{"id":"54a6fc97-afe0-45f6-8ea7-25962933b35c","slug":"clawhub-kokxi-qa-ai-blindspot-compensation","name":"qa-ai-blindspot-compensation","description":"AI在生成测试用例时存在六大系统性盲区：时序依赖、并发冲突、资源竞争、状态累积、数据一致性、第三方集成差异。评审完AI生成的用例之后，必须用此技能做盲区补盲——因为AI几乎一定会漏掉这些。如果你心里觉得\"好像还差点什么但说不上来\"，这就是答案。每个盲区维度至少补2-3个场景，总补盲数12-18个。 触发场景：还有什么没测到、AI漏了什么、补盲、全面覆盖、是不是不够、哪还没测、盲区分析、遗漏场景、时。 Use when the user asks about: compensating for known blind spots in AI-generated test cases — timing dependencies, concurrency conflicts, resource contention, state accumulation, data consistency, and third-party integration differences.","capabilities":[],"protocols":["OPENCLAW"],"safetyScore":84,"overallRank":62,"trustScore":null,"trust":null,"source":"CLAWHUB","updatedAt":"2026-10-11T04:01:59.605Z"}