SUMMARY: Genome-wide association studies have revealed that many disease-associated genetic variants lie in non-coding regions of the genome. To prioritize these variants and clarify their functional roles, accurate classification of cis-regulatory elements is essential. Early approaches relied on characteristic histone marks, while more recent methods use Hidden Markov Models to segment the genome into chromatin states. However, these models often produce abstract states that require manual interpretation to assign regulatory function. REgulamentary is introduced as a rule-based framework for de novo, genome-wide annotation of cis-regulatory elements in a cell type-specific manner. Its behaviour is compared with count-based and segmentation-based approaches to highlight differences in classification strategy and the interpretability advantages of a rule-based design. Finally, its utility in the analysis of complex disease loci is demonstrated through application to published genetic association data to prioritize likely causal variants. AVAILABILITY AND IMPLEMENTATION: REgulamentary is implemented in Python with a Snakemake-based workflow for reproducible analysis, integrating standard bioinformatics tools. The software is available at: https://github.com/Genome-Function-Initiative-Oxford/REgulamentary.