Julia is a general-purpose programming language. The language itself is actually really good as a scripting language, including integrating well with shell, C, Fortran, R and Python, for example:

https://docs.julialang.org/en/latest/manual/running-external...

The only trade-off here is the restricted resources the Julia devs can afford. Focusing on the scientific computation niche, the Julia compiler was built to compile code just ahead of time, since this way it can create code as fast as compiled languages while still being dynamic like any interpreted language (such as Matlab and Python). But there is nothing in the language that prevents it from being fully interpreted (and therefore having minimal compile time overhead, but generating poorly optimized code for long running processes), or alternatively pre-compiling and caching to have both (which is being worked on: https://github.com/JuliaLang/PackageCompiler.jl )

Not Scripting != Not general purpose

I'm not a Julia developer, but the great thing with Julia is that the libraries are exceptionally easy to read and extend, because the core developers make a strong effort to write clean code. Just look at ImageMagick.jl and you'll see, but I have the same experience with other libraries as well.

Also the language itself supports extending code (multiple dispatch really helps it to stay clean).