sympy
Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.
npx skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. ## Installation Tested against **SymPy 1.14.0** (stable; April 2025). Requires **Python 3.9+**. ```bash # Install SymPy using uv uv pip install "sympy>=1.14" # Optional: for lambdify and plotting examples uv pip install numpy scipy matplotlib ``` Check your version: ```python import sympy print(sympy.__version__) ``` ## 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, ana
- Overview
- Installation
- When to Use This Skill
- Core Capabilities
- Working with SymPy: Best Practices
- 1. Always Define Symbols First
- 2. Use Assumptions for Better Simplification
- 3. Use Exact Arithmetic
- 4. Numerical Evaluation When Needed
- 5. Convert to NumPy for Performance
- 6. Use Appropriate Solvers
- Reference Files Structure
- Common Use Case Patterns
- Pattern 1: Solve and Verify
Install SymPy using uv uv pip install "sympy>=1.14" uv pip install numpy scipy matplotlib
What does the sympy skill do?
Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill sympy --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.