Perhaps the most used source for deriving prestige and status scores (e.g. ISEI, SIOPS, EGP) from occupational classifications are Harry Ganzeboom’s SPSS codes (Ganzeboom 2016), which he publishes on his website. The well-known Stata modules by John Hendrickx (2002, 2004) adapt these scripts in Stata. Albeit these scripts being the most sophisticated mechanism publicly available to calculate prestige and status scores, the approach via syntax codes has two main shortcomings: (1) the necessarily complex architecture of those scripts makes it hard for users to fully comprehend the derivation process in its details; (2) currently, code for deriving prestige and status score variables from the latest ISCO-08 is available for SPSS only. Packages for other statistical platforms, such as R or SAS, also are only partially available. We present a Stata module which tries to overcome these issues. By creating a framework that establishes all variables’ derivation via lookup tables, the whole process becomes more flexible. This approach can produce the same results as the established way of using Harry Ganzeboom’s SPSS or John Hendrickx Stata codes, when used with the appropriate lookup tables delivered within the package. However, several benefits emerge from the concept of lookup tables: The end user can easily understand why a certain code led to a certain status score by having a detailed look at the tables. Additionally, he can customize the whole process to his needs by using self-administered lookup tables, either by only slightly adapting the original tables, or by interchanging the lookup information with completely different tables. Using the latter path, the full flexibility of the lookup table approach leads to considerable advantages. Not only does it enable the user to derive other than the “standard” prestige or status scores, like the Magnitude Prestige Scale (MPS) or Blossfeld’s occupational classification (BLK). Our approach also gives the possibility to run the derivation process from totally different input, from national classifications (like the German KldB) up to bare answers from surveys. It also makes it possible to create cross-walk tables between classifications. Thereby, error checking and reporting, as well as the possibility of “inline documentation” of the derivation process (by citation of the lookup tables) and (multilingual) labeling of values, is handled by the Stata procedure in a structured, standard way. User defined lookup tables can, when properly documented, be submitted to the authors and become part of the package and its documentation. Finally, our approach of using lookup tables for variable derivation can be ported to other statistical software platforms, like SPSS, R or SAS. All tables are convertible between platforms, so that only the program logic of the derivation process has to be translated into the specific platform’s language. This can eventually lead to a default way of deriving occupational scores from classifications across different software platforms and harmonization of these variables between surveys producing research data, such as NEPS, SOEP or PASS.