sympy-symbolic-math
Symbolic math in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, ODEs, code gen (lambdify, C/Fortran). Use for exact symbolic results. For numerical use numpy/scipy; for stats use statsmodels.
npx skills add BioTender-max/awesome-bio-agent-skills --skill sympy-symbolic-math --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 ## Overview SymPy is a Python library for symbolic mathematics that performs exact computation using mathematical symbols rather than numerical approximations. It covers algebra, calculus, equation solving, linear algebra, physics, and code generation — all within pure Python with no external dependencies. ## When to Use - Solving equations symbolically (algebraic, systems, differential equations) - Performing calculus operations (derivatives, integrals, limits, series expansions) - Simplifying and manipulating algebraic expressions - Working with matrices symbolically (eigenvalues, determinants, decompositions) - Converting symbolic expressions to fast numerical functions (lambdify → NumPy) - Generating code from math expressions (C, Fortran, LaTeX) - Needing exact results (e.g., `sqrt(2)` not `1.414...`) - For **numerical computing** (array operations, linear algebra on data), use numpy/scipy - For **statistical modeling** (regression, hypothesis testing), use statsmodels ## Prerequisites ```bash pip install sympy # Optional for numerical evaluation: pip install numpy matplotlib ``` SymPy is pure Python — no compiled dependencies, installs everywher
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- 1. Symbols and Expressions
- 2. Calculus
- 3. Equation Solving
- 4. Matrices and Linear Algebra
- 5. Code Generation
- 6. Physics Module
- Key Concepts
- Exact vs Numerical Arithmetic
- Solver Selection Guide
pip install sympy Optional for numerical evaluation: pip install numpy matplotlib
What does the sympy-symbolic-math skill do?
Symbolic math in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, ODEs, code gen (lambdify, C/Fortran). Use for exact symbolic results. For numerical use numpy/scipy; for stats use statsmodels.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill sympy-symbolic-math --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 BioTender-max/awesome-bio-agent-skills, a repository with 144 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.