arXiv:2602.12670v4 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark whose current inventory contains 87 tasks across 8 domains paired with curated Skills and deterministic verifiers. Our latest aggregate evaluation runs the 87-task benchmark under matched no-Skills and curated-Skills conditions for 18 model-harness configurations. Curated Skills raise the average
The rapid development and adoption of LLM agents necessitate robust evaluation methods to ensure their efficacy and reliability, making benchmark introductions timely.
A standardized benchmark for AI agent skills provides critical insight into performance and helps drive the development of more capable and reliable AI systems, directly impacting industry adoption and investment.
The introduction of SkillsBench offers a concrete, measurable way to assess the practical utility of agent skills, allowing developers and organizations to objectively evaluate and improve their LLM agent implementations.
- · AI Agent Developers
- · Enterprises Adopting LLM Agents
- · AI Research Institutions
- · Ineffective Agent Skill Providers
- · LLM Agent Developers Without Robust Testing
Developers will begin to integrate SkillsBench into their LLM agent development cycles to validate performance.
Improved benchmarking will lead to a clearer differentiation between effective and ineffective agent skills, accelerating innovation in functional AI agents.
The widespread adoption of validated, highly skilled AI agents could significantly accelerate automation across industries, transforming white-collar workflows.
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Read at arXiv cs.AI