Agent skill · research science · k-dense-ai
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.
Why this skill is useful
Provides detailed domain-specific knowledge on symbolic mathematics operations and code generation that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, scipy, sympy installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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+. Check your 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, 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 Seven capability areas are documented in references/corecapabilities.md: 1. Symbolic computation basics — symbols, expressions, simplification, substitution. 2. Calculus — differentiation, integration, limits, series. 3. Equation solving — solve, solveset, linear and nonlinear systems, ODEs. 4. Matrices and linear algebra — see references/matrices-linear-algebra.md. 5. Physics and mechanics — see references/physics-mechanics.md. 6. Advanced mathematics — see references/advanced-topics.md. 7. Code generation and output — see references/code-generation-printing.md. Deeper treatment of the first three is in references/core-capabilities.md. Working with SymPy: Best Practices 1. Always Define Symbols First 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/sympy