When MaxHermes completes a complex task, it evaluates its own approach and writes a new permanent skill file. Those skills accumulate. Subsequent similar tasks invoke the stored skill instead of reconstructing the workflow from the base model.
MiniMax documentation mentions a four-layer memory architecture with persistent memory, session archive, skill files, and user modeling. Only the skill files are relevant here and they are opaque. This is not a criticism of the architecture. Single-agent self-evolution sidesteps the inter-agent handoff precision loss that multi-agent use cases document repeatedly. That is a legitimate advantage.
Of course it saves you the time you would have spent writing out your skills, but more importantly it embodies a mindset of self-improvement which I believe is even more valuable.
In your work, what does a function like this saves?