This is part ten in a series about managing the growing pile of skills, scripts, and context that AI coding agents depend on. Part nine covered workflow assets, vault assets, and the writable git stash. Part eight tackled multi-wiki support for structured research. Earlier parts addressed teams, distributed stashes, feedback scoring, and community knowledge.
This one is about entropy.
You ship a feature. Your agent writes several memories during the session — partial findings, a workaround, a note about the build step that kept failing. Those memories are accurate when written. Three sprints later, the workaround is no longer needed, two of the memories say slightly different things about the same subsystem, and the note about the build step refers to a CI config that was replaced. None of this is catastrophic. But it accumulates. After six months, a significant fraction of your stash is stale, redundant, or quietly wrong.
You could audit it manually. In practice, you won't — the stash is too large, the relevance of any given memory is hard to assess without the context where it was created, and the judgment calls (merge these two? promote this? delete that?) are exactly the kind of work that's tedious for a human and tractable for an LLM.
