Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and success criteria vary across analytical formats. This raises the key question of how to discover reusable data-analysis skills from unlabeled exploration alone. We propose DataCOPE, an unsupervised verifier-guided skill discovery framework for data-analytic agents. DataCOPE derives verifier signals from the exploration trajectories and uses them to characterize relative quality or aggreement among trajectories. It iteratively coordinates a Data-Analytic Agent for trajectory generation, an Unsupervised Verifier for signal extraction, and a Skill Manager for contrastive skill distillation. For report-style analysis, we instantiate the verifier as an Adaptive Checklist Verifier that derives task-specific criteria, scores reports by verifiable coverage, and iteratively refines the checklist. For reasoning-style analysis, we instantiate it as an Answer Agreement Verifier that groups trajectories by answer agreement and uses self-consistency as an auxiliary signal. We evaluate DataCOPE on report-style analysis from Deep Data Research and reasoning-style analysis from DABStep. Across both settings, DataCOPE consistently improves held-out performance over baselines. Averaged across four model settings, DataCOPE improves the mean score by 9.71% and 32.30% on report-style and reasoning-style tasks respectively.

翻译：推理时技能增强提供了一种轻量级方法，通过注入可复用的程序性知识来提升数据分析智能体，而无需更新模型参数。然而，发现有效的数据分析技能仍具挑战性，原因是可靠监督代价高昂且成功标准因分析格式而异。这引出了一个关键问题：如何仅通过无标签探索就能发现可复用的数据分析技能。我们提出DataCOPE，一种面向数据分析智能体的无监督验证器引导技能发现框架。DataCOPE从探索轨迹中推导验证器信号，并利用这些信号刻画轨迹间的相对质量或一致性。它迭代式协调数据智能体（用于轨迹生成）、无监督验证器（用于信号提取）和技能管理器（用于对比性技能蒸馏）。对于报告式分析，我们将验证器实例化为自适应清单验证器，该验证器推导任务特定标准、通过可验证覆盖率为报告评分，并迭代精炼清单。对于推理式分析，我们将其实例化为答案一致性验证器，该验证器按答案一致性对轨迹分组，并以自一致性作为辅助信号。我们在Deep Data Research报告式分析与DABStep推理式分析上评估DataCOPE。在两种设置下，DataCOPE均持续提升了基线方法在保留任务上的性能。在四种模型设置上取平均，DataCOPE在报告式和推理式任务上分别将平均分提升了9.71%和32.30%。