SkillBrowser: Selective Skill Exposure for Scalable Skill-Using Agents· SkillBrowser：选择性技能暴露
Large language model agents increasingly rely on skills to extend their capabilities in complex tasks. However, scalable skill use requires Selective Skill Exposure (SSE): providing relevant procedural content when needed while avoiding unnecessary exposure that may disrupt reasoning. Existing paradigms fall short in complementary ways. Progressive disclosure exposes metadata for a growing skill library, introducing context pollution; retrieval limits catalog exposure, but static retrieval from the initial task query may be misaligned with capability needs that emerge during reasoning and may still inject unnecessary skills. We propose SkillBrowser, a Meta-Skill-managed framework that enables an agent to identify current capability needs, formulate targeted queries, and selectively load sk
提出SkillBrowser框架，实现代理在大规模技能库中按需选择性加载技能。
- 核心方法
- SkillBrowser通过元技能管理，使代理能够识别当前能力需求，形成有针对性的查询，并选择性地加载所需技能。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有技能使用方法存在上下文污染或技能需求错位问题，不能有效支持代理在复杂任务中的可扩展技能使用。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种新的框架来优化代理技能加载过程，减少上下文污染和技能需求错位，提高技能使用的效率和效果。
- 意义与局限
- SkillBrowser有助于提升代理在执行复杂任务时的灵活性和智能化水平，但它需要进一步的实验验证以证明其有效性和可靠性。