The mechanical behavior of dermal tissues is unarguably recognized for its diagnostic ability and in the last decades received a steadily increasing interest in dermatology practices. Among the various methods to investigate the mechanics of skin in clinical environments, suction-based ones are especially noteworthy, thanks to their qualities of minimal invasiveness and relative simplicity of setups and data analysis. In such experiments, structural visualization of the sample is highly desirable, both in its own right and because it enables elastography. The latter is a technique that combines the knowledge of an applied mechanical stimulus and the visualization of the induced deformation to result in a spatially resolved map of the mechanical properties, which is particularly important for an inhomogeneous and layered material such as skin. We present a device, designed for clinical trials in dermatology practices, that uses a handheld probe to (1) deliver a suction-based, controlled mechanical stimulus and (2) visualize the subsurface structure via optical coherence tomography. We also present a device-agnostic data-analysis framework, consisting of a Python library, released in the public domain. We show the working principle of the setup on a polymeric model and on a volunteer's skin.