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    <abstract>The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evaluation becomes critical to ensuring their utility, quality, and safety in real-world applications. Consequently, the field is undergoing an emerging paradigm shift from isolated skill creation to automated, evaluation-driven skill evolution. In this survey, we systematically examine the landscape of skill evolution and evaluation beyond foundational skill creation. We categorize evolution into four distinct paradigms, spanning execution feedback, trajectory distillation, compression, and reinforcement learning, showing how each element contributes to improving skill utility and reliability. We also provide an analysis of six skill-centric benchmark categories, identifying structural gaps in benchmark coverage, trade-offs, and metric richness to advance skill research. Finally, we identify open directions for building skill ecosystems that are generalizable, efficient, and verifiably safe. The project URL is https://github.com/Cassie07/AgentSkill_Survey</abstract>
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%0 Conference Proceedings
%T Agent Skill Evaluation and Evolution: Frameworks and Benchmarks
%A Ding, Kexin
%A Zhou, Yang
%A Jin, Can
%A Tong, Feng
%A Zhou, Mu
%A Metaxas, Dimitris N.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ding-etal-2026-agent
%X The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evaluation becomes critical to ensuring their utility, quality, and safety in real-world applications. Consequently, the field is undergoing an emerging paradigm shift from isolated skill creation to automated, evaluation-driven skill evolution. In this survey, we systematically examine the landscape of skill evolution and evaluation beyond foundational skill creation. We categorize evolution into four distinct paradigms, spanning execution feedback, trajectory distillation, compression, and reinforcement learning, showing how each element contributes to improving skill utility and reliability. We also provide an analysis of six skill-centric benchmark categories, identifying structural gaps in benchmark coverage, trade-offs, and metric richness to advance skill research. Finally, we identify open directions for building skill ecosystems that are generalizable, efficient, and verifiably safe. The project URL is https://github.com/Cassie07/AgentSkill_Survey
%U https://aclanthology.org/2026.findings-emnlp.1427/
%P 30412-30431

Markdown (Informal)

[Agent Skill Evaluation and Evolution: Frameworks and Benchmarks](https://aclanthology.org/2026.findings-emnlp.1427/) (Ding et al., Findings 2026)

• Agent Skill Evaluation and Evolution: Frameworks and Benchmarks (Ding et al., Findings 2026)

ACL

• Kexin Ding, Yang Zhou, Can Jin, Feng Tong, Mu Zhou, and Dimitris N. Metaxas. 2026. Agent Skill Evaluation and Evolution: Frameworks and Benchmarks. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 30412–30431, Budapest, Hungary. Association for Computational Linguistics.