This is part eight in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part one introduced progressive disclosure. Part two unified your local assets across platforms. Part three added persistent memory. Previous parts addressed teams, distributed stashes, and community knowledge. This one is about a different problem: knowledge accumulation. You start a new research area — say, LLM inference optimization. You read papers. You take notes. You save PDFs. Your agent writes summaries, discovers connections, asks follow-up questions that lead to more notes. Six weeks later you have a pile of markdown files, half-digested papers in a downloads folder, and a memory that says "we covered KV cache quantization somewhere." You search for it. You get three files with different partial takes on the same topic. None of them have the answer you need. This isn't a storage problem. You have all the information.…