The era of traditional SEO is over. In 2026, visibility is determined by LLM citation behavior, AI Overview placement, and cross-platform entity reinforcement. CASMOS (Claude AI Search & Monetization Operating System) is not a collection of tips—it’s a modular operating system for exploiting AI-mediated search infrastructure designed for operators who prioritize speed, citations, and revenue over brand longevity.
This guide provides the complete 5-step prompt system, tactical context for each stage, and copy-paste prompts you can run in Claude immediately. Use them sequentially for full execution or modularly for rapid iteration.
Why This System Works in 2026
AI search fundamentally changed how visibility compounds. A manufacturer went from zero to 90 AI Overviews and achieved a 2,300% increase in AI traffic by optimizing for LLM citation behavior. Another site generated 300+ monthly AI referrals and saw 200% month-over-month growth by implementing structured, extractive content. One operator broke into top rankings across 10 pages in just 10 days using GEO-first tactics—no backlinks, no paid ads, no content history.
The pattern is clear: citation capture beats traditional ranking. AI systems prioritize structured data, modular content, and entity signals over domain age or backlink profiles. This creates exploitable gaps for operators who understand system mechanics.
The System: How to Use These Prompts
Run Step 1 → Step 5 sequentially for comprehensive execution, or rerun individual steps to iterate, scale, or pivot. Outputs from earlier steps become inputs for later steps. This mirrors how elite operators actually work: research → exploit → build → distribute → monetize → reinforce.
Each prompt is designed to be pasted directly into Claude. Replace the {$VARIABLES} with your specific niche, findings, or outputs from previous steps.
STEP 1: Environment & Opportunity Recon (Research Engine)
Why Research Before Building
Understand how AI search and competitors behave before deciding what to build. This step maps LLM retrieval patterns, citation biases, and structural weaknesses in your target niche.
When to Run Competitive Recon
- Entering a new niche
- Evaluating monetization opportunities
- Before building any content assets
- When competitor strategies appear stale
LLM Citation Behavior Patterns
LLMs cite sources based on retrieval probability, not quality. Perplexity cites 2-3x more domains than ChatGPT or Gemini, but parametric models show 42% citation overlap—the highest pairwise similarity. This means established domains with historical content dominate parametric citations while fresh, structured content captures RAG citations.