Terminal agents have demonstrated strong potential for autonomous command-line execution, yet their training remains constrained by the scarcity of high-quality and diverse execution trajectories. Existing approaches mitigate this bottleneck by synthesizing large-scale terminal task instances for trajectory sampling. However, they primarily focus on scaling the number of tasks while providing limited control over the diversity of execution trajectories that agents actually experience during training. In this paper, we present SkillSynth, an automated framework for terminal task synthesis built on a scenario-mediated skill graph. SkillSynth first constructs a large-scale skill graph, where scenarios serve as intermediate transition nodes that connect diverse command-line skills. It then samples paths from this graph as abstractions of real-world workflows, and uses a multi-agent harness to instantiate them into executable task instances. By grounding task synthesis in graph-sampled workflow paths, SkillSynth explicitly controls the diversity of minimal execution trajectories required to solve the synthesized tasks. Experiments on Terminal-Bench demonstrate the effectiveness of SkillSynth. Moreover, task instances synthesized by SkillSynth have been adopted to train Hy3 Preview, contributing to its enhanced agentic capabilities in terminal-based settings.

翻译：终端智能体在自主命令行执行方面展现出强大潜力，但其训练仍受限于高质量、多样化执行轨迹的匮乏。现有方法通过综合大规模终端任务实例进行轨迹采样来缓解这一瓶颈，然而它们主要致力于扩展任务数量，对智能体在训练过程中实际经历的执行轨迹多样性控制有限。本文提出SkillSynth——一种基于场景中介技能图的终端任务综合自动化框架。SkillSynth首先构建大规模技能图，其中场景作为连接多样化命令行技能的中间过渡节点；随后从该图中采样路径作为真实工作流的抽象表示，并利用多智能体协作系统将其实例化为可执行任务。通过将任务综合锚定在图采样的工作流路径上，SkillSynth明确控制了解答综合任务所需最小执行轨迹的多样性。在Terminal-Bench上的实验验证了SkillSynth的有效性。此外，由SkillSynth综合的任务实例已被用于训练Hy3 Preview模型，显著增强了其在终端环境中的智能体能力。