Soil moisture dynamics govern processes at the soil-vegetation-atmosphere interface, yet capturing these dynamics at relevant spatiotemporal scales remains challenging. Cosmic Ray Neutron Sensing (CRNS) has emerged as a key technology for measuring soil moisture at the field scale, bridging the spatial gap between traditional point-scale measurements and coarser remote sensing products. With the increasing adoption of CRNS across research infrastructures, including eLTER, standardized and flexible processing tools are essential for supporting CRNS integration within these networks. Here we present neptoon, an open-source Python tool that implements a modular, expandable framework to support both operational deployment of CRNS, as well as methodological innovation. Building from previous CRNS processing tools, we will present the overall architecture of neptoon and how it implements established processing methodologies while maintaining extensibility for emerging approaches. Through an intuitive configuration system and graphical user interface, neptoon streamlines data processing workflows and ensures reproducibility across research sites. As our understanding of the sensor signal continues to improve, the ability for research infrastructures to quickly implement the latest advancements becomes ever more important. We will demonstrate how neptoon facilitates rapid deployment of these latest processing methodologies, supports cross-site harmonisation, whilst also enabling robust testing of experimental correction methods. Through its support of multiple stakeholders, from researchers to sensor owners, the latest advancements can be pushed quickly back to the broader community. By providing a standardised yet flexible processing framework, neptoon aims to accelerate the integration of CRNS measurements into critical zone research and enhance our understanding of soil moisture dynamics across scales.