Researchers have created a benchmark to test the reliability of agent skill retrieval systems. These systems are used in AI applications where skills can be retrieved from a library and contribute to an agent's instructions or execution. The benchmark, called SameCapRisk-Bench, pairs helpful skills with their 'risky' siblings that share the same capability family but differ on certain execution-controlling aspects. The researchers tested several public retrieval systems using this benchmark and found that while they were good at retrieving helpful skills, they often exposed the risky siblings as well. This highlights the need for systems to report both capability matching and risk exposure when retrieving skills. --- Why it matters: This matters because AI applications rely on accurate skill retrieval to function properly. If a system retrieves the wrong skill or exposes a risky sibling, it can lead to errors or even security breaches. By understanding and addressing this issue, researchers can improve the reliability of agent skill retrieval systems. Source: https://arxiv.org/abs/2606.10388