When and How Do Skills Fail Agents? Tracing Failures in Skill-Augmented Agent Trajectories to Their Source

Skills have become an important mechanism for improving the ability of large language model (LLM) agents to perform complex tasks. When a skill-augmented execution fails, however, the failure signal itself says little about the cause: a defective skill and an agent's misuse of a correct skill produce the same outcome but demand opposite repairs. This makes failure attribution over long-horizon trajectories of skill-augmented agents an important research problem. To study this problem, we develop a fine-grained attribution taxonomy and construct a human-annotated benchmark containing 194 trajectories from 30 task groups to evaluate the attribution capabilities of leading models. Our evaluation shows that many mainstream reasoning models have not yet reached a practically useful level of attribution performance. To further advance research on this capability, we propose SkillTracer, a 9B model specialized for skill attribution. SkillTracer improves attribution pass@1 by 11.3 points over its base model and outperforms frontier models evaluated without explicit reasoning at a fraction of their inference cost. Benchmark and code are available at https://anonymous.4open.science/r/SkillAttribution-5906.