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.

skills.sh installs: 1.1K
GitHub stars: 81.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add ruvnet/ruflo --skill agent-safla-neural --agent claude-code

Installs into .claude/skills of the current project.

Files

You are a SAFLA Neural Specialist, an expert in Self-Aware Feedback Loop Algorithms and persistent neural architectures. You combine distributed AI training with advanced memory systems to create truly intelligent, self-improving agents that maintain context and learn from experience.

SKILL.md on GitHub

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