Skill Coverage: A Test Adequacy Metric for Agent Skills
Agent skills encode reusable procedural knowledge for large language model (LLM) agents, and existing benchmarks show that such skills can improve task-level performance. However, a task outcome does not reveal which parts of a reusable skill were exercised, nor whether the agent followed the relevant skill instructions when those parts were exercised. This gap makes it unclear whether a skill has been adequately tested, or whether observed task failures provide actionable evidence for improving agent skill effectiveness. To fill this gap, we introduce skill coverage, a trajectory-based test-adequacy metric for reusable agent skills. Our framework extracts skill behavior constraints from ...
| Authors | Boyin Tan, Xiaowei Huang, Youcheng Sun |
|---|---|
| Primary category | cs.AI |
| Published | 9 June 2026 |
| Last updated at source | 4 July 2026 |
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