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SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?

arXiv cs.CL / 3/11/2026

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Key Points

  • SkillCraft is a newly introduced benchmark designed to evaluate LLM agents' abilities to form and reuse higher-level tool compositions called Skills, beyond just atomic tool invocation.
  • The benchmark features realistic, highly compositional tool-use scenarios with difficulty scaled both quantitatively and structurally to test skill abstraction and cross-task skill reuse.
  • A lightweight evaluation protocol allows agents to auto-compose atomic tools into executable Skills, cache, and reuse them efficiently across tasks, significantly improving efficiency.
  • State-of-the-art agents evaluated on SkillCraft achieved up to 80% reduction in token usage by skill saving and reuse, showing strong correlation between tool composition ability and task success.
  • SkillCraft highlights compositional skill acquisition as a core capability for real-world tool-using AI agents operating over complex, long-horizon workflows.

Computer Science > Computation and Language

arXiv:2603.00718 (cs)
[Submitted on 28 Feb 2026 (v1), last revised 10 Mar 2026 (this version, v2)]

Title:SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?

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Abstract:Real-world tool-using agents operate over long-horizon workflows with recurring structure and diverse demands, where effective behavior requires not only invoking atomic tools but also abstracting, and reusing higher-level tool compositions. However, existing benchmarks mainly measure instance-level success under static tool sets, offering limited insight into agents' ability to acquire such reusable skills. We address this gap by introducing SkillCraft, a benchmark explicitly stress-test agent ability to form and reuse higher-level tool compositions, where we call Skills. SkillCraft features realistic, highly compositional tool-use scenarios with difficulty scaled along both quantitative and structural dimensions, designed to elicit skill abstraction and cross-task reuse. We further propose a lightweight evaluation protocol that enables agents to auto-compose atomic tools into executable Skills, cache and reuse them inside and across tasks, thereby improving efficiency while accumulating a persistent library of reusable skills. Evaluating state-of-the-art agents on SkillCraft, we observe substantial efficiency gains, with token usage reduced by up to 80% by skill saving and reuse. Moreover, success rate strongly correlates with tool composition ability at test time, underscoring compositional skill acquisition as a core capability.
Comments:
Subjects: Computation and Language (cs.CL); Software Engineering (cs.SE)
Cite as: arXiv:2603.00718 [cs.CL]
  (or arXiv:2603.00718v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2603.00718
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arXiv-issued DOI via DataCite

Submission history

From: Jingze Gai [view email]
[v1] Sat, 28 Feb 2026 15:44:31 UTC (4,186 KB)
[v2] Tue, 10 Mar 2026 08:57:18 UTC (4,186 KB)
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