than their own domains (vendor research, Oct 2025). Distribution matters as much as publication.\n- **Earned media distribution increases AI citations up to 325%** vs publishing only on your own site (syndication research, Dec 2025).\n\nAll citations above are data-backed. The ski ramp framework has p-value 0.0 (statistically indisputable) in large-sample citation studies.\n\n## Process\n\n1. **Define business goals and audience.** What must the content produce — leads, trials, awareness, authority? Who specifically is the reader, in which intent state?\n2. **Map the topic universe.** Core topics (3-7) → subtopics (5-15 per core) → supporting questions (unlimited). This becomes the cluster architecture.\n3. **Design cluster architecture.** Pillar pages (broad, 2500+ words, link hub) → cluster pages (specific subtopics, link back to pillar + peers) → supporting content (FAQ, glossary, tools, comparisons).\n4. **Audit existing content.** Score every page on the 1-5 matrix below: **keep**, **update**, **consolidate**, **prune**, or identify as a **gap** to create.\n5. **Create content briefs with AEO structure baked in.** Use the ski ramp framework by default. See `references\u002Fcontent-brief-template.md` for the complete brief template.\n6. **Build the editorial calendar.** Mix new creation with refresh cycles. Assume quarterly review of all priority content.\n7. **Define measurement criteria.** What does success look like per content type? Rankings, traffic, AI citations, leads, revenue?\n\n## Frameworks\n\n### Topic cluster design template\n\n```\nPillar page: \"The Complete Guide to [Core Topic]\"\n├── Cluster page 1: \"[Specific subtopic]\"\n├── Cluster page 2: \"[Specific subtopic]\"\n├── Cluster page 3: \"[Specific subtopic]\"\n│   ├── Supporting: FAQ\n│   ├── Supporting: Comparison \u002F vs\n│   └── Supporting: Tool\u002Fcalculator\n└── Cluster page N: ...\n```\n\n**Linking rules:**\n- Every cluster page links back to the pillar (upward)\n- Pillar links to all clusters (downward)\n- Clusters cross-link to 2-3 topically related clusters (sideways)\n- Supporting content links to its parent cluster\n- Anchor text is descriptive, not \"click here\"\n\n**Sizing:** pillar pages 2,500-5,000 words; cluster pages 1,200-2,500 words; supporting content as needed. These are guides, not targets — write what the topic actually requires.\n\n### Content audit scoring matrix\n\n| Score | Label | Action | Criteria |\n|-------|-------|--------|----------|\n| **1** | Strong | **Keep \u002F Monitor** | Ranking top 10, driving conversions, recently updated, AI-cited |\n| **2** | Needs refresh | **Update** | Ranking 11-30, traffic declining, outdated info, structurally sound |\n| **3** | Duplicative | **Consolidate** | Multiple thin pages on same topic, cannibalizing each other |\n| **4** | Underperforming | **Prune or rewrite** | Not ranking, no traffic, no backlinks, thin content |\n| **5** | Gap | **Create** | Topic covered by competitors but missing from our site |\n\n**Pruning is not deleting.** A pruned page may be merged into another (301 redirect), rewritten entirely, noindexed (for legal\u002Fhistorical retention), or deleted with a redirect to the most relevant parent page.\n\n### The \"Ski Ramp\" — AEO content structure\n\n**This is the single most important content framework in this skill.** Derived from large-sample ChatGPT citation analysis (p-value 0.0). Apply it to every piece of content meant to earn AI citations.\n\n**1. Front-load the answer (first 30% captures 44.2% of citations).**\n- Put the key claim, definitive answer, or headline finding in the first paragraph.\n- No throat-clearing intros. No \"In this article, we'll explore…\"\n- Write the way a journalist writes a news lede: most important fact first.\n\n**2. Use question-based H2 headings (78.4% of question-containing citations come from headings).**\n- LLMs treat H2s as prompts and the following paragraphs as answers.\n- Phrase H2s the way a user would ask the question: \"How long does SEO take to show results?\" not \"SEO Timeline Overview.\"\n- One topic per H2 section.\n\n**3. Follow each H2 with a direct, definitive answer.**\n- No hedging. \"Teams that publish twice a week see 2.3x more traffic\" beats \"Publishing frequency may help traffic.\"\n- The direct answer goes in the first 1-2 sentences after the heading. Supporting detail follows.\n- LLMs penalize hedging language in the extraction pass.\n\n**4. High entity density (~20%).**\n- Use specific brand names, tool names, numbers, dates, proper nouns.\n- \"Increase budget by 15% in Q3\" beats \"increase budget in the second half of the year.\"\n- Dates and numbers are universal citation positives — include real ones whenever possible.\n\n**5. The middle of paragraphs matters.**\n- 53% of citations come from mid-paragraph, not the opening sentence.\n- Put the information gain — the specific, concrete, citable fact — somewhere in the middle of a 3-5 sentence paragraph, after context and before the transition.\n\n**6. Include a summary\u002Fconclusion section (the \"wake-up\" zone).**\n- The last 10% of content before the footer sees a citation bump.\n- A short summary or \"Key takeaways\" block captures this.\n\n**What NOT to do:**\n- Don't artificially stuff entities or brand names — modern LLMs detect this and it damages trust signals.\n- Don't build content just to be \"summarizable.\" Large-sample work: making content summarizable alone doesn't make earning citations easier. Information gain and entity clarity do.\n- Don't use clickbait H2s that don't match the question a user would actually ask. The AI matches on semantic intent, not clever copy.\n\n### Content brief template (summary)\n\nCore fields every brief should include:\n\n| Field | Notes |\n|-------|-------|\n| Target keyword cluster | Primary + 5-15 supporting |\n| Search intent | Informational \u002F commercial \u002F transactional \u002F navigational |\n| Fan-out sub-queries | From seo-keyword-research skill |\n| Target word count | Based on top-10 SERP average + 20% |\n| H1 | Primary topic, natural phrasing, includes target KW |\n| H2 structure | **Question-based**, one topic each, sequenced by the user journey |\n| First-paragraph key claim | The specific answer\u002Ffinding to front-load |\n| Key entities | Named brands, tools, people, places, dates, numbers to include |\n| Internal links | To pillar (required) + 2-3 related clusters + supporting content |\n| External citations | Primary sources, data, expert quotes |\n| Schema type | Article, FAQPage, HowTo, Product, etc. |\n| Competitor content to beat | Top 3-5 currently ranking |\n| Information gain angle | What new data\u002Finsight do we add that nothing else has? |\n| Named author + credentials | Required for E-E-A-T |\n\nSee `references\u002Fcontent-brief-template.md` for the complete version with prompts and guidance for each field.\n\n### Content freshness cadence\n\n| Content type | Refresh interval | Triggers an ad-hoc refresh |\n|--------------|------------------|----------------------------|\n| Pillar pages | Quarterly | Core update, major competitor launch, outdated stat |\n| Cluster pages (priority) | Quarterly | Drop below top 10, traffic decay >20%, factual staleness |\n| Cluster pages (secondary) | Semi-annually | Significant SERP shift |\n| News\u002Ftrend posts | As needed | Original news posts do not get refreshed; they get superseded |\n| Evergreen reference | Annually | Only when facts change |\n| Product\u002Fpricing pages | On change | Whenever pricing, features, or positioning shifts |\n\n**Why:** Content not updated quarterly is ~3x more likely to lose AI citations in published multi-model benchmarks. Freshness is a confirmed signal across several open and closed LLMs. This is a real, data-backed signal — but remember, AI citation itself is volatile (45.5% of citations change between consecutive observations), so absolute consistency is impossible.\n\n### E-E-A-T checklist\n\nApplies to Google quality rater guidelines and overlaps with LLM trust signals.\n\n- [ ] Named author with real credentials (not \"Admin\" or \"Editorial Team\")\n- [ ] Author bio \u002F about page with professional history\n- [ ] First-party expertise, original research, or direct experience cited\n- [ ] Sources linked to primary data (not other blogs citing the same source)\n- [ ] Publication date visible on-page\n- [ ] Last-updated date visible when content is refreshed\n- [ ] Clear editorial process or methodology statement\n- [ ] Organization schema with founding date, logo, social profiles\n- [ ] Author schema (`Person` type) linking to credentials\n\n### Information gain strategy\n\n\"Information gain\" = what does this page add that no other page on the topic contains? Without it, you're competing on formatting and word count, which is a losing game against established sites.\n\n**High information gain sources:**\n- Original research (surveys, studies, experiments)\n- First-party data (usage metrics, customer anonymized data, proprietary analytics)\n- Expert commentary from named practitioners\n- Proprietary methodology or framework\n- Case studies with real numbers\n- Specific screenshots, walkthroughs, or demonstrations nobody else has\n\n**Critical amplification insight:** Brands are **6.5x more likely to be cited via third-party sources** than their own domains. **Earned media distribution boosts AI citations up to 325%** vs publishing only on-site. This means original research has *more value when distributed* — a proprietary study pitched to industry publications, syndication partners, and expert podcasts will earn more AI citations than the same study published only on your blog.\n\nPlan information gain as a *distributable asset*, not a blog post. Hand off to **seo-link-building** for the distribution plan.\n\n## Output format\n\n```\n## Content Strategy — [Client\u002FProject]\n\n**Business goals:** [List]\n**Target audience:** [Persona + intent state]\n**Time horizon:** [Quarter \u002F half-year \u002F year]\n\n### Topic universe\n| Core topic | Subtopics | Pillar status | Cluster status |\n|-----------|-----------|---------------|----------------|\n\n### Cluster architecture\n[Visual tree or table showing pillar → cluster → supporting for each core topic]\n\n### Content audit summary\n| Score | Count | Action | Est. effort |\n|-------|-------|--------|-------------|\n| 1 Keep | | | |\n| 2 Update | | | |\n| 3 Consolidate | | | |\n| 4 Prune\u002Frewrite | | | |\n| 5 Gap — create | | | |\n\n### Editorial calendar (next 90 days)\n| Week | Action | Content | Type | Brief owner | Writer |\n|------|--------|---------|------|-------------|--------|\n\n### Content briefs\n[Link or reference to detailed briefs for each piece — see references\u002Fcontent-brief-template.md]\n\n### Information gain \u002F distribution plan\n[What original assets will we create? How will they be distributed beyond our site?]\n\n### Measurement\n| KPI | Baseline | Target | Review cadence |\n|-----|----------|--------|----------------|\n```\n\n## Example — SaaS analytics startup\n\n**Goals:** 3x organic leads in 6 months; establish topical authority in product analytics.\n**Audience:** Product managers and growth leads at B2B SaaS companies, 10-200 employees.\n**Time horizon:** Next 90 days (with 6-month vision).\n\n**Topic universe (core topics):**\n1. Product analytics fundamentals\n2. Event tracking and instrumentation\n3. Funnel analysis\n4. Retention and cohort analysis\n5. A\u002FB testing and experimentation\n\n**Cluster architecture for \"Product analytics fundamentals\" (example):**\n- Pillar: `\u002Fproduct-analytics-guide` (3,500 words, the definitive primer)\n  - Cluster: `\u002Fproduct-analytics-vs-web-analytics`\n  - Cluster: `\u002Fproduct-analytics-tools-comparison`\n  - Cluster: `\u002Fhow-to-choose-product-analytics-tool`\n  - Cluster: `\u002Fproduct-analytics-metrics-that-matter`\n  - Supporting: `\u002Fproduct-analytics-glossary`\n\n**Audit (36 existing posts):**\n- 8 Strong — keep as-is\n- 14 Needs refresh (mostly 2023 posts with outdated stats)\n- 6 Consolidate into 2 pillar pages\n- 5 Prune (thin, no traffic, no backlinks)\n- 12 Gaps identified against 3 competitor sites\n\n**Ski ramp brief for `\u002Fproduct-analytics-guide` pillar:**\n- **First paragraph key claim:** \"Product analytics tools capture user interactions with your product to answer three questions: what users do, why they do it, and what to change next. Teams that instrument 15-30 core events see 2-3x better retention visibility than teams tracking everything.\"\n- **H2s (all question-based):**\n  - What is product analytics?\n  - How is product analytics different from web analytics?\n  - What events should I track?\n  - Which product analytics tool is right for my team?\n  - How do I measure retention with product analytics?\n  - What does a product analytics stack look like in 2026?\n- **Key entities:** named tools in the category, GA4, event, funnel, cohort, retention, activation (adjust to your vertical).\n- **Information gain angle:** Original benchmark survey of 240 product teams on instrumentation depth vs retention visibility.\n- **Distribution plan:** Syndicate the benchmark to Mind the Product, ProductLed, and offer exclusive cut to one Tier-1 publication for launch day coverage.\n\n**Editorial calendar:** 6 new pieces \u002F month + 4 refreshes \u002F month. Benchmark survey is the Q2 tentpole asset.\n\n## Guidelines\n\n- **Apply the ski ramp to every page meant to earn AI citations.** It is the highest-confidence AEO framework we have (large-sample citation analysis, p=0.0).\n- **Never hedge in the first paragraph.** \"This may help teams understand X\" ranks and cites worse than \"Teams that do X see Y.\" Definitive language wins in both humans and LLMs.\n- **Entity density is a knob, not a dial.** Target ~20% entity density naturally — don't stuff. If it reads awkwardly to a human, it reads awkwardly to the ranking systems.\n- **Pillar pages cannot be shallow.** 2,500+ words is typical because a true pillar answers the full topic. If you can't write 2,500 words of substance, it's not a pillar topic for you yet.\n- **Audit before you build.** Most content budgets are wasted on net-new pieces when existing content would outperform with a refresh. Score before you write.\n- **Information gain is leverage.** Original research is 10x the work of rehashing; if distributed well, it's 100x the return. Budget for both creation *and* distribution.\n- **Quarterly refresh is the default cadence**, not a best-case. Build it into the calendar from day one.\n- **E-E-A-T is not a checkbox.** \"Add an author photo\" without real credentials is theater. The underlying trust signal has to be real — named expert, actual experience, verifiable history.\n- **Do not believe the \"AI content penalty\" myth.** Large-sample citation studies (~1.9M pages) — 87.8% of AI-cited pages are at least AI-assisted. Quality and information gain matter; origin does not. Use AI tools where they help, review and edit for accuracy and voice.\n- **AI citations are volatile.** 45.5% of AI Overview citations change between consecutive observations. Don't chase day-to-day citation movements; measure quarterly patterns at minimum.\n- **Do not wait for a \"3-month freshness cliff.\"** The 3-month cliff is a myth in its absolute form. Freshness is a signal; quarterly updates reduce citation loss by ~3x. That's the real data.\n\n## Reference files\n\nWhen planning content briefs or writing to the standard, read:\n\n- `references\u002Fcontent-brief-template.md` — Complete content brief template with field-by-field guidance and ski-ramp examples. Read when creating or reviewing a content brief.\n\n## Cross-skill handoffs\n\n- **← seo-keyword-research:** Receive clustered keyword map and fan-out sub-queries as input.\n- **→ seo-onpage-optimization:** Hand off content briefs for page-level ski ramp implementation.\n- **→ seo-link-building:** Hand off information gain assets (original research, data studies) for distribution plans.\n- **→ aeo-ai-search-visibility:** Hand off content for citation readiness review.\n- **→ seo-reporting:** Hand off priority pages for tracking.\n",{"name":5,"description":7},"main","c137446804eb85b2cc0ba009c62068253a7c47be","skills\u002Fseo-aeo-content-strategy",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":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill seo-aeo-content-strategy",{"tool":74,"command":75},"bunx","bunx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill seo-aeo-content-strategy",{"tool":77,"command":78},"pnpm","pnpm dlx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill seo-aeo-content-strategy","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-aeo-content-strategy\u002FSKILL.md",true,[82],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fcontent-brief-template.md","REFERENCE","参考文档",11751,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-aeo-content-strategy\u002Freferences\u002Fcontent-brief-template.md",[89,112,141,159,188,207],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":97,"publishedAt":55},"01M718Q16XB7D7ZMGNNA1226G5","technical-seo-ai-crawler-audit","Diagnose and prioritize the technical issues blocking a site from being crawled, indexed, ranked, and cited. 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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",[95,96],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},[40,98,99,100,101,102,103,104,50,105,106,107,42,108,109,39,110,111],"technical","technical seo","technical seo ai crawler audit","audit","crawler","crawl","diagnose","issues","clean","indexer","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":55},"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":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":120,"categoryName":121,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000033","数据分析师",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},[124,125,126,127,40,128,129,130,131,132,133,134,135,136,137,138,139,140],"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":55},"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":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},[150,40,151,152,35,153,154,155,51,156,157,45,158],"kt","seo max kt","max","long-form","methodology","form","bundle","full","long",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":170,"publishedAt":55},"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",[165,166,169],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":167,"categoryName":168,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},[40,171,172,173,174,175,176,177,178,179,50,180,181,182,183,184,185,139,186,187],"seo keyword research","seo keyword research intent mapping","research","intent","keyword research","mapping","ai tools","standalone","down","search","prompt","when","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":197,"publishedAt":55},"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",[194],{"categoryNo":195,"categoryName":196,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[198,181,199,200,40,51,171,180,201,202,203,204,173,139,186,44,175,205,206,157],"prompt picker","llm visibility","brand","up","journey","prompt library","across","library","tracking",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":213,"publishedAt":55},"01M718PFCRAXVY3V3B8CFER9H7","paid-search-ads-playbook","Google and Microsoft paid search planning: structure, keywords, match types, negatives, RSAs, bidding lens, and landing-page alignment for conversion-focused spend.","https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fpaid-search-ads-playbook\u002FSKILL.md",[],[214,215,180,216,217,218,219,220,221,51,222,223,224,46,225,42,226,227],"paid search ads playbook","paid","ads","playbook","alignment","lens","types","planning","page","landing","conversion","microsoft","match","landing-page"]