arXiv:2605.23657v1 Announce Type: new Abstract: Skills, i.e., structured workflow instructions distilled for large language models (LLMs), are becoming an increasingly important mechanism for improving agent performance on real-world downstream tasks. However, as the open-source skill ecosystem rapidly expands, it remains unclear how different models and agent frameworks interact with skills, how to evaluate skill quality, and how users should select skills under practical cost-performance trade-offs. In this paper, we present \textsc{OpenSkillEval}, an automatic evaluation framework for both
The rapid expansion of the open-source skill ecosystem for LLMs necessitates frameworks to evaluate, audit, and select skills for real-world agent applications.
Evaluating and comparing LLM skills is critical for developers and businesses to build reliable, cost-effective, and safe AI agents, impacting their adoption and effectiveness.
The introduction of automated evaluation frameworks like OpenSkillEval will standardize skill assessment, allowing for more informed decisions on skill integration and development within AI agent ecosystems.
- · AI agent developers
- · Open-source AI communities
- · Businesses adopting AI agents
- · LLM framework providers
- · Inefficient skill developers
- · Proprietary, non-auditable AI solutions
OpenSkillEval provides a standardized method for comparing the performance and cost of different LLM skills.
This standardization will accelerate the development and adoption of robust AI agents by improving skill quality and reliability.
A highly transparent and auditable skill ecosystem could lead to new governance models for AI agent deployment, focusing on safety and performance benchmarks.
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