Harmonizing variables is an important part of research in the social sciences as it maximizes the value of existing research data by enhancing the secondary reuse value of the datasets. However, to be of wide-spread scientific value to the community, the harmonisation process must be documented and published in precise and transparent ways, so as to be both replicable and citable. GESIS solution to this challenge was to draw on DDI metadata structuring when developing our line of "CharmStats" harmonization tools. CharmStats processes metadata informed by the structure DDI uses for variable-, question- and study level information. Our poster maps the types of input flows, including DDI, into the CharmStats system, displays the types of outputs generated by CharmStats and which data formats are used while importing and exporting.