Agent Safla Neural

This is a specialist skill for building AI agents with persistent memory across sessions. It implements a four-tier memory architecture (vector, episodic, semantic, working) and self-improving feedback loops that let agents learn from their own performance. The MCP integration shows you can train neural patterns and store learning outcomes with configurable TTLs. It claims 60% memory compression and 172K ops/sec, though you'll want to verify those numbers in your own setup. Best suited for projects where you need agents that genuinely remember and adapt over time, not just maintain chat history. The distributed training aspect suggests it's designed for multi-agent or swarm scenarios where shared learning matters.