SkillBrowser: Selective Skill Exposure for Scalable Skill-Using Agents
We propose SkillBrowser, a Meta-Skill-managed framework that enables an agent to identify current capability needs, formulate targeted queries, and selectively load skills on demand.
ProofPaper ↗
Key points
- 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.
- 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.
- These results demonstrate the viability of Meta-Skill-managed SSE for scalable skill repositories.
Sources (1)
- [1]SkillBrowser: Selective Skill Exposure for Scalable Skill-Using AgentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 02:44 PM
We propose SkillBrowser, a Meta-Skill-managed framework that enables an agent to identify current capability needs, formulate targeted queries, and selectively load skills on demand.
Large language model agents increasingly rely on skills to extend their capabilities in complex tasks.
Extractive summary: sentences quoted from the sources.
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