SkillJuror framework shows agent skill organization shapes runtime behavior
Researchers introduce SkillJuror, a framework for evaluating how the structural organization of LLM agent Skills — not just their content — affects runtime behavior and task outcomes.
Score breakdown
The findings demonstrate that how procedural knowledge is structured for LLM agents — not just what it contains — measurably changes agent search behavior and task outcomes, establishing Skill organization as a distinct design variable for agent systems.
- 01SkillJuror is a framework for evaluating LLM agent Skill writing paradigms using semantically controlled variants, matched multi-trial evaluations, and trajectory evidence.
- 02The study compares Progressive Disclosure (a concise root file pointing to on-demand supporting resources) against a normalized flat baseline.
- 03Evaluated across an 82-task SkillsBench study with 410 matched trials.
Zhiyu Chen, Zihan Guo, and Bo Huang present SkillJuror, a framework designed to isolate the effect of Skill organization on LLM agent runtime behavior, independent of task knowledge content. The core insight is that current benchmarks rarely distinguish what a Skill says from how it is structured — a gap the authors address by introducing semantically controlled variants, matched multi-trial evaluations, and trajectory evidence as evaluation mechanisms.
It offers weaker gains on tasks that depend on exact output conventions, numerical thresholds, or long artifact-generation pipelines.
The study contrasts two organizational paradigms: Progressive Disclosure, in which a concise root file directs agents to supporting resources only as needed, and a normalized flat baseline that presents all information at once. Across an 82-task SkillsBench study with 410 matched trials, Progressive Disclosure drove a substantial increase in agent engagement with Skill resources — distinct resources touched per trajectory rose from 1.18 to 3.85, and effective uptake events rose from 1.33 to 3.92 — and produced 17 additional verifier-passing trials (+4.1%) over the flat baseline.
The authors find that the performance benefit is conditional: Progressive Disclosure is most effective when the exposed supporting resources are actionable for the task at hand, such as guiding implementation, checking, or repair steps. It offers weaker gains on tasks that depend on exact output conventions, numerical thresholds, or long artifact-generation pipelines. The paper concludes that Skill organization is not merely a presentational choice but a structural variable that shapes how agents search and apply procedural knowledge. Code is available at https://github.com/zhiyuchen-ai/skill-juror.
Key facts
- 01SkillJuror is a framework for evaluating LLM agent Skill writing paradigms using semantically controlled variants, matched multi-trial evaluations, and trajectory evidence.
- 02The study compares Progressive Disclosure (a concise root file pointing to on-demand supporting resources) against a normalized flat baseline.
- 03Evaluated across an 82-task SkillsBench study with 410 matched trials.
- 04Progressive Disclosure increased distinct Skill resources touched per trajectory from 1.18 to 3.85.
- 05Effective uptake events per trajectory rose from 1.33 to 3.92 under Progressive Disclosure.
- 06Progressive Disclosure yielded 17 additional verifier-passing trials out of 410 matched trials (+4.1%) over the flat baseline.
- 07The benefit is task-dependent: weaker when tasks hinge on exact output conventions, numerical thresholds, or long artifact-generation pipelines.
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