AI Coding Agents Produce Generic Boilerplate With No Access to Real-World Domain Learnings

Summary

When founders ask AI agents (Cursor, Claude Code, Codex) to architect a product or plan market entry, the agents have zero access to real-world ecosystem learnings and instead emit generic, theoretical boilerplate—causing founders to waste days reinventing already-solved wheels. There is no built-in, accessible way to inject curated domain knowledge (proven GTM playbooks, prior implementations, audience bottlenecks, monetization/unit economics) into the agent's context. The only workaround is manually building a bespoke RAG/knowledge base, which is out of reach for most solo builders. This gap between what agents can produce and what founders actually need is severe and largely unsolved, and it is the exact opening that an installable domain skill (like a GTM coach skill) is meant to fill.