◇ Could this help me?
Yes. The insight is that a growing collection of skills, memories, and instructions is not free: overlapping and outdated context competes with the relevant context, so a setup that only ever accumulates gets worse over time even though every individual addition looked like an improvement. Pairing a capture step with a retirement step is the right structural answer, and running the capture after a long conversation is well timed, since that is exactly when the reusable pattern is visible and about to be forgotten. General practice, with one adjustment: have the pruning step produce a keep, compact, or delete recommendation for you to approve rather than acting on its own, because deciding something is stale is a judgment call and the cost of being wrong is losing something you cannot easily rebuild.
Build me two complementary routines for managing my own AI setup. The first runs after a long working session: review what we did, and identify anything worth keeping, separating durable facts that belong in memory from repeated procedures that would be better packaged as a reusable skill. Draft each one rather than just naming it. The second runs periodically: audit my accumulated memories, skills, and instruction files, find things that are stale, duplicated, or contradict each other, and produce a keep, compact, or delete recommendation for each with your reasoning. The second routine must never delete anything itself, only recommend, and it should flag overlaps between items even when both are still accurate.