본문으로 건너뛰기
cccskills
무료GitHub에 공개

programmatic-seo-playbook

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Replicate the 7 programmatic SEO plays that took Zapier, Clay, Composio, Gamma, Mintlify, HeyGen and HubSpot to

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Planning search pages from an existing company or product database
  • Designing pages for app integrations and supported environments
  • Expanding search pages across languages and regions
  • Planning checks for citations in AI search answers

포함된 파일(1)

  • SKILL.md9.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Programmatic SEO Playbook

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.

Process

  1. Learn the business - product, who buys it, what data the company already owns (integrations, customers, catalog, languages, user content), CMS, and how many pages it can realistically ship per week
  2. Score the 7 plays - for each play: do they have the data, is there search demand, does the page answer a real question (see scoring table)
  3. Pick 1 to 3 plays - never all seven; one play done at 1,000+ pages beats seven done at 50
  4. Design each play - entity list, URL pattern, title formula, template sections, unique data per page, internal links, schema
  5. Plan the rollout - first 50 pages by hand-checked quality, then batches, index monitoring, and the "thin page" kill rule
  6. Set the AI-citation checks - the query set to test in ChatGPT, Perplexity and AI Overviews, and what to change when a page ranks in Google but isn't cited

The 7 plays

#PlayWho proved itData you needPages builtResult
1A page for every entity in your databaseClayYour own dataset (companies, people, products)20,692 company dossier pages + 476 "generate [industry] leads" tools15K AI citations/mo, Perplexity cites Clay 4K times/mo
2A page for every pair of things you connectZapierList of integrations or compatible items110,000 "[App A] + [App B]" pages4.2M organic visits/mo, 161K AI citations/mo (most of any SaaS)
3A page for every item × every environmentComposioItems × frameworks, platforms, use cases18,395 "[app] for [framework]" pages47K organic visits/mo on 582 keywords
4Let users publish the pagesGammaPublic user-generated content120,000+ indexed user decks + 410 guide pages × 24 languages3.4M organic visits/mo from 21K keywords
5Make every customer link backMintlifyA product that lives on the customer's domain2,506 pages of its own, 19,800 referring domainsDR 90, ChatGPT cites it more than Perplexity, Gemini and Copilot combined
6A page for every language and localeHeyGenLanguages, regions, formats you support102 translate pairs, 68 TTS languages, 260 tool pages, × 12 locales1.5M organic visits/mo, 26K AI citations/mo, $200M ARR
7A free tool for every job your buyer doesHubSpotSmall jobs adjacent to the productWebsite grader, email signature maker, 64 "[tool] vs HubSpot" pages + 7,800 blog posts4.0M organic visits/mo, 84K AI citations/mo

Scoring table

Score each play 0 to 3 on every column, sum, pick the top scores. Skip any play with a 0 in "answers a question".

Column0123
We have the datanonecould scrape or buy itpartial, in our productcomplete, in our database today
Search demand existsnobody searches the patternlong tail onlysteady monthly volumecompetitors already rank for it
Answers a questionpage would be a name swapanswers with one factanswers with data others don't haveanswers and lets the visitor act (tool, download, signup)
Path to signupnonelink in footerCTA relevant to the pagethe page IS the product entry point

Play design cards

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

Play notes

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.

Rollout plan

WeekDoCheck
1Entity list, URL pattern, template, 10 pages by handEvery page answers its question without the template showing
2First 50 pages, hub page, sitemap, schemaSearch Console: all 50 indexed within 14 days
3-4500 pages/weekImpressions per page; noindex anything at 0 impressions after 90 days
5+Add comparison pages and FAQ blocks with the problem phrasingAI citation checks below

AI-citation checks

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:

  • Retitle to mirror the question wording ("how do I connect X to Y", not "X Y integration")
  • Add an FAQ block with the question as the H2 and a direct 2-sentence answer
  • Confirm the page is indexed in Bing (no Bing index, no ChatGPT citation)
  • Get the page mentioned on one third-party site: a listicle, a Reddit answer, a partner's docs

Output format

  1. Business summary and the data the company already owns
  2. Scoring table with all 7 plays scored
  3. Play design cards for the chosen 1 to 3 plays
  4. Rollout plan with page counts per week
  5. The 20-40 question list for AI-citation checks
  6. Risks: thin-content exposure, duplicate pairs, index bloat, and the kill rules for each

Data access (Ryze MCP)

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:

  • claude.ai / Claude Desktop: Settings → Connectors → Add custom connector → https://connector.get-ryze.ai/mcp
  • Claude Code: claude mcp add ryze --transport http https://connector.get-ryze.ai/mcp
  • Cursor: Settings → MCP → add the same URL

Setup guide: https://www.get-ryze.ai/how-to-connect-claude-to-google-meta-ads-mcp

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Evaluating results from a headline A/B test
  • Estimating sample sizes before starting a test
  • Comparing results by device or traffic source
irinabuht12-oss/marketing-skills5.5천2026년 10월 13일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Planning a test for a new ad headline
  • Estimating traffic and time needed for a landing page test
  • Checking whether interim results justify choosing a winner
irinabuht12-oss/marketing-skills5.5천2026년 10월 13일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Reviewing an account inherited from another team or agency
  • Running a quarterly advertising account health check
  • Investigating structural causes of stalled performance
irinabuht12-oss/marketing-skills5.5천2026년 10월 13일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Refreshing ad copy when existing creatives lose effectiveness
  • Starting an ad copy brainstorming session
  • Testing messaging angles for a new audience
irinabuht12-oss/marketing-skills5.5천2026년 10월 13일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Checking extension coverage before launching campaigns
  • Quarterly reviews of outdated promotional details
  • Updating extensions after website changes or product launches
irinabuht12-oss/marketing-skills5.5천2026년 10월 13일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Reviewing budget allocation across advertising channels
  • Finding channels with declining returns on extra spending
  • Balancing proven campaigns with new experiments
irinabuht12-oss/marketing-skills5.5천2026년 10월 13일 업데이트

irinabuht12-oss의 스킬 모두 보기

이 스킬의 문제 신고