lib-sympy

Provides Python symbolic mathematics with calculus, equation solving, and LaTeX output via SymPy.

Updated Mar 13, 2026
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-sympy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: lib-sympy
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/lib-sympy
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill lib-sympy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill empowers you to perform complex symbolic mathematical computations with exact precision, moving beyond numerical approximations for algebra, calculus, and more.

Core Features & Use Cases

  • Symbolic Algebra & Calculus: Solve equations, differentiate, integrate, and simplify expressions exactly.
  • Advanced Math: Tackle problems in geometry, number theory, and combinatorics.
  • Code Generation: Convert mathematical expressions into executable Python, C, or Fortran code.
  • Use Case: Solve a system of non-linear differential equations symbolically, then generate C code for the solution to be used in a simulation.

Quick Start

Use the lib-sympy skill to define symbols x and y, then compute the derivative of x**2 * sin(y) with respect to x.

Frequently Asked Questions about lib-sympy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform exact symbolic algebra and calculus in Python without numerical approximations?

You can perform exact symbolic algebra and calculus in Python by using the SymPy library to define symbols and compute exact expressions. It directly solves equations and calculates derivatives without numerical approximations.

Can I generate executable C or Fortran code from mathematical expressions in Python?

Yes, you can generate executable C or Fortran code from Python mathematical expressions using SymPy. This capability translates symbolic solutions into compiled language source code for high-performance simulation deployment.

What is the best way to solve non-linear differential equations symbolically and export LaTeX?

The best way to solve non-linear differential equations symbolically and export LaTeX is using a Python symbolic math library. It computes exact analytical solutions and renders them into formatted LaTeX strings for academic documentation.

Does SymPy support advanced number theory and combinatorics computations?

Yes, SymPy supports advanced number theory and combinatorics computations. It extends beyond standard algebra and calculus to provide exact symbolic manipulation for complex mathematical problem domains.

Do I need to install any specific libraries to run Python symbolic math operations?

You need to install the SymPy library to run Python symbolic math operations, as it is the required dependency for all symbolic manipulation tasks. This library provides the core engine for exact algebraic computation and calculus.