Algorithms for solving the consensus problem are fundamental to distributed computing. Despite their brevity, their ability to operate in concurrent, asynchronous and failure-prone environments comes at the cost of complex and subtle behaviors. Accordingly, understanding how they work and proving their correctness is a non-trivial endeavor where abstraction is immensely helpful. Moreover, research on consensus has yielded a large number of algorithms, many of which appear to share common algorithmic ideas. A natural question is whether and how these similarities can be distilled and described in a precise, unified way. In this work, we combine stepwise refinement and lockstep models to provide an abstract and unified view of a sizeable family of consensus algorithms. Our models provide insights into the design choices underlying the different algorithms, and classify them based on those choices.