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Maltcms, short for "Modular Application Toolkit for Chromatography Mass-Spectrometry" is an application framework mainly suited for developers working in the domain of bioinformatics for metabolomics and proteomics. Its aim is to provide reusable, efficient datastructures, abstracting from the various low-level data-formats like netcdf (ANDIMS), mzXML, mzData and mzML and providing consistent access to data features like mass spectra, chromatograms and metadata. Furthermore, Maltcms provides a platform for the implementation and execution of processing pipelines. New and existing algorithms can be easily implemented or included and executed together with existing processing elements.

The Interaction Model Mapper enables a code-first approach to the development of a skill by abstracting the interaction model. Alexa types such as Intents and Slots are defined as Java classes, and corresponding interaction model data such as sample utterances, prompts, synonyms, etc. are stored in resource bundles. A skill is then constructed from a collection of these entities, and the interaction model is rendered from the CLI. This abstraction enables developers to componentize and share parts of their skill with others and promotes a standard software build/deploy lifecycle for skills.