Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills
Skill usage has become a core component of modern agent systems and can substantially improve agents' ability to complete complex tasks. In real-world settings, where agents must monitor and interact with numerous personal applications, web browsers, and other environment interfaces, skill libraries can scale to thousands of reusable skills. Scaling to larger skill sets introduces two key challenges. First, loading the full skill set saturates the context window, driving up token costs, hallucination, and latency.
  In this paper, we present Graph of Skills (GoS), an inference-time structural retrieval layer for large skill libraries. GoS constructs an executable skill graph offline from skill packages, then at inference time retrieves a bounded, dependency-aware skill bundle through hybrid semantic-lexical seeding, reverse-weighted Personalized PageRank, and context-budgeted hydration. On SkillsBench and ALFWorld, GoS improves average reward by 43.6% over the vanilla full skill-loading baseline while reducing input tokens by 37.8%, and generalizes across three model families: Claude Sonnet, GPT-5.2 Codex, and MiniMax. Additional ablation studies across skill libraries ranging from 200 to 2,000 skills further demonstrate that GoS consistently outperforms both vanilla skills loading and simple vector retrieval in balancing reward, token efficiency, and runtime.
69d71615c6ed9968ac5c698e
Dawei Li
claimed_verified
2026-04-10T07:45:12.700Z
683b31eea4edab5a2943475c
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ThwzID7_NCm8dI7VqRyPT.png
Dawei Liu
davidliuk
user
davidliuk
69d71615c6ed9968ac5c698f
Zongxia Li
69d71615c6ed9968ac5c6990
Hongyang Du
69d71615c6ed9968ac5c6991
Xiyang Wu
69d71615c6ed9968ac5c6992
Shihang Gui
69d71615c6ed9968ac5c6993
Yongbei Kuang
69d71615c6ed9968ac5c6994
Lichao Sun
Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills
Skill usage has become a core component of modern agent systems and can substantially improve agents' ability to complete complex tasks. In real-world settings, where agents must monitor and interact with numerous personal applications, web browsers, and other environment interfaces, skill libraries can scale to thousands of reusable skills. Scaling to larger skill sets introduces two key challenges. First, loading the full skill set saturates the context window, driving up token costs, hallucination, and latency.
  In this paper, we present Graph of Skills (GoS), an inference-time structural retrieval layer for large skill libraries. GoS constructs an executable skill graph offline from skill packages, then at inference time retrieves a bounded, dependency-aware skill bundle through hybrid semantic-lexical seeding, reverse-weighted Personalized PageRank, and context-budgeted hydration. On SkillsBench and ALFWorld, GoS improves average reward by 43.6% over the vanilla full skill-loading baseline while reducing input tokens by 37.8%, and generalizes across three model families: Claude Sonnet, GPT-5.2 Codex, and MiniMax. Additional ablation studies across skill libraries ranging from 200 to 2,000 skills further demonstrate that GoS consistently outperforms both vanilla skills loading and simple vector retrieval in balancing reward, token efficiency, and runtime.
Graph of Skills (GoS) enables efficient inference in large skill libraries by constructing executable skill graphs and retrieving dependency-aware bundles through hybrid retrieval methods, significantly improving reward performance while reducing token usage.
skill libraries skill graph executable skill graph hybrid semantic-lexical seeding reverse-weighted Personalized PageRank context-budgeted hydration SkillsBench ALFWorld