SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior
Quick Answer
SkillJuror introduces a framework to evaluate agent skill organization, revealing that Progressive Disclosure significantly enhances runtime behavior in LLM agents.
Quick Take
SkillJuror introduces a framework to evaluate agent skill organization, revealing that Progressive Disclosure significantly enhances runtime behavior in LLM agents. In an 82-task SkillsBench study, it increased distinct Skill resources accessed from 1.18 to 3.85 and effective uptake events from 1.33 to 3.92, demonstrating that skill organization impacts procedural knowledge application.
Key Points
- Progressive Disclosure increases distinct Skill resources accessed from 1.18 to 3.85.
- Effective uptake events rise from 1.33 to 3.92 in the SkillsBench study.
- Skill organization influences how agents apply procedural knowledge.
- 17 additional verifier-passing trials were achieved, a 4.1% increase.
- Task dependency affects the benefits of Progressive Disclosure.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 11543v1 Announce Type: new Abstract: 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.
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