Efficient performance engineering for Julia programs heavily relies on understanding the result of type-inference on your program, type-inference as process is sensitive to local information or call-context. Many Julia users use the information provided by @code_typed to analyse the behaviour of type-inference on a function. This method becomes cumbersome and inefficient with deeply nested programs where the user needs to reconstruct local information to inspect called methods. This talk introduces a tool that streamlines this process and allows users to take a static walk through their dynamic program. It simplifies the performance engineering process and is capable of handling code that uses tasks, threads and GPUs.