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sympy

Perform exact symbolic mathematics on algebraic, calculus, and differential equation expressions.

2|Updated Apr 12, 2026
Search Tags:#physics#calculus#code-generation#linear-algebra#sympy#symbolic-math#computer-algebra
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill sympy-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sympy
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/sympy
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill sympy-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Enable exact symbolic reasoning and manipulation of mathematical expressions so users obtain analytic results, proofs, and reproducible code instead of floating-point approximations or ad hoc numerical workarounds.

Core Features & Use Cases

  • Symbolic algebra & simplification: create symbols, expand, factor, collect, and simplify complex expressions with assumptions.
  • Calculus & series: compute derivatives, integrals, limits, and series expansions symbolically and evaluate to arbitrary precision when needed.
  • Equation solving: solve algebraic systems, nonlinear systems, and differential equations with exact representations and verification patterns.
  • Matrices & linear algebra: build symbolic matrices, compute determinants, eigenvalues, diagonalization, and solve linear systems.
  • Physics, geometry, and number theory: apply mechanics, vector analysis, quantum operators, geometry constructions, prime factorization, modular arithmetic, and Diophantine solving.
  • Code generation & output: convert symbolic expressions to NumPy functions, C/Fortran code, LaTeX, MathML, and optimized routines using lambdify, codegen, autowrap, and ufuncify.
  • Use Case Example: derive a symbolic integral, simplify the result under assumptions, produce LaTeX for publication, and generate a fast NumPy function to evaluate the result numerically.

Quick Start

Use the sympy skill to symbolically solve an equation, simplify the result with appropriate assumptions, and return both an exact analytic derivation and an optional runnable NumPy function.

Frequently Asked Questions about sympy

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

FAQPage Schema
How do I compute exact symbolic integrals and derivatives instead of floating-point approximations?▼

Exact symbolic integrals and derivatives are computed by applying symbolic mathematics rules to mathematical expressions, returning analytic results with arbitrary precision rather than floating-point approximations. You obtain exact closed-form solutions verifiable through assumptions.

Can I convert symbolic math expressions into executable C or Fortran code?▼

Symbolic math expressions can be converted into executable C or Fortran code using code generation workflows. The process transforms exact symbolic results into optimized routines and lambdified NumPy functions for fast numerical evaluation.

What is the best way to solve differential equations and nonlinear systems symbolically?▼

Solving differential equations and nonlinear systems symbolically involves applying exact algebraic representations to the equations. This yields analytic solutions with exact verification patterns, ensuring reproducible mathematical derivations instead of numerical approximations.

Does this approach to symbolic computation work with NumPy and mpmath for numerical evaluation?▼

Symbolic computation integrates directly with NumPy and mpmath for numerical evaluation. You generate lambdified numeric functions from exact symbolic expressions, bridging analytic derivations with high-performance numerical workflows.

How do I simplify complex algebraic expressions with mathematical assumptions?▼

Complex algebraic expressions are simplified by applying mathematical assumptions to symbols, enabling expand, factor, collect, and simplify operations. This produces exact transformed expressions validated under the specified constraints.

When should I not use symbolic mathematics for my computations?▼

Symbolic mathematics should be avoided when you require rapid numerical approximations over exact analytic derivations, or when processing highly complex expressions where symbolic simplification becomes computationally prohibitive compared to direct numerical methods.

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