of experiences in which “domain-specific heuristics turned out to be the key component in several industrial applications of problem solvers” [@InvitedFriedrich15]. However, as argued in [@DBLP:journals/ai/Rintanen12]: “the main challenge in defining a variable selection scheme is its integration in the CDCL algorithm in a productive way”. Indeed, domain experts can quickly provide promising heuristic-criteria based on properties of solutions, but they may not have the knowledge and the experience to plug them in CDCL implementations.

The goal of the paper is to ease the design and the evaluation of new heuristics for CDCL solvers as well as to reduce the efforts needed to provide effective implementations. To this end we distill the key ingredients that drive the search of CDCL solvers, and propose a general framework for designing and implementing new heuristics. Useless to say that one could invent new heuristics based on other different “ingredients”. Nevertheless, our framework is quite rich and flexible, and indeed it covers the definition of the most popular heuristics for CDCL, as well as enables the definition of interesting problem-specific heuristics. Moreover, further features could be easily incorporated. A number of examples are provided to demonstrate the applicability of the proposed framework for defining 