the self-extending skills part is really interesting. ive been building AI agents with persistent memory for a while now and the skill/tool extensibility piece is where most frameworks fall short. they either give you a rigid plugin system or completley open-ended function calling with no guardrails.

how are you handling the trust boundary for self-created skills? thats usually where things get tricky.

also curious about the memory architecture. file-based memory (like markdown files the agent reads/writes) has been surprisingly effective in my experience compared to fancy vector DB approaches. simpler to debug, easier for the agent to reason about, and way less infrastructure overhead. whats your approach?