做智能体开发或研究自主系统的团队，MUSE-Autoskill 给出了一个可落地的技能管理闭环——从创建到评估再到跨任务复用，值得直接参考其设计思路。
MUSE-Autoskill 提出了一种以技能为中心的智能体框架，让LLM智能体能够持续创建、记忆、管理和评估技能，实现自我进化。传统方法将技能视为孤立静态的产物，而该框架通过统一的技能生命周期（创建、记忆、管理、评估、优化）显著提升了技能的可复用性和可靠性。框架引入了技能级记忆，让每个技能跨任务积累经验，从而更有效地适应新场景。在SkillsBench上的实验表明，这种生命周期管理的技能能提高任务成功率、效率、复用率，甚至支持跨智能体迁移。这项工作对构建长期自主进化的AI智能体系统具有重要参考价值。
MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and long-term improvement. We propose MUSE-Autoskill Agent (Memory-Utilizing Skill Evolution), a skill-centric agent framework that lets agents continuously improve their task-solving capability by creating, reusing, and refining skills under a unified lifecycle (creation, memory, management, evaluation, and refinement). Our framework enables agents to create skills on demand, store and reuse them across tasks, organize and select them efficiently, and evaluate them through unit tests and runtime feedback for continuous refinement. We further introduce skill-level memory that accumulates experience for each skill across tasks, enabling more effective reuse and adaptation over time. Experiments on SkillsBench provide initial evidence that lifecycle-managed skills can improve task success, efficiency, reuse, and cross-agent transfer, highlighting the importance of treating skills as long-lived, experience-aware, and testable assets.