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sympy

Lets your agent perform exact mathematical calculations using symbols. It doesn't use approximate numbers, ensuring precision. Useful for complex math problems.

What it does

This tool helps perform exact mathematical calculations using symbols, like in a math notebook, instead of just numbers. It can solve equations, find derivatives, integrals, and more.

When to use it

  • ✓Solving complex equations without approximating numbers.
  • ✓Calculating derivatives or integrals of functions.
  • ✓Working with matrices exactly.

Example of what to ask your AI

“Solve the equation x^2 - 4 = 0 for x.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/sympy/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/sympy/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

You tell me the math problem you want to solve, and I use a special library called SymPy to find the exact answer. It understands symbols like 'x' and 'y' to do the calculations.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill sympy

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/sympy/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

SymPy - Symbolic Mathematics in Python

Detailed Guide

Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

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...)

Getting Started Examples

Example 1: Solve Quadratic Equation

from sympy import symbols, solve, sqrt
x = symbols('x')
solution = solve(x**2 - 5*x + 6, x)
# [2, 3]

Example 2: Calculate Derivative

from sympy import symbols, diff, sin
x = symbols('x')
f = sin(x**2)
df_dx = diff(f, x)
# 2*x*cos(x**2)

Example 3: Evaluate Integral

from sympy import symbols, integrate, exp
x = symbols('x')
integral = integrate(x * exp(-x**2), (x, 0, oo))
# 1/2

Example 4: Matrix Eigenvalues

from sympy import Matrix
M = Matrix([[1, 2], [2, 1]])
eigenvals = M.eigenvals()
# {3: 1, -1: 1}

Example 5: Generate Python Function

from sympy import symbols, lambdify
import numpy as np
x = symbols('x')
expr = x**2 + 2*x + 1
f = lambdify(x, expr, 'numpy')
f(np.array([1, 2, 3]))
# array([ 4,  9, 16])

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
FAQ

What are 'symbolic calculations'?

It's doing math using letters and symbols (like 'x', 'y', 'sqrt(2)') instead of just approximate numbers (like 1.414 for the square root of 2). This ensures the answer is exact.

Can I use it for any kind of math?

It's great for algebra, calculus, linear algebra, and some areas of physics and number theory. It's not for complex statistics or machine learning.

Do I need to know how to code in Python?

No, you just need to tell me what you want in plain language. I'll handle the technical part of using SymPy.

Details

Original description by the author

SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#4302
License
MIT
Updated
10/15/2026

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