Skill Cleanliness: Diagnosing and Restoring Execution Recoverability in Agent Skills

Skills provide language-model agents with reusable knowledge and operating procedures. Yet an agent can fail even when a Skill contains correct knowledge sufficient to solve the task. We introduce Skill Uncleanliness to describe organizational barriers that make task-relevant knowledge difficult to extract or available knowledge difficult to turn into executable actions. To diagnose these barriers, we propose Skill Cleanliness Score (SCS), which evaluates how knowledge can be accessed and used for a particular task and executor, and localizes defects that obstruct execution. We further propose Verified Skill Restoration (VSR), which uses the diagnosis to expose relevant evidence and make execution relations explicit while retaining their source support, intervening only when the repair has verifiable grounding. Experiments across task environments and model families show that targeted restoration can improve task success. On ScienceWorld, VSR improves success by 4.8–9.3 percentage points over a raw demonstration overlay with the same execution gate across seven executors from four model families. On ALFWorld, observation-driven progress restoration substantially improves execution by smaller models.