arXiv AI By Jermyn Zhen Yong Bek, Zhuang Qiang Bok, Zhongtian Sun

FinSkillBench: Evaluating AI Agents and Domain Skills for Investment Management

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FinSkillBench is an evaluation suite that tests whether language model agents can use financial domain skills to solve investment management tasks across portfolio construction, risk management, and fundamental analysis. The benchmark contains 12 subtasks with 2,603 episodes, each providing point‑in‑time inputs, hidden ground truth, and a verifier. Experiments show that curated skill packages improve performance significantly, while self‑generated skills offer little benefit, indicating that reliable procedural skills are crucial for effective AI agents in this domain.

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arXiv AI
Jun 17

A Framework for Evaluating Agentic Skills at Scale

arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.

By Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby