# Sympy

Use this skill when working with symbolic mathematics in Python.

- Repository: https://github.com/zLanqing/codex-claude-academic-skills (5550 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-05-14
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add zLanqing/codex-claude-academic-skills --skill sympy -g -a claude-code -y`
Codex: `npx skills add zLanqing/codex-claude-academic-skills --skill sympy -g -a codex -y`
Cursor: `npx skills add zLanqing/codex-claude-academic-skills --skill sympy -g -a cursor -y`
Gemini CLI: `npx skills add zLanqing/codex-claude-academic-skills --skill sympy -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:**
```python
from sympy import symbols, Symbol
x, y, z = symbols('x y z')
expr = x**2 + 2*x + 1

# With assumptions
x = symbols('x', real=True, positive=True)
n = symbols('n', integer=True)
```

**Simplification and manipulation:**
```python
from sympy import simplify, expand, factor, cancel
simplify(sin(x)**2 + cos(x)**2)  # Returns 1
expand((x + 1)**3)  # x**3 + 3*x**2 + 3*x + 1
factor(x**2 - 1)    # (x - 1)*(x + 1)
```

**For detailed basics:** See `references/core-capabilities.md`

### 2. Calculus

**Derivatives:**
```python
from sympy import diff
diff(x**2, x)        # 2*x
diff(x**4, x, 3)     # 24*x (third derivative)
diff(x**2*y**3, x, y)  # 6*x*y**2 (partial derivatives)
```

The rest: https://github.com/zLanqing/codex-claude-academic-skills/blob/HEAD/scientific-toolkit-skill/references/scientific-skills/sympy/SKILL.md