This is your go-to for symbolic math in Python when you need exact answers instead of floating point approximations. It covers the full SymPy workflow from defining symbols and solving equations algebraically to calculus operations, matrix manipulations, and physics calculations. The skill includes proper guidance on lambdify for converting symbolic expressions to fast NumPy functions, code generation for C and Fortran, and critical details like using assumptions on symbols to get better simplifications. What makes it genuinely useful is the coverage of common pitfalls, like the difference between Rational(1,2) and 0.5, and when to reach for solveset versus linsolve versus dsolve. If you're doing anything beyond basic numerical computation, this skill will save you from StackOverflow rabbit holes.
npx skills add https://github.com/davila7/claude-code-templates --skill sympy