In Computational Science, Engineering and Finance (CSEF)scripts typically serve as the ``glue'' between potentially highlycomplex and computationally expensive external subprograms.Differentiability of the resulting programs turns out to beessential in the context of derivative-based methods for error analysis, uncertainty quantification, optimization or training of surrogates.We argue that it shouldbe enforced by the scripting languageitself through exclusive support of differentiable (smoothed) externalsubprograms and differentiable intrinsics combined withprohibition of nondifferentiable branches in the data flow.Illustration is provided by a prototype adjoint code compiler for asimple Python-like scripting language.