Mengyu Sun et al. introduce SkillCT for agent skill assessment

Mengyu Sun et al. introduce SkillCT for agent skill assessment

Mengyu Sun, Zunlong Zhou, Ziyuan Yang, and Yi Zhang introduce SkillCT, a benchmark for assessing agent skills—packages of instructions, scripts, and resources for specialized tasks—beyond downstream task execution. They argue that existing benchmarks give too little attention to specification quality, behavioral robustness, semantic alignment, and responsibility boundaries, while task-specific tools, environments, and repeated executions limit scalability and pre-deployment assessment. SkillCT organizes assessment into nine dimensions across Static Assessment of the skill specification, Dynamic Assessment of behavior across backbone models and request variations, and Human-Assisted Assessment of behavior-chain consistency and task granularity. The authors say this gives a broader view of skill quality than execution success. They apply it to a public skill corpus to establish a reference benchmark for analyzing weaknesses and relationships across dimensions. Their execution-free baseline predicts unseen skills’ multidimensional quality profiles by retrieving similar reference skills, without running the skills being evaluated. The authors report that it reduces token consumption and evaluation time while preserving consistency with full assessment. They also report that SkillCT-guided optimization improves downstream task success and execution efficiency.

Publication: 7e979909-b274-4a53-8c76-7c7aaf852d27

attribution — pass: The title and summary credit Mengyu Sun, Zunlong Zhou, Ziyuan Yang, and Yi Zhang, matching the arXiv author list. Results are consistently framed as what the authors argue, say, or report.

Authors:Mengyu Sun, Zunlong Zhou, Ziyuan Yang, Yi Zhang

summary — pass: The summary faithfully tracks the abstract: the gap in existing benchmarks, the nine dimensions across three aspects, the corpus application, the execution-free baseline, and the optimization result. It defines agent skills in plain language and keeps technical terms exact. Terms like backbone models and token consumption can be handled by term tooltips. Optional repair: note that skills are increasingly distributed through public Web repositories, and the stated repository-maintenance use, to make the stakes clearer. The question targets a real gap.

Agent skills package instructions, scripts, and resources for specialized tasks and are increasingly distributed through public Web repositories.

date — pass: The originalPublished value of 2026-10-28 matches the arXiv v1 submission date. The later feed date of Oct 31 is correctly not used.

[Submitted on 28 Oct 2026]

support — pass: All five claims are source-stated and attributed, and their excerpts are exact substrings of the abstract. Efficiency, consistency, and optimization results remain the authors' reports, not verified outcomes. The limitations correctly note that the abstract gives no underlying measures and does not identify the corpus.

This reduces token consumption and evaluation time, while preserving consistency with the full assessment.

media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source, and no other source media was found.