1. Embeds the current request.
2. Runs a semantic + timestamp-weighted search over your past sessions. Returns only the top N items that look relevant to this request.
3. Those get injected into the prompt as context (like extra system/user messages), so Claude sees just enough to stay oriented without blowing context limits.
Think of it like: Attention over your historical work, more so than brute force recall. Context on demand basically giving you an infinite context window. Bookmark + semantic grep + temporal rank. It doesn’t “know everything all the time.” It just knows how to ask its own past: “What from memory might matter for this?”
When you try it, I’d love to hear where the mechanism breaks for you.
Then Claude uses the MCP tools according to the SKILL definition: https://github.com/mutable-state-inc/ensue-skill/blob/main/s...
I think of it like a file tree with proper namespacing and keep abstract concepts in separate directories. so like my food preferences will be in like /preferences/sandos. or you can even do things like /system-design preferences and then load them into a relevant conversation for next time.
I'm sold.
With that said, I can't think of a way that this would work. How does this work? I took a very quick glance, and it's not obvious at first glance.
The whole problem is, the AI is short on context, it has limited memory. Of course, you can store lots of memory elsewhere, but how do you solve the problem of having the AI not know what's in the memory as it goes from step to step? How does it sort of find the relevant memory at the time that that relevance is most active?
Could you just walk through the sort of conceptual mechanism of action of this thing?