SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

Researchers announced SkillsBench, a comprehensive benchmark for evaluating how well agent skills function across diverse tasks, in a paper published on arXiv on February 26, 2026, addressing the critical need for standardized measurement of skill effectiveness despite their growing adoption in large language model agents. The benchmark comprises 86 tasks spanning 11 domains, each paired with curated skills and deterministic verifiers to ensure consistent evaluation. This innovative approach allows researchers to assess the actual value of agent skills—structured packages of procedural knowledge that enhance LLM capabilities at inference time—rather than relying solely on theoretical assumptions about their utility. The evaluation framework systematically tests each task under three distinct conditions: without any skills, with curated pre-designed skills, and with self-generated skills, providing a comprehensive picture of skill effectiveness across different scenarios. The researchers applied this benchmark to 7 different agent-model configurations to gather comparative data on performance variations across architectures and implementations.