Large Language Model agents demonstrate potential in solving real-world problems via tools, yet generalist intelligence is bottlenecked by scarce high-quality, long-horizon data. Existing methods collect privacy-constrained API logs or generate scripted interactions lacking diversity, which struggle to produce data requisite for scaling capabilities. We propose AgentSkiller, a fully automated framework synthesizing multi-turn interaction data across realistic, semantically linked domains. It employs a DAG-based architecture with explicit state transitions to ensure determinism and recoverability. The pipeline builds a domain ontology and Person-Centric Entity Graph, defines tool interfaces via Service Blueprints for Model Context Protocol servers, and populates environments with consistent databases and strict Domain Policies. A cross-domain fusion mechanism links services to simulate complex tasks. Finally, the pipeline creates user tasks by verifying solution paths, filtering via execution-based validation, and generating queries using a Persona-based Simulator for automated rollout. This produces reliable environments with clear state changes. To demonstrate effectiveness, we synthesized $\approx$ 11K interaction samples; experimental results indicate that models trained on this dataset achieve significant improvements on function calling over baselines, particularly in larger parameter regimes.

翻译：大型语言模型智能体通过工具展现出解决现实世界问题的潜力，但通用智能的瓶颈在于缺乏高质量、长视野的数据。现有方法收集受隐私约束的API日志或生成缺乏多样性的脚本化交互，难以产生扩展能力所需的数据。我们提出了AgentSkiller，一个完全自动化的框架，用于合成跨现实、语义关联领域的多轮交互数据。它采用基于有向无环图的架构，具有显式的状态转换，以确保确定性和可恢复性。该流水线构建领域本体和以人为中心的实体图，通过面向模型上下文协议服务器的服务蓝图定义工具接口，并用一致的数据库和严格的领域策略填充环境。跨领域融合机制通过链接服务来模拟复杂任务。最后，流水线通过验证解决方案路径、基于执行的验证进行过滤，并使用基于人物角色的模拟器生成查询以实现自动化部署，从而创建用户任务。这产生了具有清晰状态变化的可靠环境。为验证有效性，我们合成了约11K个交互样本；实验结果表明，在此数据集上训练的模型在函数调用方面相比基线取得了显著提升，尤其是在更大参数规模下。