We present STARK, a lightweight and flexible Python toolkit for extracting and analyzing syntactic (sub)trees from dependency-parsed corpora. By systematically slicing each sentence into interpretable syntactic units based on configurable parameters, STARK enables bottom-up, data-driven exploration of syntactic patterns at multiple levels of abstraction—from fully lexicalized constructions to general structural templates. It supports any CoNLL-U-formatted corpus and is available as a command-line tool, Python library, and interactive online demo, ensuring seamless integration into both exploratory and large-scale corpus workflows. We illustrate its functionality through case studies in noun phrase analysis, multiword expression identification, and syntactic variation across corpora, demonstrating its utility for a wide range of corpus-driven syntactic investigations.