Filtering is a powerful tool in CFD that can aid in accurately and efficiently predicting the governing physics in simulations, leading to improved designs. Filters can remove subgrid scale high-frequency physics so that only large scale structures remain in the filtered solution, alleviate aliasing error, and mitigate Gibbs phenomenon. They can even extract hidden accuracy. The same ideas are useful in data compression, post-processing, and machine learning. Well-designed filters, such as the one that gives rise to the Smoothness-Increasing Accuracy-Conserving (SIAC) post-processing filters, can be used to extract hidden information in certain numerical simulations, creating even more accurate representations of the data. They can be adapted for boundaries, unstructured grids, and non-smooth solutions. Furthermore, well-designed filters have the potential to accurately capture multi-scale physics, and are flexible enough to combine simulation information with experimental data. The SIAC Magic Toolbox provides a codebase for efficient, effective, flexible filters for general data. It takes in two data files: one data file consisting of information on the mesh and a second data file consisting of information from the corresponding approximation, either modal or nodal data. If desired, the user can choose parameters that correlate to the amount of dissipation, accuracy, and scaling. Otherwise these parameters are set as default parameters. The toolbox then returns the filtered information in the same format.