What problem does it solve? Crossing from symbolic mathematics into numerical computing introduces silent errors: wrong symbol ordering, dtype mismatches, domain violations, and solver results accepted on a success flag alone. This Skill enforces explicit contracts at the SymPy-to-NumPy-to-SciPy boundary so numerical results stay faithful to the original mathematics. ## Core Features & Use Cases - Explicit lambdify contracts: Define symbol order, modules selection, input shapes, broadcasting, dtypes, and singular-point behavior before vector evaluation. - SciPy solver adaptation: Adapt lambdified callables to root-finding, least-squares, minimization, and integration signatures, then validate with residuals, constraints, and multiple starts or brackets. - Cross-boundary verification: Cross-check numerical results against higher-precision SymPy/mpmath or independent formulations, including finite-difference checks of symbolic Jacobians. - Use Case: You derived a symbolic expression like exp(-a*x) - x and need its root for a given parameter. The Skill guides you to lambdify with an explicit ordered tuple, solve with a bracketed brentq call, and reject the result unless the residual falls below tolerance. ## Quick Start Use the sympy-numpy-scipy-boundaries skill to lambdify my symbolic expression and solve it with SciPy, verifying the residual and domain assumptions.
