SUMMARY: The rapid expansion of multi-omics data has enabled the development of molecular signatures-coordinated patterns of molecular features that serve as powerful biomarkers for diagnosis, prognosis, and therapeutic decision-making. Despite their potential, many published signatures suffer from limited reproducibility and narrow applicability, partly due to challenges in summarizing complex, multi-feature profiles into a single, statistically sound and biologically meaningful score. Here, we introduce sigscores, an R package that streamlines the computation of summary scores for molecular signatures. Building on the quality control principles of our earlier tool, sigQC, sigscores supports an extensive array of scoring metrics-including measures of central tendency, dispersion, and aggregation. It incorporates a resampling framework to generate empirical null distributions for rigorous significance assessment and provides integrated visualization tools for diagnostic evaluation. Optimized for parallel execution on multi-core systems, sigscores is well-suited for both exploratory research and high-throughput large-scale applications. AVAILABILITY AND IMPLEMENTATION: Source code freely available for download on GitHub at https://github.com/alebarberis/sigscores, implemented in R and supported on MacOS and MS Windows.