High-Velocity Advertising (HVA) — the discipline for creative discovery at machine speed. CLEAR decision rule, the five-layer Stack, the Read Ladder, the two-clocks asymmetry, Benjamini-Hochberg correction, the Vault moat, and the Fit Boundary. The spec behind /hva, /hva-forge, /hva-lint, /hva-review, /hva-vault and the hva-desk agent.

2026-06-151

irresistible-offer

DojoCodingLabs/GTM-Engineering-Command-Center

Direct response copywriting framework for paid ads — Hormozi offers, PAS, AIDA, variation generation, LATAM hooks

2026-06-151

synthetic-demand

DojoCodingLabs/GTM-Engineering-Command-Center

Synthetic Demand Validation (SDV) — predict purchase intent, price tolerance, and objections for ad creatives, offers, and landing copy by eliciting free-text reactions from a synthetic persona panel and mapping them to Likert distributions. Comparative ranking is the headline signal. Use for "will this convert", "which variant wins", "is $X the right price", "pre-test this creative/offer/copy before spend".

2026-06-151

cross-channel-logic

DojoCodingLabs/GTM-Engineering-Command-Center

Cross-channel GTM orchestration — channel sequencing, attribution models, retargeting chains, multi-touch journeys

2026-06-151

name	seo-engineering
description	SEO engineering — technical SEO, content strategy, schema markup (JSON-LD), GEO optimization for AI citations

SEO Engineering

Engineer search visibility through technical optimization, content strategy, structured data, and generative engine optimization (GEO). This skill covers the full SEO stack from crawlability to AI citation optimization.

SEO Architecture

SEO System
  ├── Technical SEO (how search engines crawl and index your site)
  │   ├── Core Web Vitals (LCP, INP, CLS)
  │   ├── Crawlability (sitemap, robots.txt, internal linking)
  │   ├── Indexability (canonical URLs, noindex, hreflang)
  │   └── Site Architecture (URL structure, breadcrumbs, pagination)
  ├── Content SEO (what you publish and how it ranks)
  │   ├── Keyword Research (intent mapping, volume, difficulty)
  │   ├── Content Clusters (pillar pages + supporting articles)
  │   ├── Programmatic SEO (templated pages at scale)
  │   └── Content Freshness (update cadence, topical authority)
  ├── Schema Markup (structured data for rich results)
  │   ├── Organization, Product, FAQ, HowTo, BreadcrumbList
  │   ├── Article, Course, SoftwareApplication
  │   └── Review, AggregateRating, VideoObject
  └── GEO (Generative Engine Optimization)
      ├── AI Citation Optimization (ChatGPT, Claude, Perplexity)
      ├── Entity-first content architecture
      └── Structured knowledge format

Core Web Vitals Targets

Metric	Good	Needs Improvement	Poor
LCP (Largest Contentful Paint)	< 2.5s	2.5-4.0s	> 4.0s
INP (Interaction to Next Paint)	< 200ms	200-500ms	> 500ms
CLS (Cumulative Layout Shift)	< 0.1	0.1-0.25	> 0.25

Framework-Specific Optimizations

Framework	LCP Fix	INP Fix	CLS Fix
Next.js	Use `next/image` with priority, SSR above-fold	React Server Components, reduce client JS	Reserve space with `width`/`height` on images
Vite/React (SPA)	SSG/prerender critical pages, lazy load below-fold	Code split routes, defer non-critical JS	Fixed layout containers, font-display: swap
Astro	Static by default (good), optimize images	Minimal JS by design (good)	Use Astro Image component

URL Structure Best Practices

Good:
  /blog/how-to-learn-solidity
  /courses/blockchain-fundamentals
  /tools/smart-contract-auditor
  /pricing

Bad:
  /blog/post/12345
  /p?id=abc123
  /courses/course-detail?slug=blockchain
  /page/about-us/company/info

Rules:

Lowercase, hyphens (never underscores or camelCase)
3-5 words max per slug
Include primary keyword
No dates in evergreen content URLs
Flat hierarchy (max 3 levels deep)

Sitemap Configuration

<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  <url>
    <loc>https://example.com/</loc>
    <lastmod>2026-04-14</lastmod>
    <changefreq>weekly</changefreq>
    <priority>1.0</priority>
  </url>
  <url>
    <loc>https://example.com/blog/learn-solidity</loc>
    <lastmod>2026-04-10</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
  </url>
</urlset>

Sitemap Rules

Maximum 50,000 URLs per sitemap file (use sitemap index for more)
Only include canonical URLs (no duplicates, no redirects)
Update lastmod only when content actually changes
Exclude noindex pages, paginated pages, search results
Submit via Google Search Console and reference in robots.txt

robots.txt Template

User-agent: *
Allow: /
Disallow: /api/
Disallow: /dashboard/
Disallow: /settings/
Disallow: /admin/
Disallow: /_next/data/
Disallow: /auth/

Sitemap: https://example.com/sitemap.xml

Keyword Research Framework

Intent Classification

Intent	Signal Words	Content Type	Conversion Potential
Informational	how to, what is, guide, tutorial	Blog posts, guides	Low (top of funnel)
Navigational	[brand name], login, pricing	Homepage, product pages	Medium
Commercial	best, vs, review, comparison, top 10	Comparison pages, listicles	High
Transactional	buy, signup, free trial, discount, pricing	Landing pages, pricing	Highest

Content Cluster Model

Pillar Page: "Complete Guide to Smart Contract Development"
  ├── Cluster: "Solidity Tutorial for Beginners" → links to pillar
  ├── Cluster: "Smart Contract Security Best Practices" → links to pillar
  ├── Cluster: "Testing Smart Contracts with Hardhat" → links to pillar
  ├── Cluster: "Smart Contract Design Patterns" → links to pillar
  └── Cluster: "Deploying Smart Contracts to Mainnet" → links to pillar

Each cluster page links back to the pillar. The pillar links to all clusters. This creates topical authority.

Environment Variables

GOOGLE_SEARCH_CONSOLE_SITE=https://example.com  # Verified property URL
GOOGLE_SITE_VERIFICATION=XXXXXXXXXXXX            # Meta tag verification code

Measurement

Key SEO Metrics

Metric	Source	Frequency
Organic Traffic	Google Analytics / PostHog	Weekly
Keyword Rankings	Google Search Console	Weekly
Indexed Pages	Google Search Console	Monthly
Core Web Vitals	PageSpeed Insights API	Monthly
Backlink Profile	Ahrefs / Moz API	Monthly
AI Citation Rate	Manual checks + Perplexity	Monthly

Citation Science (April 2026)

Research from the SEO/GEO Atlas (7 independent passes, April 2026) reveals that AI citation is predictable and engineerable. The following 12-point checklist determines whether a page will be cited by ChatGPT, Perplexity, Google AI Overviews, or Claude.

The 12-Point Citation Checklist

Every page targeting AI citation must pass these checks:

Retrieval Rank -- Position 0 in retrieval = 58% citation probability. Position 10 = 14%. Rank in the retrieval index is the single strongest predictor of citation. Optimize for traditional search ranking first.
Title-Query Alignment -- Pages where the title aligns with the query at 50%+ see a 2.2x citation lift. Titles must mirror the exact phrasing users (and AI fan-out queries) use.
Focused Scope -- 500-2,000 words with 7-20 subheadings. Focused pages beat "ultimate guides." AI systems prefer extractable, self-contained answers over sprawling content.
The Ski Ramp -- 44.2% of all citations come from the first 30% of a page. Front-load the highest-density, most quotable information at the top.
Optimal Passage Length -- 134-167 words per section (between H2/H3 headings). 120-180 words is the sweet spot for ChatGPT-style extraction.
Definitive Language -- Use authoritative, declarative statements. "X is the leading..." not "X might be considered..."
Q&A Format -- Question headings with direct answers in the first sentence. AI systems extract Q&A pairs with high fidelity.
Entity Density -- Target 20.6% proper noun density (named people, companies, products, technologies). Web average is 5-8%. High entity density signals factual, citable content.
Balanced Tone -- Avoid promotional or hyperbolic language. AI systems prefer neutral, authoritative content. A balanced perspective actually increases citation probability.
Simpler Writing -- Grade level 16 (Flesch-Kincaid). Academic-level clarity without unnecessary complexity. Readable but substantive.
Source Citations -- Include outbound references to authoritative sources with specific data. AI systems prefer content that itself cites sources.
Freshness Signals -- Visible datePublished and dateModified. AI systems inject "2026" into 28.1% of fan-out queries, so dated content gets filtered out.

The Bimodal Reality

Only 15% of retrieved pages ever get cited. Citation is bimodal: pages are either always-cited or never-cited. There is almost no middle ground. Passing the 12-point checklist moves a page from the "never" category to "always."

Platform-Specific Optimization

Each AI platform has different source biases, content preferences, and citation patterns. Optimize for the platform your audience uses most.

ChatGPT

Source bias: Wikipedia (48%), Reddit (13%), YouTube (9%)
Content format: 120-180 word sections, straight H2 headings, no fancy formatting
Naming: Names brands in 36% of responses
Strategy: Mirror Wikipedia's neutral encyclopedic tone. Use clear H2 headings that match common queries. Keep sections self-contained and extractable.

Google AI Overviews

Source bias: YouTube (23%), Reddit (21%), established publishers
Content format: Schema-dense pages, Google top-10 rank required as prerequisite
Strategy: You must already rank in Google's top 10 to be eligible for AI Overviews. Add Article schema with datePublished/dateModified, FAQPage schema, and Organization schema with sameAs links.

Perplexity

Source bias: Reddit (47%), academic/white-paper format content
Content format: Data tables, numbered lists, statistical citations
Strategy: Write in a white-paper format with explicit data tables. Perplexity heavily favors content that looks like research. Include statistical claims with source attributions.

Claude

Source bias: Favors deep structural content, technical documentation
Content format: Detailed, well-organized technical content
Naming: Names brands in 46% of responses (vs ChatGPT's 36%)
Strategy: Deeper structural content with clear hierarchies. Claude citations favor pages that demonstrate domain expertise through comprehensive coverage.

The 11% Overlap Problem

Only 11% of domains are cited by BOTH ChatGPT and Google AI Overviews. Optimizing for one does not guarantee visibility on the other. Platform-specific pages or content variations may be necessary for full coverage.

AI Crawlers and robots.txt

AI systems use two types of crawlers: retrieval crawlers (fetch pages to answer queries in real-time) and training crawlers (fetch pages to train models). The recommended strategy is to allow retrieval crawlers (they drive citations) while optionally blocking training crawlers.

The Balanced robots.txt Configuration

# === AI RETRIEVAL CRAWLERS (ALLOW -- these drive citations) ===
User-agent: OAI-SearchBot
Allow: /

User-agent: ChatGPT-User
Allow: /

User-agent: Claude-SearchBot
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Applebot-Extended
Allow: /

# === AI TRAINING CRAWLERS (OPTIONAL BLOCK -- your content, your choice) ===
User-agent: GPTBot
Disallow: /

User-agent: ClaudeBot
Disallow: /

User-agent: CCBot
Disallow: /

User-agent: Google-Extended
Disallow: /

# === STANDARD SEARCH CRAWLERS (ALLOW) ===
User-agent: Googlebot
Allow: /

User-agent: Bingbot
Allow: /

User-agent: *
Allow: /
Disallow: /api/
Disallow: /dashboard/
Disallow: /admin/
Disallow: /auth/

Sitemap: https://example.com/sitemap.xml

Key distinction: OAI-SearchBot (retrieval, drives citations) vs GPTBot (training). Claude-SearchBot (retrieval) vs ClaudeBot (training). Blocking training crawlers does NOT affect your citation visibility.

The LATAM GEO Arbitrage

For products targeting Latin America (like DojoOS), there is a 12-24 month arbitrage window in AI-driven search.

The Global Spanish Problem

AI models collapse all Spanish-speaking markets into a single "Global Spanish" bucket. A query about "bootcamps de programacion en Costa Rica" returns the same results as "bootcamps de programacion en Mexico" -- even though the markets, prices, regulations, and audience are completely different.

The Hreflang Opportunity

Proper hreflang tagging (es-CR, es-MX, es-CO, es-AR) is massive arbitrage:

Forces AI systems to recognize market-specific content
Creates differentiated entities per country in the knowledge graph
Extremely low competition (almost nobody in LATAM is doing GEO-specific optimization)
Costa Rica is a Tier-3 market for AI citations -- virtually no competition

The Window

12-24 months before larger competitors catch up
First-mover advantage in LATAM GEO compounds (each citation increases future citation probability)
43% of non-English fan-out queries are actually in English (the "Global English Gatekeeper" effect), so English content targeting LATAM keywords also gets cited

Implementation

Create country-specific landing pages with proper hreflang tags
Include country-specific entities (local companies, local regulations, local salary data)
Register the brand entity on Wikidata with country-specific claims
Submit to Bing Webmaster Tools (Bing powers many AI retrieval indexes)

Knowledge Base

skills/seo-engineering/rules/content-strategy.md -- Keyword research, content clusters, programmatic SEO
skills/seo-engineering/rules/technical-seo.md -- Crawlability, indexability, Core Web Vitals
skills/seo-engineering/rules/geo-optimization.md -- GEO patterns, AI citation optimization
knowledge/seo-geo-atlas-2026.md -- SEO/GEO/AEO/LLM Visibility Atlas April 2026: Citation science, platform architecture, AI crawler configuration, LATAM GEO arbitrage, 90-day action plan. 2,221 lines from 7 independent research passes.