sympy

8.6

311

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

mathematics

8.6

Rating

Installs

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Quick Review

Excellent skill for symbolic mathematics with SymPy. The description clearly delineates when to use symbolic vs numerical computation, making it easy for a CLI agent to invoke appropriately. The SKILL.md provides comprehensive task knowledge with well-organized code examples covering all major capabilities (calculus, equation solving, matrices, physics, code generation). Structure is very good with a logical overview in SKILL.md and modular reference files for deep-dives, though slightly verbose. Novelty is strong: symbolic mathematics tasks like deriving analytical solutions, performing symbolic integrals, or generating exact eigenvalues would require extensive trial-and-error or many tokens for a CLI agent without domain expertise. The skill meaningfully reduces cost by providing proven patterns, best practices (exact arithmetic, assumption usage), common pitfalls, and integration examples. Minor improvement possible by making SKILL.md slightly more concise, but overall this is a high-quality, production-ready skill.