Plan search pages at scale using data a business already owns. Compare seven approaches, select suitable ones, and define page templates, URL patterns, publishing stages, and checks for whether AI search services cite the pages.
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Replicate the 7 programmatic SEO plays that took Zapier, Clay, Composio, Gamma, Mintlify, HeyGen and HubSpot to
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Seven plays, each proven by a company at $100M+ ARR. The skill picks the plays that fit the user's business, then produces the data model, URL pattern, page template, rollout plan and the AI-citation checks for each one.
Numbers below come from the companies' own sitemaps and Ahrefs (pulled Sep 2026). Quote them, don't invent new ones.
| # | Play | Who proved it | Data you need | Pages built | Result |
|---|---|---|---|---|---|
| 1 | A page for every entity in your database | Clay | Your own dataset (companies, people, products) | 20,692 company dossier pages + 476 "generate [industry] leads" tools | 15K AI citations/mo, Perplexity cites Clay 4K times/mo |
| 2 | A page for every pair of things you connect | Zapier | List of integrations or compatible items | 110,000 "[App A] + [App B]" pages | 4.2M organic visits/mo, 161K AI citations/mo (most of any SaaS) |
| 3 | A page for every item × every environment | Composio | Items × frameworks, platforms, use cases | 18,395 "[app] for [framework]" pages | 47K organic visits/mo on 582 keywords |
| 4 | Let users publish the pages | Gamma | Public user-generated content | 120,000+ indexed user decks + 410 guide pages × 24 languages | 3.4M organic visits/mo from 21K keywords |
| 5 | Make every customer link back | Mintlify | A product that lives on the customer's domain | 2,506 pages of its own, 19,800 referring domains | DR 90, ChatGPT cites it more than Perplexity, Gemini and Copilot combined |
| 6 | A page for every language and locale | HeyGen | Languages, regions, formats you support | 102 translate pairs, 68 TTS languages, 260 tool pages, × 12 locales | 1.5M organic visits/mo, 26K AI citations/mo, $200M ARR |
| 7 | A free tool for every job your buyer does | HubSpot | Small jobs adjacent to the product | Website grader, email signature maker, 64 "[tool] vs HubSpot" pages + 7,800 blog posts | 4.0M organic visits/mo, 84K AI citations/mo |
Score each play 0 to 3 on every column, sum, pick the top scores. Skip any play with a 0 in "answers a question".
| Column | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| We have the data | none | could scrape or buy it | partial, in our product | complete, in our database today |
| Search demand exists | nobody searches the pattern | long tail only | steady monthly volume | competitors already rank for it |
| Answers a question | page would be a name swap | answers with one fact | answers with data others don't have | answers and lets the visitor act (tool, download, signup) |
| Path to signup | none | link in footer | CTA relevant to the page | the page IS the product entry point |
For every chosen play, fill this card:
Play: [1-7]
Entity list: [what each page is about, count, where the list lives]
URL pattern: /[type]/[entity-slug]
Title formula: [Entity] [modifier] - [Brand] (mirror the exact words people search and ask AI)
H1: same as title without the brand
Unique data per page: [3+ fields that change per page, not just the name]
Template sections: intro (entity-specific, 80-150 words) / data block / how-to or use case / related entities / CTA
Internal links: hub page -> every spoke; every spoke -> hub + 3-5 siblings + 1 comparison page
Schema: [Product | SoftwareApplication | Organization | Person | ItemList | FAQPage] + BreadcrumbList
Index rule: noindex until the page has [unique data threshold]; kill pages with 0 impressions after 90 days
First 50: hand-check for accuracy, then batch 500/week
1. Entity pages (Clay). Works when the data is yours and public elsewhere is scattered. The dossier page answers "who is the CEO of X" or "how much did X raise" and the CTA is "enrich this lead". Google traffic is small (73K/mo), AI citations are large (15K/mo): entity pages get cited more than they get clicked. Add a "generate [industry] leads" tool page per vertical.
2. Pair pages (Zapier). The strongest pattern in the set. One template, every combination. Requirements: the pair must be real (both sides exist), the page must show what the pair does (triggers, actions, examples), and the CTA must start the pair. 110,000 pages need a sitemap index, hub pages per app, and a canonical rule so A+B and B+A don't compete.
3. Item × environment (Composio). Same idea as pairs but crossed with frameworks, platforms or use cases. Warning from Composio's numbers: 47K Google visits, only 9 ChatGPT citations a month. These pages rank for the integration name ("connect agent to Gmail") but AI is asked the problem ("how do I let my agent use Gmail"). Add the problem phrasing to the title or an FAQ block on every page, or the play ranks in Google and stays invisible in AI.
4. User-generated pages (Gamma). Requires a product where users make something shareable. Make public documents indexable by default (Gamma allows crawlers into /docs), give each a clean title from the user's content, add an "explore" layer of guide pages that answer buyer questions ("can AI make a full presentation for me") and translate that layer. Google AI Mode cites Gamma 14.6K times a month off user pages.
5. Customer backlinks (Mintlify). Only for products that render on the customer's domain (docs, widgets, hosted pages, badges). Every instance carries a link home. 19,800 referring domains off 2,500 pages gives DR 90. Ask: can our product leave a link on the customer's site by default? If yes, this outranks any content play.
6. Language pages (HeyGen). One page per language pair, per language feature, per small tool, then mirror the set into every locale you sell in. Titles mirror the search ("translate video from English to Spanish"). Every added language becomes hundreds of URLs. Use hreflang, one sitemap per locale, and native-quality copy for the top 5 locales.
7. Free tools (HubSpot). Build small tools for jobs adjacent to the product (grader, generator, calculator, checker). Tools get cited by AI more than articles about the same topic. Wrap each tool in a "[tool] vs [competitor]" page and 3-5 how-to posts that link to it. Same keyword count as Zapier, half the AI citations: tools beat essays, pair pages beat tools.
| Week | Do | Check |
|---|---|---|
| 1 | Entity list, URL pattern, template, 10 pages by hand | Every page answers its question without the template showing |
| 2 | First 50 pages, hub page, sitemap, schema | Search Console: all 50 indexed within 14 days |
| 3-4 | 500 pages/week | Impressions per page; noindex anything at 0 impressions after 90 days |
| 5+ | Add comparison pages and FAQ blocks with the problem phrasing | AI citation checks below |
Google ranking is not AI citation (Composio ranks, gets 9 ChatGPT citations a month). For every play, build a list of 20-40 buyer questions and run them monthly in ChatGPT, Perplexity and Google AI Overviews. If a page ranks in Google and is not cited:
This skill works best with live account data. Connect the free Ryze MCP once and Claude reads your Google Ads, Meta Ads, GA4 and Search Console directly:
https://connector.get-ryze.ai/mcpclaude mcp add ryze --transport http https://connector.get-ryze.ai/mcpSetup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp
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Analyze A/B test results to assess whether observed differences are likely due to chance. Check test duration and sample size, compare audience segments, distinguish statistical findings from practical value, and generate hypotheses for follow-up tests.
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Plan and analyze A/B tests for Google and Meta advertising. Estimate required traffic and duration, track results, and assess whether observed differences are statistically significant, meaning unlikely to be explained by chance alone.
中文简介正在准备中。当前显示英语简介。
Review Google and Meta campaign structures against goals, budgets, and conversion data. Identify excessive segmentation, overlapping targeting, and fragmented budgets, then get consolidation recommendations, an explained account structure, and a migration plan.
中文简介正在准备中。当前显示英语简介。
Analyze successful Google and Meta ads to find useful patterns in headlines, body text, and calls to action. Generate new copy variants that preserve those patterns, with platform formatting, test priorities, and suggested pairings for comparison.
中文简介正在准备中。当前显示英语简介。
Review Google Ads extensions, the extra links and details shown with ads, across campaigns. Find missing coverage, weak performance, and outdated information, then get replacement copy based on successful ads and landing pages, plus rollout priorities.
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Analyze spending, revenue, and conversions across advertising channels to recommend budget shifts. Compare overall efficiency with the revenue gained from additional spending, identify saturation, and produce a gradual reallocation plan with monitoring priorities.
中文简介正在准备中。当前显示英语简介。