[2504.06188] SkillFlow: Scalable and Efficient Agent Skill Retrieval System

Computer Science > Artificial Intelligence arXiv:2504.06188 (cs) [Submitted on 8 Apr 2025 (v1), last revised 27 Mar 2026 (this version, v2)] Title:SkillFlow: Scalable and Efficient Agent Skill Retrieval System Authors:Fangzhou Li, Pagkratios Tagkopoulos, Ilias Tagkopoulos View a PDF of the paper titled SkillFlow: Scalable and Efficient Agent Skill Retrieval System, by Fangzhou Li and 2 other authors View PDF HTML (experimental) Abstract:AI agents can extend their capabilities at inference time by loading reusable skills into context, yet equipping an agent with too many skills, particularly irrelevant ones, degrades performance. As community-driven skill repositories grow, agents need a way to selectively retrieve only the most relevant skills from a large library. We present SkillFlow, the first multi-stage retrieval pipeline designed for agent skill discovery, framing skill acquisition as an information retrieval problem over a corpus of ~36K community-contributed this http URL definitions indexed from GitHub. The pipeline progressively narrows a large candidate set through four stages: dense retrieval, two rounds of cross-encoder reranking, and LLM-based selection, balancing recall and precision at each stage. We evaluate SkillFlow on two coding benchmarks: SkillsBench, a benchmark of 87 tasks and 229 matched skills; and Terminal-Bench, a benchmark that provides only 89 tasks, and no matched skills. On SkillsBench, SkillFlow-retrieved skills raise Pass@1 from 9.2% to 16...