  The labor market is changing rapidly, prompting increased interest in the
automatic extraction of occupational skills from text. With the advent of
English benchmark job description datasets, there is a need for systems that
handle their diversity well. We tackle the complexity in occupational skill
datasets tasks -- combining and leveraging multiple datasets for skill
extraction, to identify rarely observed skills within a dataset, and overcoming
the scarcity of skills across datasets. In particular, we investigate the
retrieval-augmentation of language models, employing an external datastore for
retrieving similar skills in a dataset-unifying manner. Our proposed method,
\textbf{N}earest \textbf{N}eighbor \textbf{O}ccupational \textbf{S}kill
\textbf{E}xtraction (NNOSE) effectively leverages multiple datasets by
retrieving neighboring skills from other datasets in the datastore. This
improves skill extraction \emph{without} additional fine-tuning. Crucially, we
observe a performance gain in predicting infrequent patterns, with substantial
gains of up to 30\% span-F1 in cross-dataset settings.
