SkillGenBench: Benchmarking Skill Generation Pipelines for LLM Agents

Problem
This paper addresses a significant gap in the evaluation of skill generation capabilities for large language model (LLM) agents. While existing benchmarks focus on the efficacy of provided skills or the ability of agents to perform downstream tasks using raw context, they do not specifically isolate the skill generation process. The authors introduce SkillGenBench, a benchmark designed to evaluate the generation of correct, reusable, and executable skills from various sources, including repositories and documents. This work is presented as a preprint and has not yet undergone peer review.

Method
SkillGenBench establishes a unified and controlled protocol for benchmarking skill generation pipelines. The benchmark encompasses two primary generation regimes:

1. Task-conditioned generation: Skills are synthesized based on specific tasks revealed during the process.
2. Task-agnostic generation: A reusable skill library is distilled without prior knowledge of downstream tasks.

The benchmark evaluates two procedural sources:

• Repository-grounded instances: Skills are generated from distributed code, configuration, and scripts.
• Document-grounded instances: Skills are derived from long-form text, requiring the distillation of procedures and constraints.

The authors provide standardized task specifications, pinned environments, and evaluation protocols that focus on deterministic execution-based checks. Auxiliary signals are included for diagnostic purposes. The experiments conducted utilize various skill-generation methods and backbones, revealing substantial performance variation and distinct failure modes based on the source of the skills.

Results
The authors report significant performance discrepancies across different skill-generation methods, highlighting the challenges associated with reusable skill distillation. While specific numerical results are not disclosed in the abstract, the paper emphasizes that the evaluation reveals distinct failure modes when generating skills from software repositories compared to long-form documents. This indicates that the choice of source material has a critical impact on the effectiveness of skill generation.

Limitations
The authors acknowledge that the benchmark may not cover all possible contexts in which skill generation could occur, potentially limiting its applicability. Additionally, the reliance on deterministic execution-based checks may not capture the full spectrum of skill performance in dynamic environments. The paper does not address the scalability of the benchmark to larger datasets or more complex skill generation tasks, which could be a significant limitation for future research.

Why it matters
SkillGenBench provides a reproducible testbed for studying skill generation as an independent research problem within agent systems. By isolating skill generation from downstream task performance, this benchmark enables researchers to better understand the intricacies of skill creation and its implications for LLM agents. The findings from this work could inform the development of more robust and versatile LLM agents capable of generating reusable skills, ultimately enhancing their applicability across various domains.

Authors: Yifan Zhou, Zhentao Zhang, Ziming Cheng, Shuo Zhang, Qizhen Lan, Zhangquan Chen, Zhi Yang, Qianyu Xu et al.
Source: arXiv:2605.18693
URL: https://arxiv.org/abs/2605.18693v1