  Hypothesis tests based on unbinned log-likelihood (LLH) functions are a
common technique used in multi-messenger astronomy, including IceCube's
neutrino point-source searches. We present the general Python-based tool
"SkyLLH", which provides a modular framework for implementing and executing
log-likelihood functions to perform data analyses with multi-messenger
astronomy data. Specific SkyLLH framework features for a new and improved
time-integrated IceCube point-source analysis are highlighted, including the
support for kernel density estimation (KDE) based probability density
functions. In addition, the support for a variety of point-source analysis
types, such as stacked and time-variable searches, will be presented.
