arXiv: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 holdin
The rapid development and deployment of LLM agents necessitate better methods for evaluating their performance and understanding how skill organization impacts their effectiveness.
Improving the skill organization of AI agents directly translates to more efficient and capable autonomous systems, accelerating their integration into various industries.
This research provides a framework (SkillJuror) to methodically evaluate different paradigms of agent skill organization, allowing developers to optimize agent design beyond mere skill content.
- · AI Agent developers
- · LLM companies
- · Autonomous system integrators
- · Robotics
- · Inefficient AI agent development pipelines
- · Organizations relying on brute-force LLM prompting without structured skill desi
More sophisticated and reliable AI agents become deployable across a wider range of tasks.
This improved agent capability drives further automation in white-collar work and complex operational environments.
The enhanced efficiency and reliability of AI agents could significantly accelerate the development of general artificial intelligence by providing better tools for self-improvement and complex problem-solving.
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Read at arXiv cs.AI