Generative Skill Composition for LLM Agents
Score
59Why selected
The paper formalizes an underexplored crucial problem—structured skill composition, and proposes a simple yet effective method (SkillComposer) that achieves significant and consistent gains across advanced models, approaching the gold-skill upper bound at lower cost, with valuable implications for agent system design.
AI commentary
「This work addresses the structural bottleneck in skill composition for LLM agents by unifying subset, count, and order prediction into an end-to-end sequence generation, substantially improving task success while reducing prompt cost, offering a new paradigm for building reusable and scalable skill systems.」
Quick overview
LLM agents benefit from skill libraries for complex tasks, but selecting the right skill composition—subset, count, and order—remains a bottleneck. Existing methods either expose all skills or use retrieval, missing the structural nature. This paper formalizes structured skill composition and proposes SkillComposer, a constrained autoregressive decoder that jointly predicts the skill plan. Using a human-curated skill library for training, SkillComposer outperforms retrieval baselines in composition quality and downstream task success, matching the gold-skill upper bound with lower prompt-token cost.
- Formalizes structured skill composition: jointly predict subset, count, and order given a task and skill library
- Proposes SkillComposer, a constrained autoregressive decoder that generates skill plans in a single pass, capturing dependencies between consecutive skills
- Builds training set from a real human-curated skill library; evaluations show significant improvements in composition quality and downstream task success
- On GPT-5.2-Codex and Gemini-3-Pro-Preview, SkillComposer boosts pass rates by +23.1 and +18.2 percentage points, matching gold-skill upper bound with lower prompt token cost
Full text
Computer Science > Computation and Language
arXiv:2606.32025v1 (cs) [Submitted on 30 Jun 2026]Title:Generative Skill Composition for LLM Agents
Authors:Xinyu Zhao, Zhen Tan, Vaishnav Tadiparthi, Nakul Agarwal, Kwonjoon Lee, Ehsan Moradi Pari, Hossein Nourkhiz Mahjoub, Tianlong Chen View a PDF of the paper titled Generative Skill Composition for LLM Agents, by Xinyu Zhao and 7 other authors View PDF HTML (experimental)Abstract:Recent LLM agents benefit from skills for solving complex tasks. Skills encapsulate modular packages of procedural knowledge and instructions for performing specialized tasks, such as setting up a sandboxed environment, running a test suite, or refactoring a function across multiple files. As skill libraries grow and become reusable across tasks and domains, selecting an appropriate skill composition has emerged as a central bottleneck. Existing approaches fall into two categories. One exposes the agent's reasoning to the entire skill collection; the other performs skill retrieval via embeddings or LLM-based rerankers. Both provide useful insights; however, they miss the structural nature of skill composition, which is a joint decision over which skills, how many, and in what order -- three dimensions that cannot be decoupled. We formalize this as structured skill composition: given a task and a skill library, predict an executable skill plan that jointly specifies the activated subset, count, and execution order. We propose SkillComposer, which instantiates structured skill composition as task-conditioned skill sequence prediction. SkillComposer uses a constrained autoregressive decoder over skill identifiers, so subset, count, and order emerge jointly from a single decoding pass, and dependencies between successive skills are captured naturally. We build a training set of task-composition pairs from a real, human-curated skill library. We then evaluate SkillComposer along two axes: composition quality on a held-out test set, and downstream task success on SkillsBench across two production-grade coding agents. On GPT-5.2-Codex, Gemini-3-Pro-Preview, SkillComposer raises the pass rate by +23.1, +18.2pp over the no-skill baseline, surpassing top-3 retrieval and matching the gold-skill retrieval upper bound at lower prompt-token cost.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.32025 [cs.CL] |
| (or arXiv:2606.32025v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.32025 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Xinyu Zhao [view email][v1] Tue, 30 Jun 2026 17:53:09 UTC (666 KB)
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