[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M718PY2PKA7BYS5KNJ2A9DRT":3,"skill-same:01M718PY2PKA7BYS5KNJ2A9DRT":87},{"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":31,"publishedAt":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M718PY2PKA7BYS5KNJ2A9DRT","seo-reporting-measurement",null,"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.","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-reporting-measurement\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000035","市场营销",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"seo reporting measurement","reporting","measurement","seo","seo reporting","constraint","build","with","trust","deterministic","source","layer","data","data sources","reports","false","gsc","sources","2026-11-10 10:12:30",false,"---\nname: seo-reporting-measurement\ndescription: \"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.\"\n---\n\n## Why this matters\n\n- **GSC data is ~75% incomplete.** Google filters approximately 75% of search impressions for \"privacy\" (industry analysis, Feb 2026). Single-source GSC decisions are unreliable.\n- **Multi-touch attribution reveals 30-60% more organic value than last-click models** — which is why most SEO teams under-report their actual business contribution.\n- **GSC branded query filter** (released November 2025) finally enables native branded vs non-branded segmentation — one of the biggest reporting improvements of the last five years.\n- **Median publisher: 10% YoY decline in organic traffic H1 2025** despite maintained visibility — this is the zero-click effect showing up in the numbers. Report on impressions and rankings, not just clicks.\n- **AI visibility is probabilistic.** \u003C1% chance of same brand list from same prompt; \u003C0.1% chance of same order (multi-run replication studies). All AI metrics must be reported with sample sizes and confidence notes.\n- **Zero-click reality:** 58.5% of US searches and 77.2% of mobile searches end without a click (third-party search studies, 2025). Click-based KPIs alone understate visibility.\n\n## Process\n\n1. **Data foundation audit.** Is GSC connected? Is GA4 set up correctly? Are conversions tracked? Without this, nothing else matters.\n2. **Define the KPI hierarchy.** Leading → core → business → AI visibility. Each layer has its own cadence and audience.\n3. **Build attribution.** Default to multi-touch when possible. Document the model and its known limitations.\n4. **Structure the reports.** Weekly tactical, monthly strategic, quarterly deep-dive. Each has a specific audience and purpose.\n5. **Handle algorithm updates.** When Google updates or an AI model shifts, reports need to flag and contextualize — not just show numbers going up or down.\n6. **Report on AI visibility with appropriate caveats.** Sample sizes, platform breakdowns, quarterly trends.\n7. **Continuously refine.** Retire metrics that don't drive decisions; add metrics that do.\n\n## Frameworks\n\n### Critical data caveat to lead every report with\n\n> **⚠️ GSC impression data is approximately 75% incomplete.** Google filters most search impressions for privacy. Single-source GSC decisions are unreliable. All figures below should be treated as *directional trends*, not absolute numbers. Cross-reference with third-party tools and focus on *deltas over time*, not point values.\n\nThis is not boilerplate — stakeholders consistently misinterpret SEO reports when they don't understand the data limits. Lead every report with this caveat.\n\n### KPI hierarchy\n\nOrganized from **leading** (early signals) to **lagging** (business outcomes). Each tier has different review cadence and different audiences.\n\n**Leading indicators (weekly review):**\n- Impressions (GSC)\n- CTR by position band\n- Scroll depth by landing page type\n- Time on page by intent segment\n- Indexed pages count (GSC Coverage)\n- Core Web Vitals field scores (GSC)\n- Crawl stats (GSC Crawl Stats report)\n- New issues detected in GSC\n\n**Core SEO metrics (monthly review):**\n- **Organic sessions segmented by intent** (not aggregate) — informational\u002Fcommercial\u002Ftransactional\n- **Branded vs non-branded traffic** — use GSC branded query filter (November 2025+)\n- **Ranking distribution** — top 3, top 10, top 20 keyword counts\n- **New vs lost keywords** — velocity of keyword portfolio\n- **SERP feature presence** — featured snippets, PAA, AI Overviews\n- **Internal link coverage** on priority pages\n- **Content freshness** — how many priority pages updated in last quarter\n\n**Business metrics (monthly review, executive audience):**\n- **Organic-attributed revenue** (multi-touch model preferred)\n- **Lead generation from organic**\n- **Conversion rate by landing page type**\n- **Customer acquisition cost from organic**\n- **Lifetime value from organic customers** (if tracked)\n\n**AI visibility metrics (quarterly review minimum):**\n- **Brand mention frequency** across platforms (60-100+ runs per prompt)\n- **AI referral traffic** (source\u002Fmedium reports in GA4)\n- **Citation rate** — how often your domain is cited across AI queries\n- **Competitor share-of-voice in AI**\n- **Per-platform visibility** — never collapse into a single score\n\n### Metrics to retire\n\nPer industry reporting, these metrics should NOT be used as primary KPIs:\n\n| Metric | Why retire it | What to use instead |\n|--------|--------------|---------------------|\n| **Organic traffic as standalone KPI** | Lacks intent context; treats all traffic as equal | Organic sessions segmented by intent |\n| **Average keyword position** | Meaningless in aggregate (averaging ranks across different queries is statistically incoherent) | Ranking distribution buckets (top 3, top 10, top 20) |\n| **Domain authority as a business metric** | A third-party proxy score, not a Google metric | Referring domain count and quality, actual ranking performance |\n| **Bounce rate in isolation** | GA4 redefined it; often misinterpreted | Engagement rate + task completion signals |\n| **Single-run AI visibility \"score\"** | Non-deterministic; meaningless without sample size | Visibility % across 60+ runs per prompt per platform |\n| **Individual meta description CTR** | Google rewrites ~60% of meta descriptions; you're measuring Google's rewrite, not yours | Page-level CTR trends with content changes logged |\n\n### Revenue attribution formula\n\n```\nMonthly Organic Value =\n  (Organic Sessions × Conversion Rate × Average Order Value)\n  +\n  (Lead Gen Conversions × Lead Value × Close Rate)\n```\n\n**Important:** This is a last-click formula. Multi-touch attribution typically reveals 30-60% more organic value than last-click. If you can, use GA4's data-driven attribution model or build a custom model that credits organic for its role in multi-touch journeys.\n\n**Document your model.** Every report should state which attribution model is used and its known limitations. Stakeholders will ask why \"SEO revenue\" differs from \"paid social revenue\" — the model is the answer.\n\n### AI visibility measurement protocol (reporting layer)\n\nWhen reporting AI visibility to stakeholders, **every metric must include sample size and the measurement protocol**. See `references\u002Fai-metrics-reporting.md` for the detailed reporting patterns.\n\nCore rules:\n\n- **Sample size minimum:** 60 runs per prompt per platform. Anything less is noise.\n- **Never report \"rank position\" in AI output.** \u003C0.1% consistency — it's not a stable metric.\n- **Report per-platform, not aggregated.** 86% of top-cited sources are platform-unique; aggregating hides the pattern.\n- **Quarterly cadence** is the default; monthly if you're actively optimizing.\n- **Flag the confidence** of every AI metric: Data-backed, Directional, or Experimental.\n\n### Algorithm update assessment framework\n\nWhen Google rolls out a core update or an AI platform shifts behavior:\n\n1. **Pause before reporting.** Google core updates take 2-4 weeks to fully roll out. Initial ranking shifts are not final.\n2. **Segment the impact.** Don't report \"traffic is down 12%.\" Break it down: which pages? which intent buckets? which query types?\n3. **Cross-reference with known changes.** Was there a recent migration? A new competitor? A CWV regression? Update impact gets blamed for unrelated issues constantly.\n4. **Compare to industry.** If traffic dropped 12% and the industry dropped 15%, you're relatively stable. If the industry is flat and you dropped 12%, it's specific to your site.\n5. **Flag what you don't know.** \"Some of this may be algorithm-driven and some may be zero-click expansion from AI Overviews — it will take 30-60 days to distinguish.\"\n6. **Recommend observation windows, not reactive changes.** The worst thing post-update is to start rewriting content before the update finishes rolling out.\n\nSee `references\u002Falgorithm-update-playbook.md` for the full diagnostic playbook.\n\n### Report structure template\n\nAdapt to the audience. Executives want the one-pager; SEO teams want the detail.\n\n```\n## SEO Performance Report — [Period]\n\n### Executive Summary\n[2-3 sentences: what happened this period, what it means for the business, what to do next. No jargon. No fluff.]\n\n### Business Impact\n| Metric | This Period | Prior Period | YoY | Target |\n|--------|-------------|--------------|-----|--------|\n| Organic Revenue | | | | |\n| Organic Leads | | | | |\n| Organic Sessions (non-branded) | | | | |\n| Organic CAC | | | | |\n\n⚠️ Attribution model: [last-click \u002F multi-touch \u002F data-driven]. GSC data ~75% filtered — trends more reliable than absolutes.\n\n### Search Visibility\n- Ranking distribution (top 3 \u002F top 10 \u002F top 20)\n- Branded vs non-branded impressions and clicks\n- SERP feature presence (featured snippets, PAA, AI Overviews)\n- New vs lost keywords this period\n\n### AI Search Visibility (if tracking)\n| Platform | Visibility % | Δ vs last | Sample size |\n|----------|-------------|-----------|-------------|\n\n⚠️ AI metrics are probabilistic. Replication studies: \u003C1% chance of same brand list from same prompt. All figures represent statistical patterns across [X] runs per prompt per platform. Focus on quarterly trends.\n\n### Content Performance\n- Top performing pages (by revenue\u002Fconversions, not just traffic)\n- Decaying content (pages losing rankings or traffic)\n- Content refresh priorities (decayed priority pages)\n- New content performance (published this period)\n\n### Technical Health\n- Core Web Vitals (LCP, INP, CLS — field data)\n- Indexation status (indexed vs discovered vs excluded)\n- Critical issues (GSC errors, coverage issues, manual actions)\n- AI crawler access status\n\n### Competitive Landscape\n- Key ranking movements in the competitive set\n- Opportunities (pages where we gained position)\n- Threats (pages where competitors gained)\n\n### Recommended Actions (Prioritized)\n| Priority | Action | Expected Impact | Effort | Timeline |\n|----------|--------|----------------|--------|----------|\n\n### Appendix: Methodology Notes\n- Data sources: GSC, GA4, rank-tracking \u002F backlink tool, [other]\n- Attribution model in use\n- AI measurement protocol and sample sizes\n- Known limitations: GSC filtering, AI non-determinism, attribution caveats\n- Period definitions: [date range, comparison period]\n```\n\n### Reporting cadence\n\n| Cadence | Audience | Focus | Output |\n|---------|----------|-------|--------|\n| **Weekly (tactical)** | SEO team, content team | Rankings changes, traffic anomalies, technical issues, newly indexed pages | Dashboard + 5-bullet weekly note |\n| **Monthly (strategic)** | Marketing leadership, department heads | Full performance across KPI hierarchy, content wins\u002Flosses, recommendations | Structured report (template above) |\n| **Quarterly (deep dive)** | Executive team, board if applicable | Trend analysis, strategy review, competitive repositioning, AI visibility assessment, ROI analysis | Comprehensive presentation |\n| **Ad-hoc (incident)** | Relevant stakeholders | Algorithm updates, major traffic shifts, migrations, crises | Incident report with diagnosis + plan |\n\n### Dashboard layer\n\nFor ongoing visibility, build a dashboard (Looker Studio, GA4 native, or a BI tool) with:\n\n- Rolling 12-month trend for priority KPIs\n- Branded vs non-branded segmentation\n- Top pages by revenue (not just traffic)\n- Top queries by click volume and by impression volume (two different lists)\n- CWV field scores\n- Referring domain velocity\n- AI visibility panel with sample sizes surfaced\n\nUpdate weekly; review monthly in the structured report.\n\n## Output format\n\nSee the report structure template above. For each report, customize the sections based on what the audience can act on. Executive reports drop the technical detail; SEO team reports drop the business framing.\n\n## Example — monthly SEO report for a mid-market SaaS\n\n**Period:** March 2026\n**Audience:** VP of Marketing + Head of Growth\n\n---\n\n### Executive summary\n\nOrganic revenue grew 18% month-over-month, driven by 12 new product analytics cluster pages published in Q1 now ranking on page 1. AI visibility on ChatGPT rose from 12% to 19% after rewriting the pricing page to server-side render. Core Web Vitals regressed on mobile (LCP 3.8s vs 2.4s target) after a blog redesign — fix planned for sprint 14.\n\n### Business impact\n\n| Metric | Mar 2026 | Feb 2026 | YoY | Target |\n|--------|----------|----------|-----|--------|\n| Organic revenue (multi-touch) | $187K | $158K | +34% | $200K |\n| Organic demo requests | 142 | 119 | +41% | 150 |\n| Organic sessions (non-branded) | 89K | 82K | +22% | 100K |\n\n⚠️ Multi-touch data-driven attribution via GA4. GSC impression data ~75% filtered — trend reliable; absolute impression count understated.\n\n### Search visibility\n\n- **Ranking distribution:** 34 keywords in top 3 (+6), 127 in top 10 (+14), 310 in top 20 (+23)\n- **Branded traffic:** 38% of organic sessions (stable)\n- **Non-branded traffic:** 62% (up from 58% last month — the 12 new cluster pages are the primary driver)\n- **AI Overviews present** on 28 of 50 priority keywords — we are cited in 7 of them\n- **New keywords (first rank):** 47 (strong)\n- **Lost keywords:** 12 (normal churn)\n\n### AI search visibility\n\n| Platform | Visibility % | Δ vs Feb | Sample size |\n|----------|-------------|----------|-------------|\n| ChatGPT | 19% | +7% | 15 prompts × 60 runs |\n| Perplexity | 24% | +2% | 15 prompts × 60 runs |\n| AI Overviews | 14% | +6% | 15 prompts × 60 runs |\n| AI Mode | 8% | +1% | 15 prompts × 60 runs |\n\n⚠️ AI metrics are probabilistic. Replication studies: \u003C1% chance of same brand list on same prompt. Figures represent patterns across 60 runs per prompt per platform. ChatGPT gain corresponds to pricing page SSR launch on March 11.\n\n### Content performance\n\n- **Top performers:** `\u002Fproduct-analytics-guide` (pillar, 8.2K sessions, 22 demos), `\u002Famplitude-vs-mixpanel-vs-posthog` (4.8K sessions, 31 demos — highest converting), `\u002Fevent-scoping-framework` (3.1K sessions, 18 demos)\n- **Decaying:** 3 old category pages losing rankings steadily — queued for Q2 refresh\n- **Refresh priorities:** `\u002Fwhat-is-product-analytics` (ranked #8, falling from #3 over 3 months)\n\n### Technical health\n\n- **CWV (mobile, field data):** LCP 3.8s ⚠️ (target ≤2.5s), INP 180ms ✅, CLS 0.08 ✅\n- **LCP regression** traced to blog redesign — new hero image treatment is 2.1MB unoptimized. Fix in sprint 14.\n- **Indexation:** 924 indexed (+12), 18 new Coverage errors (all thin tag archives, queued for noindex)\n- **AI crawler access:** GPTBot, PerplexityBot, ClaudeBot all hitting site normally (~40\u002Fday, ~25\u002Fday, ~10\u002Fday respectively)\n\n### Recommended actions\n\n| Priority | Action | Impact | Effort | Timeline |\n|----------|--------|--------|--------|----------|\n| Critical | Fix LCP regression (compress blog hero images, preload) | High — CWV is page experience signal | Low | Sprint 14 |\n| High | Refresh `\u002Fwhat-is-product-analytics` with updated stats, ski ramp structure | High — recovery of top-3 position | Medium | April W2 |\n| High | Publish Q2 benchmark data study | High — PR asset + linkable content | High | End of April |\n| Medium | Server-render remaining 4 JS-heavy landing pages | Medium — unlocks AI visibility on those pages | Medium | Sprint 15 |\n| Medium | Add FAQPage schema to top 10 commercial pages | Medium — rich result eligibility | Low | April W1 |\n\n### Appendix\n\n- Data sources: GSC, GA4 (DDA attribution), rank-tracking tool, AI visibility tracker\n- Attribution: GA4 data-driven; may differ from GA4 last-click by ~40% for organic\n- AI measurement: 15 prompts × 60 runs across ChatGPT, Perplexity, AI Overviews, AI Mode. Run on 3\u002F28-3\u002F30. Re-measured after pricing page SSR launch.\n- Known limitations: GSC ~75% impression filtering, AI probabilistic, CWV field data 28-day lag\n- Period: March 1-31, 2026. Compared to February 1-28, 2026 and March 1-31, 2025.\n\n---\n\n## Guidelines\n\n- **Lead every report with the GSC caveat.** Stakeholders consistently misinterpret SEO data because they don't know it's ~75% filtered. State it every time.\n- **Always segment organic traffic by intent.** Aggregate organic traffic is near-useless as a KPI — it conflates transactional wins with informational zero-click impressions.\n- **Use the GSC branded query filter (November 2025+)** to separate branded and non-branded. This is one of the biggest analytics improvements in years and most teams still don't use it.\n- **Retire average keyword position.** Averaging positions across different queries is statistically incoherent. Use ranking distribution (top 3, top 10, top 20) instead.\n- **Report AI visibility with sample sizes every time.** \"Visibility 18% across 60 runs\" not \"Visibility 18%.\" The second form invites false precision.\n- **Never collapse AI visibility into a single score.** 86% of top-cited sources are platform-unique. A \"unified AI score\" hides the actionable signal.\n- **Multi-touch attribution reveals the real organic contribution.** Last-click typically under-reports organic by 30-60%. If your stakeholders think SEO is underperforming, the attribution model is often the cause.\n- **Zero-click is the new normal.** 58.5% US, 77.2% mobile searches end without a click (third-party search studies). Don't report declining clicks as declining performance if impressions and rankings are stable.\n- **Algorithm update impact takes 2-4 weeks to stabilize.** Don't recommend reactive changes within the rollout window — wait for the dust to settle.\n- **Content decay is a leading indicator of traffic loss.** A page dropping from #3 to #8 over 90 days predicts a traffic drop; catch it early through content performance tracking.\n- **Track referring domains and brand mentions together** — AI visibility depends on both. Mentions without links still feed AI signal.\n- **Build for decisions, not dashboards.** Every metric in every report should answer the question \"what would we do differently if this number changed?\" If nothing, remove the metric.\n- **The report is for the reader.** Executives want the one-page summary with the one recommendation. SEO specialists want the detail. Build two versions if needed.\n- **Flag confidence levels on every recommendation.** \"Data-backed,\" \"directional,\" or \"experimental.\" This builds trust with stakeholders who will come back to hold you accountable.\n- **Reports are a trust instrument.** Over-claiming on good months and hiding bad months destroys the discipline. Show both, contextualize both, and let the trend do the talking.\n\n## Reference files\n\nRead these when the task warrants:\n\n- **`references\u002Fai-metrics-reporting.md`** — Detailed guidance on reporting AI visibility metrics responsibly, including sample-size tables, caveat language, and common stakeholder pitfalls. Read when building an AI visibility section of a report.\n- **`references\u002Falgorithm-update-playbook.md`** — Diagnostic playbook for responding to Google core updates, AI platform shifts, and traffic anomalies. Read when you suspect an algorithm-driven impact.\n\n## Cross-skill handoffs\n\n- **← all other SEO skills** — Receive baselines, KPIs, and priority actions for tracking.\n- **→ seo-keyword-research** — Flag keywords with declining performance for re-evaluation.\n- **→ seo-content-strategy** — Flag content with decay for refresh prioritization.\n- **→ seo-technical-audit** — Escalate CWV regressions, indexation issues, crawl anomalies.\n- **→ aeo-ai-search-visibility** — Feed visibility baseline data for strategy iteration.\n","## Why this matters\n\n- **GSC data is ~75% incomplete.** Google filters approximately 75% of search impressions for \"privacy\" (industry analysis, Feb 2026). Single-source GSC decisions are unreliable.\n- **Multi-touch attribution reveals 30-60% more organic value than last-click models** — which is why most SEO teams under-report their actual business contribution.\n- **GSC branded query filter** (released November 2025) finally enables native branded vs non-branded segmentation — one of the biggest reporting improvements of the last five years.\n- **Median publisher: 10% YoY decline in organic traffic H1 2025** despite maintained visibility — this is the zero-click effect showing up in the numbers. Report on impressions and rankings, not just clicks.\n- **AI visibility is probabilistic.** \u003C1% chance of same brand list from same prompt; \u003C0.1% chance of same order (multi-run replication studies). All AI metrics must be reported with sample sizes and confidence notes.\n- **Zero-click reality:** 58.5% of US searches and 77.2% of mobile searches end without a click (third-party search studies, 2025). Click-based KPIs alone understate visibility.\n\n## Process\n\n1. **Data foundation audit.** Is GSC connected? Is GA4 set up correctly? Are conversions tracked? Without this, nothing else matters.\n2. **Define the KPI hierarchy.** Leading → core → business → AI visibility. Each layer has its own cadence and audience.\n3. **Build attribution.** Default to multi-touch when possible. Document the model and its known limitations.\n4. **Structure the reports.** Weekly tactical, monthly strategic, quarterly deep-dive. Each has a specific audience and purpose.\n5. **Handle algorithm updates.** When Google updates or an AI model shifts, reports need to flag and contextualize — not just show numbers going up or down.\n6. **Report on AI visibility with appropriate caveats.** Sample sizes, platform breakdowns, quarterly trends.\n7. **Continuously refine.** Retire metrics that don't drive decisions; add metrics that do.\n\n## Frameworks\n\n### Critical data caveat to lead every report with\n\n> **⚠️ GSC impression data is approximately 75% incomplete.** Google filters most search impressions for privacy. Single-source GSC decisions are unreliable. All figures below should be treated as *directional trends*, not absolute numbers. Cross-reference with third-party tools and focus on *deltas over time*, not point values.\n\nThis is not boilerplate — stakeholders consistently misinterpret SEO reports when they don't understand the data limits. Lead every report with this caveat.\n\n### KPI hierarchy\n\nOrganized from **leading** (early signals) to **lagging** (business outcomes). Each tier has different review cadence and different audiences.\n\n**Leading indicators (weekly review):**\n- Impressions (GSC)\n- CTR by position band\n- Scroll depth by landing page type\n- Time on page by intent segment\n- Indexed pages count (GSC Coverage)\n- Core Web Vitals field scores (GSC)\n- Crawl stats (GSC Crawl Stats report)\n- New issues detected in GSC\n\n**Core SEO metrics (monthly review):**\n- **Organic sessions segmented by intent** (not aggregate) — informational\u002Fcommercial\u002Ftransactional\n- **Branded vs non-branded traffic** — use GSC branded query filter (November 2025+)\n- **Ranking distribution** — top 3, top 10, top 20 keyword counts\n- **New vs lost keywords** — velocity of keyword portfolio\n- **SERP feature presence** — featured snippets, PAA, AI Overviews\n- **Internal link coverage** on priority pages\n- **Content freshness** — how many priority pages updated in last quarter\n\n**Business metrics (monthly review, executive audience):**\n- **Organic-attributed revenue** (multi-touch model preferred)\n- **Lead generation from organic**\n- **Conversion rate by landing page type**\n- **Customer acquisition cost from organic**\n- **Lifetime value from organic customers** (if tracked)\n\n**AI visibility metrics (quarterly review minimum):**\n- **Brand mention frequency** across platforms (60-100+ runs per prompt)\n- **AI referral traffic** (source\u002Fmedium reports in GA4)\n- **Citation rate** — how often your domain is cited across AI queries\n- **Competitor share-of-voice in AI**\n- **Per-platform visibility** — never collapse into a single score\n\n### Metrics to retire\n\nPer industry reporting, these metrics should NOT be used as primary KPIs:\n\n| Metric | Why retire it | What to use instead |\n|--------|--------------|---------------------|\n| **Organic traffic as standalone KPI** | Lacks intent context; treats all traffic as equal | Organic sessions segmented by intent |\n| **Average keyword position** | Meaningless in aggregate (averaging ranks across different queries is statistically incoherent) | Ranking distribution buckets (top 3, top 10, top 20) |\n| **Domain authority as a business metric** | A third-party proxy score, not a Google metric | Referring domain count and quality, actual ranking performance |\n| **Bounce rate in isolation** | GA4 redefined it; often misinterpreted | Engagement rate + task completion signals |\n| **Single-run AI visibility \"score\"** | Non-deterministic; meaningless without sample size | Visibility % across 60+ runs per prompt per platform |\n| **Individual meta description CTR** | Google rewrites ~60% of meta descriptions; you're measuring Google's rewrite, not yours | Page-level CTR trends with content changes logged |\n\n### Revenue attribution formula\n\n```\nMonthly Organic Value =\n  (Organic Sessions × Conversion Rate × Average Order Value)\n  +\n  (Lead Gen Conversions × Lead Value × Close Rate)\n```\n\n**Important:** This is a last-click formula. Multi-touch attribution typically reveals 30-60% more organic value than last-click. If you can, use GA4's data-driven attribution model or build a custom model that credits organic for its role in multi-touch journeys.\n\n**Document your model.** Every report should state which attribution model is used and its known limitations. Stakeholders will ask why \"SEO revenue\" differs from \"paid social revenue\" — the model is the answer.\n\n### AI visibility measurement protocol (reporting layer)\n\nWhen reporting AI visibility to stakeholders, **every metric must include sample size and the measurement protocol**. See `references\u002Fai-metrics-reporting.md` for the detailed reporting patterns.\n\nCore rules:\n\n- **Sample size minimum:** 60 runs per prompt per platform. Anything less is noise.\n- **Never report \"rank position\" in AI output.** \u003C0.1% consistency — it's not a stable metric.\n- **Report per-platform, not aggregated.** 86% of top-cited sources are platform-unique; aggregating hides the pattern.\n- **Quarterly cadence** is the default; monthly if you're actively optimizing.\n- **Flag the confidence** of every AI metric: Data-backed, Directional, or Experimental.\n\n### Algorithm update assessment framework\n\nWhen Google rolls out a core update or an AI platform shifts behavior:\n\n1. **Pause before reporting.** Google core updates take 2-4 weeks to fully roll out. Initial ranking shifts are not final.\n2. **Segment the impact.** Don't report \"traffic is down 12%.\" Break it down: which pages? which intent buckets? which query types?\n3. **Cross-reference with known changes.** Was there a recent migration? A new competitor? A CWV regression? Update impact gets blamed for unrelated issues constantly.\n4. **Compare to industry.** If traffic dropped 12% and the industry dropped 15%, you're relatively stable. If the industry is flat and you dropped 12%, it's specific to your site.\n5. **Flag what you don't know.** \"Some of this may be algorithm-driven and some may be zero-click expansion from AI Overviews — it will take 30-60 days to distinguish.\"\n6. **Recommend observation windows, not reactive changes.** The worst thing post-update is to start rewriting content before the update finishes rolling out.\n\nSee `references\u002Falgorithm-update-playbook.md` for the full diagnostic playbook.\n\n### Report structure template\n\nAdapt to the audience. Executives want the one-pager; SEO teams want the detail.\n\n```\n## SEO Performance Report — [Period]\n\n### Executive Summary\n[2-3 sentences: what happened this period, what it means for the business, what to do next. No jargon. No fluff.]\n\n### Business Impact\n| Metric | This Period | Prior Period | YoY | Target |\n|--------|-------------|--------------|-----|--------|\n| Organic Revenue | | | | |\n| Organic Leads | | | | |\n| Organic Sessions (non-branded) | | | | |\n| Organic CAC | | | | |\n\n⚠️ Attribution model: [last-click \u002F multi-touch \u002F data-driven]. GSC data ~75% filtered — trends more reliable than absolutes.\n\n### Search Visibility\n- Ranking distribution (top 3 \u002F top 10 \u002F top 20)\n- Branded vs non-branded impressions and clicks\n- SERP feature presence (featured snippets, PAA, AI Overviews)\n- New vs lost keywords this period\n\n### AI Search Visibility (if tracking)\n| Platform | Visibility % | Δ vs last | Sample size |\n|----------|-------------|-----------|-------------|\n\n⚠️ AI metrics are probabilistic. Replication studies: \u003C1% chance of same brand list from same prompt. All figures represent statistical patterns across [X] runs per prompt per platform. Focus on quarterly trends.\n\n### Content Performance\n- Top performing pages (by revenue\u002Fconversions, not just traffic)\n- Decaying content (pages losing rankings or traffic)\n- Content refresh priorities (decayed priority pages)\n- New content performance (published this period)\n\n### Technical Health\n- Core Web Vitals (LCP, INP, CLS — field data)\n- Indexation status (indexed vs discovered vs excluded)\n- Critical issues (GSC errors, coverage issues, manual actions)\n- AI crawler access status\n\n### Competitive Landscape\n- Key ranking movements in the competitive set\n- Opportunities (pages where we gained position)\n- Threats (pages where competitors gained)\n\n### Recommended Actions (Prioritized)\n| Priority | Action | Expected Impact | Effort | Timeline |\n|----------|--------|----------------|--------|----------|\n\n### Appendix: Methodology Notes\n- Data sources: GSC, GA4, rank-tracking \u002F backlink tool, [other]\n- Attribution model in use\n- AI measurement protocol and sample sizes\n- Known limitations: GSC filtering, AI non-determinism, attribution caveats\n- Period definitions: [date range, comparison period]\n```\n\n### Reporting cadence\n\n| Cadence | Audience | Focus | Output |\n|---------|----------|-------|--------|\n| **Weekly (tactical)** | SEO team, content team | Rankings changes, traffic anomalies, technical issues, newly indexed pages | Dashboard + 5-bullet weekly note |\n| **Monthly (strategic)** | Marketing leadership, department heads | Full performance across KPI hierarchy, content wins\u002Flosses, recommendations | Structured report (template above) |\n| **Quarterly (deep dive)** | Executive team, board if applicable | Trend analysis, strategy review, competitive repositioning, AI visibility assessment, ROI analysis | Comprehensive presentation |\n| **Ad-hoc (incident)** | Relevant stakeholders | Algorithm updates, major traffic shifts, migrations, crises | Incident report with diagnosis + plan |\n\n### Dashboard layer\n\nFor ongoing visibility, build a dashboard (Looker Studio, GA4 native, or a BI tool) with:\n\n- Rolling 12-month trend for priority KPIs\n- Branded vs non-branded segmentation\n- Top pages by revenue (not just traffic)\n- Top queries by click volume and by impression volume (two different lists)\n- CWV field scores\n- Referring domain velocity\n- AI visibility panel with sample sizes surfaced\n\nUpdate weekly; review monthly in the structured report.\n\n## Output format\n\nSee the report structure template above. For each report, customize the sections based on what the audience can act on. Executive reports drop the technical detail; SEO team reports drop the business framing.\n\n## Example — monthly SEO report for a mid-market SaaS\n\n**Period:** March 2026\n**Audience:** VP of Marketing + Head of Growth\n\n---\n\n### Executive summary\n\nOrganic revenue grew 18% month-over-month, driven by 12 new product analytics cluster pages published in Q1 now ranking on page 1. AI visibility on ChatGPT rose from 12% to 19% after rewriting the pricing page to server-side render. Core Web Vitals regressed on mobile (LCP 3.8s vs 2.4s target) after a blog redesign — fix planned for sprint 14.\n\n### Business impact\n\n| Metric | Mar 2026 | Feb 2026 | YoY | Target |\n|--------|----------|----------|-----|--------|\n| Organic revenue (multi-touch) | $187K | $158K | +34% | $200K |\n| Organic demo requests | 142 | 119 | +41% | 150 |\n| Organic sessions (non-branded) | 89K | 82K | +22% | 100K |\n\n⚠️ Multi-touch data-driven attribution via GA4. GSC impression data ~75% filtered — trend reliable; absolute impression count understated.\n\n### Search visibility\n\n- **Ranking distribution:** 34 keywords in top 3 (+6), 127 in top 10 (+14), 310 in top 20 (+23)\n- **Branded traffic:** 38% of organic sessions (stable)\n- **Non-branded traffic:** 62% (up from 58% last month — the 12 new cluster pages are the primary driver)\n- **AI Overviews present** on 28 of 50 priority keywords — we are cited in 7 of them\n- **New keywords (first rank):** 47 (strong)\n- **Lost keywords:** 12 (normal churn)\n\n### AI search visibility\n\n| Platform | Visibility % | Δ vs Feb | Sample size |\n|----------|-------------|----------|-------------|\n| ChatGPT | 19% | +7% | 15 prompts × 60 runs |\n| Perplexity | 24% | +2% | 15 prompts × 60 runs |\n| AI Overviews | 14% | +6% | 15 prompts × 60 runs |\n| AI Mode | 8% | +1% | 15 prompts × 60 runs |\n\n⚠️ AI metrics are probabilistic. Replication studies: \u003C1% chance of same brand list on same prompt. Figures represent patterns across 60 runs per prompt per platform. ChatGPT gain corresponds to pricing page SSR launch on March 11.\n\n### Content performance\n\n- **Top performers:** `\u002Fproduct-analytics-guide` (pillar, 8.2K sessions, 22 demos), `\u002Famplitude-vs-mixpanel-vs-posthog` (4.8K sessions, 31 demos — highest converting), `\u002Fevent-scoping-framework` (3.1K sessions, 18 demos)\n- **Decaying:** 3 old category pages losing rankings steadily — queued for Q2 refresh\n- **Refresh priorities:** `\u002Fwhat-is-product-analytics` (ranked #8, falling from #3 over 3 months)\n\n### Technical health\n\n- **CWV (mobile, field data):** LCP 3.8s ⚠️ (target ≤2.5s), INP 180ms ✅, CLS 0.08 ✅\n- **LCP regression** traced to blog redesign — new hero image treatment is 2.1MB unoptimized. Fix in sprint 14.\n- **Indexation:** 924 indexed (+12), 18 new Coverage errors (all thin tag archives, queued for noindex)\n- **AI crawler access:** GPTBot, PerplexityBot, ClaudeBot all hitting site normally (~40\u002Fday, ~25\u002Fday, ~10\u002Fday respectively)\n\n### Recommended actions\n\n| Priority | Action | Impact | Effort | Timeline |\n|----------|--------|--------|--------|----------|\n| Critical | Fix LCP regression (compress blog hero images, preload) | High — CWV is page experience signal | Low | Sprint 14 |\n| High | Refresh `\u002Fwhat-is-product-analytics` with updated stats, ski ramp structure | High — recovery of top-3 position | Medium | April W2 |\n| High | Publish Q2 benchmark data study | High — PR asset + linkable content | High | End of April |\n| Medium | Server-render remaining 4 JS-heavy landing pages | Medium — unlocks AI visibility on those pages | Medium | Sprint 15 |\n| Medium | Add FAQPage schema to top 10 commercial pages | Medium — rich result eligibility | Low | April W1 |\n\n### Appendix\n\n- Data sources: GSC, GA4 (DDA attribution), rank-tracking tool, AI visibility tracker\n- Attribution: GA4 data-driven; may differ from GA4 last-click by ~40% for organic\n- AI measurement: 15 prompts × 60 runs across ChatGPT, Perplexity, AI Overviews, AI Mode. Run on 3\u002F28-3\u002F30. Re-measured after pricing page SSR launch.\n- Known limitations: GSC ~75% impression filtering, AI probabilistic, CWV field data 28-day lag\n- Period: March 1-31, 2026. Compared to February 1-28, 2026 and March 1-31, 2025.\n\n---\n\n## Guidelines\n\n- **Lead every report with the GSC caveat.** Stakeholders consistently misinterpret SEO data because they don't know it's ~75% filtered. State it every time.\n- **Always segment organic traffic by intent.** Aggregate organic traffic is near-useless as a KPI — it conflates transactional wins with informational zero-click impressions.\n- **Use the GSC branded query filter (November 2025+)** to separate branded and non-branded. This is one of the biggest analytics improvements in years and most teams still don't use it.\n- **Retire average keyword position.** Averaging positions across different queries is statistically incoherent. Use ranking distribution (top 3, top 10, top 20) instead.\n- **Report AI visibility with sample sizes every time.** \"Visibility 18% across 60 runs\" not \"Visibility 18%.\" The second form invites false precision.\n- **Never collapse AI visibility into a single score.** 86% of top-cited sources are platform-unique. A \"unified AI score\" hides the actionable signal.\n- **Multi-touch attribution reveals the real organic contribution.** Last-click typically under-reports organic by 30-60%. If your stakeholders think SEO is underperforming, the attribution model is often the cause.\n- **Zero-click is the new normal.** 58.5% US, 77.2% mobile searches end without a click (third-party search studies). Don't report declining clicks as declining performance if impressions and rankings are stable.\n- **Algorithm update impact takes 2-4 weeks to stabilize.** Don't recommend reactive changes within the rollout window — wait for the dust to settle.\n- **Content decay is a leading indicator of traffic loss.** A page dropping from #3 to #8 over 90 days predicts a traffic drop; catch it early through content performance tracking.\n- **Track referring domains and brand mentions together** — AI visibility depends on both. Mentions without links still feed AI signal.\n- **Build for decisions, not dashboards.** Every metric in every report should answer the question \"what would we do differently if this number changed?\" If nothing, remove the metric.\n- **The report is for the reader.** Executives want the one-page summary with the one recommendation. SEO specialists want the detail. Build two versions if needed.\n- **Flag confidence levels on every recommendation.** \"Data-backed,\" \"directional,\" or \"experimental.\" This builds trust with stakeholders who will come back to hold you accountable.\n- **Reports are a trust instrument.** Over-claiming on good months and hiding bad months destroys the discipline. Show both, contextualize both, and let the trend do the talking.\n\n## Reference files\n\nRead these when the task warrants:\n\n- **`references\u002Fai-metrics-reporting.md`** — Detailed guidance on reporting AI visibility metrics responsibly, including sample-size tables, caveat language, and common stakeholder pitfalls. Read when building an AI visibility section of a report.\n- **`references\u002Falgorithm-update-playbook.md`** — Diagnostic playbook for responding to Google core updates, AI platform shifts, and traffic anomalies. Read when you suspect an algorithm-driven impact.\n\n## Cross-skill handoffs\n\n- **← all other SEO skills** — Receive baselines, KPIs, and priority actions for tracking.\n- **→ seo-keyword-research** — Flag keywords with declining performance for re-evaluation.\n- **→ seo-content-strategy** — Flag content with decay for refresh prioritization.\n- **→ seo-technical-audit** — Escalate CWV regressions, indexation issues, crawl anomalies.\n- **→ aeo-ai-search-visibility** — Feed visibility baseline data for strategy iteration.\n",{"name":5,"description":7},"main","c137446804eb85b2cc0ba009c62068253a7c47be","skills\u002Fseo-reporting-measurement",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":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill seo-reporting-measurement",{"tool":69,"command":70},"bunx","bunx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill seo-reporting-measurement",{"tool":72,"command":73},"pnpm","pnpm dlx skills add narayan-metaflow\u002Fmetaflow-marketing-skills --skill seo-reporting-measurement","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-reporting-measurement\u002FSKILL.md",true,[77,83],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fai-metrics-reporting.md","REFERENCE","参考文档",8636,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-reporting-measurement\u002Freferences\u002Fai-metrics-reporting.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Falgorithm-update-playbook.md",10967,"https:\u002F\u002Fgithub.com\u002Fnarayan-metaflow\u002Fmetaflow-marketing-skills\u002Fblob\u002Fmain\u002Fskills\u002Fseo-reporting-measurement\u002Freferences\u002Falgorithm-update-playbook.md",[88,114,135,164,192,211],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":96,"publishedAt":50},"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",[94,95],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},[35,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"technical","technical seo","technical seo ai crawler audit","audit","crawler","crawl","diagnose","answer","issues","clean","indexer","google","prioritize","site","content","html","path",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":122,"publishedAt":50},"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",[120,121],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},[123,35,124,125,126,127,128,129,130,131,132,133,134],"kt","seo max kt","max","aeo","long-form","methodology","form","for","bundle","full","read","long",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":146,"publishedAt":50},"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",[141,142,145],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},[35,147,148,149,150,151,152,153,154,155,104,156,157,158,159,160,161,38,162,163],"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":165,"name":166,"nameZh":6,"description":167,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":168,"categories":169,"keywords":178,"publishedAt":50},"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",[170,173,174,175],{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},{"categoryNo":176,"categoryName":177,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[35,126,179,180,181,111,182,183,184,130,104,185,186,187,188,133,189,190,108,191],"strategy","seo aeo content strategy","content strategy","Citations","as","core","design","information","earn","structure","systems","ramp","prompts",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":201,"publishedAt":50},"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",[198],{"categoryNo":199,"categoryName":200,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[202,157,203,204,35,130,147,156,205,206,207,208,149,38,162,189,151,209,210,132],"prompt picker","llm visibility","brand","up","journey","prompt library","across","library","tracking",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":217,"publishedAt":50},"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",[],[218,219,156,220,221,222,223,224,225,130,226,227,228,188,229,108,230,231],"paid search ads playbook","paid","ads","playbook","alignment","lens","types","planning","page","landing","conversion","microsoft","match","landing-page"]