arXiv AI
SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents
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arXiv AI
Sep 28
SkillFlow: Scalable and Efficient Agent Skill Retrieval System
SkillFlow is an open, multi-stage retrieval system that helps AI agents selectively load relevant skills from a large library of community-contributed SKILL.md definitions. The pipeline uses dense retrieval, two rounds of cross-encoder reranking, and LLM-based selection to balance recall and precision. Evaluations on SkillsBench and Terminal-Bench show that SkillFlow improves performance when high-quality skills are available, but retrieval alone does not help if the corpus lacks executable skills for the target domain.
arXiv AI
Jul 22
SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval
arXiv:2607. 18785v1 Announce Type: new Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.
arXiv AI
1d ago
SkillBrowser: Selective Skill Exposure for Scalable Skill-Using Agents
SkillBrowser is a Meta‑Skill‑managed framework that allows large language model agents to identify their current capability needs, formulate targeted queries, and selectively load skills on demand. By providing relevant procedural content only when needed, it reduces context pollution and misaligned skill exposure that can disrupt reasoning. Experiments on six SRA‑Bench subsets and five LLMs show that SkillBrowser improves aggregate accuracy over static retrieval and LLM‑based selection, especially for multi‑step tool use, while mitigating accuracy degradation on already‑solvable instances.
arXiv AI
Jun 3
SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale
arXiv:2606. 03056v1 Announce Type: new Abstract: As LLM agents adopt large skill libraries, selecting the right subset becomes a structural problem rather than a similarity-matching one: skills depend on, conflict with, specialize, or duplicate one another, a structure invisible to both full enumeration and embedding similarity.
Hugging Face Trending Papers
Aug 9
SkillReason: Reasoning-Enhanced Agent Skill Retrieval for Implicit User Requests
Large language model agents increasingly rely on reusable skills to extend their capabilities beyond parametric knowl- edge. However, retrieving the appropriate skill from a large- scale library remains challenging because realistic user re- quests are often concise and underspecified, stating only the task goal while leaving the required capabilities and execu- tion steps implicit.
arXiv AI
Jul 31
SkillSight: Calibrating Generic Content Bias for Skill Retrieval
arXiv:2607. 18785v2 Announce Type: replace Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.