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About this paper
Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby
The authors present a framework for evaluating individual agent skills. It generates tasks, environments, inputs, and hidden rubrics from a skill, then compares solving with and without it. Across approximately 500 skills, 1,000 tasks, and 19 configurations, scores improved 5.5–22 points, mainly through instruction following. Results apply when skills are supplied, not independently discovered or selected.
Key insights
- Skill utility can be reframed from merely achieving a goal to emphasizing *how* a task is completed, with instruction-following being a crucial aspect for real-world quality, safety, and maintainability.
- Skills are most impactful in domains encoding specific, procedural workflows (e.g., Media & File Processing, Security & Compliance) rather than general guidelines, showing larger performance gains.
- The framework provides a granular diagnostic tool for individual skill improvement, allowing authors to identify specific behavioral changes induced by a skill and optimize its content.
Summary by alphaXiv.
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