Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organized. We study this distinction through Progressive Disclosure, where a concise root file points agents to supporting resources on demand, and compare it with a normalized flat baseline. We present SkillJuror, a framework for evaluating Skill writing paradigms through semantically controlled variants, matched multi-trial evaluations, and trajectory evidence while holding task knowledge fixed. In an 82-task SkillsBench study, Progressive Disclosure changes runtime behavior before aggregate outcomes: distinct Skill resources touched per trajectory rise from 1.18 to 3.85, and effective uptake events rise from 1.33 to 3.92. It also yields 17 additional verifier-passing trials out of 410 matched trials (+4.1%) over the normalized flat baseline. The benefit is task-dependent. Progressive Disclosure helps when supporting resources guide implementation, checking, or repair, but is weaker when success hinges on exact output conventions, numerical thresholds, or long artifact-generation pipelines. These results show that Skill organization is not mere presentation: it can change how agents search and apply procedural knowledge, while outcome gains depend on whether the exposed resources are actionable for the task. Code is available at https://github.com/zhiyuchen-ai/skill-juror.

翻译：智能体技能在推理时为大型语言模型（LLM）智能体提供程序性知识，但当前的基准测试很少区分技能内容与其组织方式。我们通过渐进式披露（Progressive Disclosure）研究这一区别，该方法中一个简洁的根文件引导智能体按需访问支持资源，并将其与归一化的扁平基线进行比较。我们提出SkillJuror，一个通过语义受控变体、匹配的多轮评估和轨迹证据，在保持任务知识不变的前提下评估技能撰写范式的框架。在82项任务的技能基准研究（SkillsBench）中，渐进式披露在聚合结果出现前即改变了运行时行为：每条轨迹中触及的不同技能资源数量从1.18升至3.85，有效采纳事件从1.33升至3.92。它在410个匹配试验中比归一化扁平基线多产生17个验证通过的试验（+4.1%）。该收益与任务相关。当支持资源指导实现、检查或修复时，渐进式披露效果较好；但当成功依赖于精确的输出约定、数值阈值或长工件生成流程时，其效果较弱。这些结果表明，技能组织并非仅仅是呈现方式：它可以改变智能体搜索和应用程序性知识的方式，而结果收益取决于暴露的资源是否对任务可行。代码见https://github.com/zhiyuchen-ai/skill-juror。