Initial consonant mutation is a key feature of Welsh, but its complexity poses significant challenges for both language learners and natural language processing (NLP) systems. While existing tools can reliably detect mutated forms, they provide no information about why a mutation occurs, i.e. what grammatical or lexical factors trigger the change. This paper introduces the novel task of mutation trigger labelling, representing the first computational attempt to analyse and explain the reasons behind Welsh mutations. Two preliminary approaches are explored: (i) a linguistically-informed rule-based system integrating Constraint Grammar rules, and (ii) large language models (LLMs), prompted in few-shot settings. Our experiments test the feasibility of automatically identifying and labelling linguistic triggers behind Welsh mutations using a dataset constructed from grammar reference books and public corpora, and establish baseline insights into how context-aware mutation analysis can be achieved. By framing mutation trigger labelling as a linguistic computational problem, this work lays important groundwork within Welsh NLP and contributes to the broader development of explainable grammatical analysis for low-resource languages.

We introduce Proffiliadur, a Python toolkit for text profiling and readability analysis in Welsh. The toolkit computes 141 surface, lexical, morphological, and syntactic indices, designed to capture linguistic variation while incorporating a Welsh-specific tokenisation process that enables accurate morphological analysis and handles phenomena such as initial consonant mutation. Proffiliadur enables systematic assessment of text accessibility and supports applications in education, healthcare, and public communication. We demonstrate the toolkit’s usefulness through two complementary analyses. First, we examine texts written in accordance with the Cymraeg Clîr ("Clear Welsh") principles and compare them with regular Welsh texts. Second, we analyse texts across CEFR proficiency levels to explore how linguistic complexity varies with learner ability. We also evaluate feature-based and neural classification models for automatic complexity detection, showing that interpretable linguistic indices alone achieve strong predictive performance (F1 = 0.94), comparable to a fine-tuned transformer (F1 = 0.97). Proffiliadur provides the first dedicated text profiling toolkit for Welsh, offering reproducible, linguistically grounded measures of readability for a low-resource language.