SkillFlow: Scalable and Efficient Agent Skill Retrieval System

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 SKILL.md 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: on SkillsBench, SkillFlow-retrieved skills raise Pass@1 from 9.2% to 16.4% (+78.3%, $p_{adj} = 3.64 × 10^{−2}$), reaching 84.1% of the oracle ceiling (oracle skill injection), while on Terminal-Bench, agents readily adopt retrieved skills (70.1% use rate) yet show no performance gain, revealing that retrieval alone is insufficient when the corpus lacks high-quality, executable skills for the target domain. SkillFlow demonstrates that framing skill acquisition as information retrieval is effective, and that the practical impact of skill-augmented agents hinges on corpus coverage and skill quality—particularly the density of runnable code and bundled artifacts.