That's a great point — asking focused questions definitely gets better results than dumping generalized knowledge. I think both approaches can complement each other. Where I see book-based skills adding value is in the iterative review loop: you let the LLM review your code against well-known principles (like Clean Code or DDIA patterns), it flags issues and suggests improvements, and you apply them repeatedly. Over multiple passes, the code quality compounds significantly. So it's less about feeding the LLM static rules and more about giving it a structured lens to evaluate through. The LLM still does the thinking — the books just sharpen its focus. That said, I'm still figuring out how to run this evaluation properly. A colleague of mine has been experimenting with spinning up sub-agents to review the outputs of the main LLM flow — essentially an automated review layer. That might be the right pattern: one agent creates, another evaluates against known principles. Curious if anyone else has tried something similar.