Agent skill · research science · aiskillstore
sympy
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.
Why this skill is useful
Provides domain-specific knowledge for symbolic computation tasks that the AI wouldn't reliably generate on its own.
What it needs
Requires sympy installed locally. About 7k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
SymPy - Symbolic Mathematics in Python Overview SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations. This skill provides comprehensive guidance for performing symbolic algebra, calculus, linear algebra, equation solving, physics calculations, and code generation using SymPy. When to Use This Skill Use this skill when: Solving equations symbolically (algebraic, differential, systems of equations) Performing calculus operations (derivatives, integrals, limits, series) Manipulating and simplifying algebraic expressions Working with matrices and linear algebra symbolically Doing physics calculations (mechanics, quantum mechanics, vector analysis) Number theory computations (primes, factorization, modular arithmetic) Geometric calculations (2D/3D geometry, analytic geometry) Converting mathematical expressions to executable code (Python, C, Fortran) Generating LaTeX or other formatted mathematical output Needing exact mathematical results (e.g., sqrt(2) not 1.414...) Core Capabilities 1. Symbolic Computation Basics Creating symbols and expressions: Simplification and manipulation: For detailed basics: See references/core-capabilities.md 2. Calculus Derivatives: Integrals: Limits and Series: For detailed calculus operations: See references/core-capabilities.md 3. Equation Solving Algebraic equations: Systems of equations: Differential equations: For detailed solving methods: See references/core-capabilities.md 4. Matrices and Linear Algebra Matrix creation and operations: Eigenvalues and eigenvectors: Solving linear systems: For comprehensive linear algebra: See references/matrices-linear-algebra.md 5. Physics and Mechanics Classical mechanics: Vector analysis: Quantum mechanics: For detailed physics capabilities: See references/physics-mechanics.md 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/sympy