It has been demonstrated repeatedly that combining multiple types of image features improves the performance of learning-based classification and regression. However, no tools exist to facilitate the creation of large pools of feature extractors by extended teams of contributors. The MASH project aims at creating such tools. It is organized around the development of a collaborative web platform where participants can contribute feature extractors, browse a repository of existing ones, run image classification and goal-planning experiments, and participate in public large-scale experiments and contests. The tools provided on the platform facilitate the analysis of experimental results. In particular, they rank the feature extractors according to their efficiency, and help to identify the failure mode of the prediction system.