2025AJ....170..222E

Stellar spectral classification according to the Morgan-Keenan (MK) system remains fundamental to astrophysical studies, yet modern surveys require automated, scalable tools. We present NutMaat, an open-source Python-based package inspired by MKCLASS, designed to automate MK classification while addressing scalability and usability limitations. It employs modern computational tools for batch processing and offers a modular architecture that enables efficient, platform-independent analysis of large spectral data sets. It also includes modules for detecting classical chemically peculiar stars, such as Am, Ap, and λ Boo types, using internal consistency checks between different line diagnostics. Tested on the CFLIB and MILES libraries, NutMaat achieved spectral and luminosity classification accuracies comparable to MKCLASS, with minimal systematic offsets and a robust performance down to a signal-to-noise ratio ≲ 10. NutMaat successfully identified chemically peculiar stars, tested on LAMOST DR7 ACV variables, and processed the SDSS-IV MaStar library-which lacks native MK classifications-to produce a stellar catalog, demonstrating survey readiness. Future development of NutMaat will focus on extending wavelength coverage beyond the 3800-5600 Å range, computational acceleration via Cython, and refining peculiarity classification. Beyond its technical design, NutMaat can provide consistent, MK-standard classification across large-scale spectroscopic surveys, facilitating reliable stellar population analyses, identification of rare objects, and the construction of high-quality spectral catalogs essential for galactic archaeology and stellar evolution studies. As an open-source tool, NutMaat bridges traditional MK methods with modern data workflows, offering a scalable solution for current and future spectroscopic surveys.