Compound annotation remains challenging, especially in small molecule MS, which heavily relies on computational tools. The open sharing of mass spectral libraries is crucial for advancing tools that depend on high-quality data for direct matching or serve as training input for machine learning. Here, we present a high-throughput flow injection-MS n data acquisition method with an automatic data processing workflow, covering metadata cleanup, experiment planning, sequence creation in Python, and data acquisition on an Orbitrap-ID-X instrument, followed by a fully automatic library generation. The mzmine project provided a configurable platform for reproducible data processing that we extended with additional MS n support. The library building workflow was added to the mzwizard, a workflow setup tool embedded in mzmine, for easy parameter setup to simplify the onboarding of new users. Once all parameters are optimized, a batch configuration file is saved and can be reused and shared.