sympy-computer-algebra

Symbolic computation using SymPy for Python-based mathematical analysis

509 stars

Best use case

sympy-computer-algebra is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Symbolic computation using SymPy for Python-based mathematical analysis

Teams using sympy-computer-algebra should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/sympy-computer-algebra/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/sympy-computer-algebra/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/sympy-computer-algebra/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How sympy-computer-algebra Compares

Feature / Agent	sympy-computer-algebra	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Symbolic computation using SymPy for Python-based mathematical analysis

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# SymPy Computer Algebra

## Purpose

Provides symbolic computation capabilities using SymPy for Python-based mathematical analysis and manipulation.

## Capabilities

- Symbolic differentiation and integration
- Equation solving (algebraic, differential)
- Series expansion and limits
- Matrix algebra and linear algebra
- Pattern matching and simplification
- Code generation (NumPy, C, Fortran)

## Usage Guidelines

1. **Symbol Definition**: Define symbols with appropriate assumptions
2. **Expression Building**: Construct symbolic expressions
3. **Simplification**: Apply appropriate simplification strategies
4. **Code Generation**: Export to efficient numerical code

## Tools/Libraries

- SymPy
- NumPy
- mpmath

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