SKILL2TASK: SELF-EVOLVING AGENTIC TASK SYNTHESIS VIA SKILL GRAPH AND PROGRESSIVE VALIDATION

Tool-using language agents are increasingly expected to complete long-horizon workflows in realistic computing environments, and training them at scale requires a large and diverse supply of executable, verifiable tasks. Yet existing pipelines generate this data with a fixed, one-shot synthesizer built from a hand-designed harness that cannot keep improving from the failures surfaced during construction. We present Skill2Task, a self-evolving framework that turns reusable community skills into executable general-agent tasks and makes the synthesis harness itself the object of optimization. It composes skills through a skill-state graph, progressively validates each environment, instruction, and verifier before use, and feeds construction and rollout diagnostics back to revise the harness each round, which raises the success rate and efficiency of task construction over successive rounds. This loop yields 10,916 verifiable tasks and 10,880 curated trajectories, and supervised fine-tuning on them consistently improves two open backbones of different scales across four agent benchmarks, raising their average benchmark score by up to 18% relative, with the largest gains on skill-composition tasks, where accuracy more than doubles. These results show that evolving the task-synthesis harness itself, rather than freezing it, yields broader and more reliable training data than one-shot synthesis.