Metabolic sensors are microbial strains modified such that biomass formation correlates with the availability of specific target metabolites. These sensors are essential for bioengineering (e.g. in growth-coupled selection of synthetic pathways), but their design is often time-consuming and low-throughput. In contrast, in silico analysis can accelerate their development. We present a systematic workflow for designing, implementing, and testing versatile metabolic sensors using Escherichia coli as a model. Glyoxylate, a key metabolite in synthetic CO2 fixation and carbon-conserving pathways, served as the test molecule. Through iterative screening of a compact metabolic reconstruction, we identified non-trivial growth-coupled designs that resulted in six metabolic sensors with different glyoxylate-to-biomass ratios. These metabolic sensors had a linear correlation between biomass formation and glyoxylate concentration spanning three orders of magnitude and were further adapted for glycolate sensing. We demonstrate the utility of these sensors in pathway engineering (implementing a synthetic route for one-carbon assimilation via glyoxylate) and environmental applications (quantifying glycolate produced by photosynthetic microalgae). The versatility and ease of implementation of this workflow make it suitable for designing and building multiple metabolic sensors for diverse biotechnological applications.