Human-Robot Collaboration Systems (HRCSs) are integral to the automation industry. Robots work alongside humans to enhance productivity and efficiency across an extensive range of applications. In each case, ensuring the safety of HRCSs is crucial, necessitating a robust safety verification process—ideally while maintaining efficiency and optimizing cost. This imposes contradicting requirements. For example, an efficient assembly task might require high robot speed, but high speed can endanger humans. Both the complexity of the, typically manual, safety validation process and the resulting restraints on efficiency can hinder widespread adoption of HRCSs. Optimization in the face of the combinatorial explosion of possible HRCSs implementations and scenarios will require a new approach. Here, we propose a scenario-based safety verification framework for collaborative robots that can systematically generate and analyze scenarios and test simulated control strategies to ensure that human safety is maintained while achieving optimal productivity. The proposed approach consists of a text-based HRCSs operational scenario that describes the scene, hardware setup, and dynamical elements in the scene. Unlike existing methods, the proposed approach is intended to enable automated scenario generation and adaptive risk assessment, providing a scalable and repeatable framework for systematically designing and evaluating collaborative robot safety in dynamic operational settings.