  Undisputedly, derivation of theoretical systematic uncertainties is an
inseparable ingredient of any robust analysis dealing with experimental data.
However, it is not uncommon, even for those analyses that use state of the art
methods and tools to suffer from insufficient statistics when it comes to the
simulated datasets used to estimate systematic uncertainties. This practically
limits the power, and sometimes the robustness of the analysis.
  In this paper, we present SysCalc, a code which is able to derive weights for
various important theoretical systematic uncertainties, including those related
to the choice of the Parton Distribution Function sets and the various scale
choices. SysCalc utilizes the central sample generated events to estimate the
related systematic uncertainties, thus, omitting the need for generating
dedicated systematics datasets, and with only a minimal added cost in terms of
computing resources. In this paper we discuss the working principles of the
code accompanied by various validation plots. We also discuss the structure of
the code followed by a practical guide for how to use the tool.
