Are Production Cloud Skills Adequately Tested? Measuring and Governing Skill Test Adequacy in Practice

Abstract

Cloud platforms increasingly deliver reusable Cloud Skills that guide AI agents through multi-step resource operations, user choices, validation, and recovery. Existing Skill evaluation primarily measures whether a Skill improves task success, but passing the available testcases does not reveal which behaviors specified by the Skill remain untested. We introduce Skill Test Adequacy, a scenario-conditioned criterion that evaluates a test suite against the complete set of operational test obligations specified by a Skill. Given a Skill package and normalized testcases containing a prompt, an initial resource state, and expected user decisions, the assessment determines whether each obligation is exercised by at least one testcase scenario; the resulting records provide both a suite-level score and explicit test gaps. We operationalize the criterion through parallel obligation proposals, disagreement-preserving aggregation, testcase-level status proposals, expert review, and source-grounded recommendations. Alibaba Cloud deploys this process as a mandatory gate before task-success evaluation and subsequent release checks. Among 157 initial assessments recorded before gate-driven remediation, 57 (36.3%) fall below the mandatory 80% gate and 76 (48.4%) remain below the recommended 90% level. The process also produces 132 reports containing 639 obligation-level recommendations, with a median of four per Skill. Finally, we release SkillAdeqBench, an exploratory subset of the reviewed records for studying automatic adequacy assessment. Skill Test Adequacy complements task-success evaluation by making the untested scope of production Cloud Skills explicit.