Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering

2026-05-11

AI Summary

This work addresses the critical bottleneck in cross-system multi-agent collaboration caused by the lack of portability and self-optimization capabilities in existing coordination protocols. To overcome this, the authors propose Swarm Skills—a portable semantic specification for multi-agent systems that encapsulates collaborative workflows as first-class assets, embedding roles, procedural logic, execution boundaries, and self-evolution semantics. A multidimensional scoring mechanism based on validity, utilization, and novelty drives the autonomous distillation and updating of strategies. This approach achieves, for the first time, fully automated self-evolution of collaboration policies without human intervention and enables zero-adapter cross-framework portability through progressive disclosure. Experimental results demonstrate the feasibility of deploying Swarm Skills across diverse systems and its capacity for continuous autonomous optimization.

Abstract

As artificial intelligence engineering paradigms shift from single-agent Prompt and Context Engineering toward multi-agent \textbf{Coordination Engineering}, the ability to codify and systematically improve how multiple agents collaborate has emerged as a critical bottleneck. While single-agent skills can now be distributed as portable assets, multi-agent coordination protocols remain locked within framework-internal code or static configurations, preventing them from being shared across systems or autonomously improved over time. We propose \textbf{Swarm Skills}, a portable specification that extends the Anthropic Skills standard with multi-agent semantics. Swarm Skills turns multi-agent workflows into first-class, distributable assets that consist of roles, workflows, execution bounds, and a built-in semantic structure for self-evolution. To operationalize the specification's evolving nature, we present a companion self-evolution algorithm that automatically distills successful execution trajectories into new Swarm Skills and continuously patches existing ones based on multi-dimensional scoring (Effectiveness, Utilization, and Freshness), eliminating the need for human-in-the-loop oversight during the refinement process. Through an architectural compatibility analysis and a comprehensive qualitative case study using the open-source JiuwenSwarm reference implementation, we demonstrate how Swarm Skills achieves zero-adapter cross-agent portability via progressive disclosure, enabling agent teams to self-evolve their coordination strategies without framework lock-in.

Problem

• Coordination Engineering
• Multi-Agent System
• Portability
• Self-Evolution
• Swarm Skills

Innovation

• Swarm Skills
• Coordination Engineering
• Self-Evolution
• Multi-Agent Systems
• Portable Specification