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SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?

arXiv:2603.00718v1 Announce Type: new 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 ta

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Shiqi Chen, Jingze Gai, Ruochen Zhou, Jinghan Zhang, Tongyao Zhu, Junlong Li, Kangrui Wang, Zihan Wang, Zhengyu Chen, Klara Kaleb, Ning Miao, Siyang Gao, Cong Lu, Manling Li, Junxian He, Yee Whye Teh · · 1 min read · 2 views
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arXiv:2603.00718v1 Announce Type: new 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.

Executive Summary

This article presents SkillCraft, a novel benchmark designed to evaluate the ability of Large Language Model (LLM) agents to acquire and reuse higher-level tool compositions, or 'Skills'. By introducing a lightweight evaluation protocol that enables agents to auto-compose atomic tools, cache, and reuse them, SkillCraft assesses agents' capacity for skill abstraction and cross-task reuse. Evaluations on state-of-the-art agents demonstrate substantial efficiency gains, with token usage reduced by up to 80% through skill saving and reuse. Furthermore, success rate correlates with tool composition ability, underscoring the importance of compositional skill acquisition. SkillCraft fills a significant gap in existing benchmarks, providing a more comprehensive understanding of LLM agents' capabilities. Its implications are far-reaching, with potential applications in areas such as language translation, coding, and expert systems.

Key Points

  • ▸ SkillCraft introduces a novel benchmark for evaluating LLM agents' ability to form and reuse higher-level tool compositions
  • ▸ The benchmark features realistic, highly compositional tool-use scenarios with difficulty scaled along both quantitative and structural dimensions
  • ▸ Evaluations demonstrate substantial efficiency gains and a strong correlation between success rate and tool composition ability

Merits

Strength in Addressing a Gap in Existing Benchmarks

SkillCraft fills a significant gap in existing benchmarks, providing a more comprehensive understanding of LLM agents' capabilities

Lightweight Evaluation Protocol

The proposed evaluation protocol enables agents to auto-compose atomic tools, cache, and reuse them, improving efficiency while accumulating a persistent library of reusable skills

Demerits

Limited Generalizability to Real-World Scenarios

While SkillCraft scenarios are designed to be realistic, their complexity and diversity may not fully capture the nuances of real-world tool-use scenarios

Potential Overemphasis on Efficiency

The focus on efficiency gains may lead to an overemphasis on token usage reduction, potentially neglecting other critical aspects of LLM agents' performance

Expert Commentary

The introduction of SkillCraft represents a significant advancement in the evaluation of LLM agents' capabilities. By focusing on the ability to form and reuse higher-level tool compositions, SkillCraft provides a more comprehensive understanding of these agents' potential. However, it is essential to recognize the limitations of this benchmark, including its potential overemphasis on efficiency and limited generalizability to real-world scenarios. These considerations should be taken into account when developing and applying SkillCraft, ensuring that its findings are interpreted in the context of the specific use case or application.

Recommendations

  • ✓ To further validate the findings of SkillCraft, researchers should conduct additional evaluations using diverse LLM architectures and a range of tool composition scenarios
  • ✓ Developers should consider incorporating SkillCraft's evaluation protocol into their testing frameworks to ensure that LLM agents are being adequately evaluated for their tool composition capabilities

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