I do this all the time in my Claude code workflow: - Claude will stumble a few times before figuring out how to do part of a complex task - I will ask it to explain what it was trying to do, how it eventually solved it, and what was missing from its environment. - Trivial pointers go into the CLAUDE.md. Complex tasks go into a new project skill or a helper script

This is the best way to re-enforce a copilot because models are pretty smart most of the time and I can correct the cases where it stumbles with minimal cognitive effort. Over time I find more and more tasks are solved by agent intelligence or these happy path hints. As primitive as it is, CLAUDE.md is the best we have for long-term adaptive memory.