Styx: A multi-language API Generator for Command-Line Tools

In numerous scientific domains, established tools have often been developed with complex command-line interfaces. Such is the case for brain imaging and bioinformatics, making the use of powerful legacy tools in modern workflow paradigms challenging. We present (i) Styx, a compiler for generating language-native wrapper functions from static tool metadata, leading to seamless integration of command-line tools within the data science ecosystem. Alongside Styx, we have created (ii) NiWrap, a collection of more than 1900 neuroimaging command-line function descriptions as a proof-of-concept implementation. These interfaces, available in Python, R, and TypeScript (available at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/styx-api">https://github.com/styx-api</ext-link>), significantly reduce the complexity of writing and interpreting software pipelines, particularly when composing workflows across packages with distinct API standards. The compiler architecture of Styx facilitates maintainability and portability across computing environments. As with all metadata-dependent infrastructure, creating sufficient metadata annotations remains a barrier to adoption. Accordingly, NiWrap demonstrates approaches that lower this barrier through direct source code extraction and LLM-assisted documentation parsing. Together, Styx and NiWrap offer a sustainable solution for interfacing diverse command-line tools with modern data science ecosystems. This modular approach enhances reproducibility and efficiency in pipeline development while ensuring portability across computing environments and programming languages.