Scientific workflows play an important role in computational research as essential artifacts for communicating the methods used to produce research findings. We are witnessing a growing number of efforts that treat workflows as first-class artifacts for sharing and exchanging scientific knowledge, either as part of scholarly articles or as stand-alone objects. However, workflows are not born to be reliable, which can seriously damage their reusability and trustworthiness as knowledge exchange instruments. Scientific workflows are commonly subject to decay, which consequently undermines their reliability over their lifetime. The reliability of workflows can be notably improved by advocating scientists to preserve a minimal set of information that is essential to assist the interpretations of these workflows and hence improve their potential for reproducibility and reusability. In this paper we show how, by measuring and monitoring the completeness and stability of scientific workflows over time we are able to provide scientists with a measure of their reliability, supporting the reuse of trustworthy scientific knowledge