ContinualSkillBench

Skill libraries are shipping in agent harnesses on the assumption that writing skills down compounds, and this benchmark tests that assumption directly. ContinualSkillBench covers five domains, each with 100 interconnected subtasks ordered by increasing difficulty and built with deliberate opportunities for cross-task skill reuse. Sequential execution generally improves performance, though the gains vary substantially across models and domains, and maintaining an explicit skill library performs comparably to plain in-context learning on average. Much of the improvement comes from adapting to prior context and feedback rather than from reusable skill abstraction, though explicit skills still pay off selectively on tasks needing reusable procedures or precise outputs. There is a useful diagnostic buried in the results. Less capable models accumulate larger, more fragmented collections of task-specific skills, which is what failed abstraction looks like from the outside.