From Agent Failures to Faulty Directives: Attribution-Based Aggregate Analysis of Skill Files

Current agentic systems increasingly rely on suites of Skill files to provide task-specific capabilities and encode proprietary knowledge that may be absent from the model's training data. Consequently, inconsistencies and errors in these files can directly affect agent behavior, leading to user dissatisfaction or task failure. However, it remains challenging for agent developers to identify which parts of a Skill file cause failures or user dissatisfaction. To the best of our knowledge, we are the first to study how agent failures can be traced to Skill directives. We introduce SkillRoot , an execution-trace-driven framework that attributes agent actions to their supporting context and represents Skill content as a knowledge graph of directives, entities, and relationships. SkillRoot aggregates evidence from successful and failed executions to compute a failure-association prior over directives, which an LLM-based judge combines with a new failure trace to localize responsible Skill chunks. Across our evaluation, SkillRoot improves faulty-directive identification by up to 6% over standard baselines.