sympy-symbolic

Derive symbolic physics equations using SymPy for mechanics and calculus.

33|6|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill sympy-symbolic-xjtulyc
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sympy-symbolic
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/01-physics/sympy-symbolic
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill sympy-symbolic-xjtulyc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sympy, numpy, matplotlib, scipy.

What problem does it solve?

It helps you derive, manipulate, and solve physics equations symbolically instead of relying only on numerical approximations, making it easier to validate derivations and obtain closed-form results.

Core Features & Use Cases

  • Symbolic algebra for physics: expand, factor, simplify (including trig), and perform power-series work on physics-relevant expressions.
  • Symbolic calculus and transforms: differentiate, integrate (definite/indefinite/parameterized), compute limits, and build Fourier series components via symbolic integration.
  • Symbolic physics workflows: solve ODEs analytically, perform linear-algebra operations relevant to mechanics/quantum mechanics, derive equations of motion using Lagrangian mechanics (including double pendulum examples), and generate fast numerical code from symbolic expressions using lambdify and CSE.
  • Use Case: Derive equations of motion for a nonlinear mechanical system (e.g., a double pendulum), linearize them for small angles, and then turn the symbolic expressions into efficient numerical functions for simulation or plotting.

Quick Start

Use this skill to derive the symbolic equations of motion for a double pendulum and output both the full nonlinear EOM and their small-angle linearized form.

Frequently Asked Questions about sympy-symbolic

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

FAQPage Schema
How do I derive equations of motion using Lagrangian mechanics in Python?

Yes, you can solve ordinary differential equations symbolically to obtain closed-form analytical solutions. This symbolic ODE-solving capability handles physics derivations directly, avoiding numerical approximations and validating exact mathematical results.

What's the best way to convert symbolic physics expressions into numerical code?

Yes, symbolic computation handles linear algebra and matrix operations relevant to mechanics and quantum mathematics. It performs calculus differentiation, integration, and Fourier series coefficient calculation to validate complex physics derivations analytically.

Does symbolic computation require any specific Python libraries for physics workflows?

Symbolic computation is ideal when you need exact closed-form results and validated physics derivations rather than numerical approximations. It is best suited for deriving equations of motion, performing linear algebra, and calculating Fourier series components analytically.

How do I calculate Fourier series coefficients using symbolic integration?

Symbolic computation performs Maxwell-style vector calculus checks and power-series expansions on physics-relevant expressions. It applies trigonometric simplification and assumption-based logic to validate complex algebraic derivations analytically.