  Common analysis techniques in multi-messenger astronomy involve hypothesis
tests with unbinned log-likelihood (LLH) functions using data recorded in
celestial coordinates to identify sources of high-energy cosmic particles in
the Universe. We present the new Python-based tool "SkyLLH" to develop such
analyses in a telescope-independent framework. The main goal of the software is
to provide an easy-to-use and modularized concept to implement and to execute
such LLH functions efficiently on the computer with high-performance. SkyLLH
can be applied on different multi-messenger data like neutrino and gamma-ray
events from experiments such as the IceCube Neutrino Observatory and the
Fermi-LAT. In this contribution we highlight SkyLLH's various design goals,
current development status, and prospects for its wider application in
multi-messenger astronomy.
