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Sympy

K-DenseCommunityCreated by K-Dense Inc.

  • Skill
  • Tool integration
  • Science and research
  • MIT
  • Low risk

From the author

Performs exact symbolic mathematics with SymPy for algebra, calculus, equation solving, symbolic linear algebra, physics, and lambdify or LaTeX code generation. Use when a task needs symbolic results, explicit assumptions, or exact arithmetic; use NumPy or SciPy for purely numerical workloads.
— the description by K-Dense Inc.

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

SymPy is a Python library designed for symbolic mathematics, allowing for exact computations using mathematical symbols rather than numerical approximations. This skill provides guidance on using SymPy for algebraic manipulation, calculus, equation solving, linear algebra, physics calculations, and generating code or LaTeX output. It emphasizes the use of symbolic definitions and assumptions to maintain mathematical precision throughout a computation.

What it does

  • Performs symbolic algebra, calculus, and equation solving.
  • Handles symbolic linear algebra and matrix operations.
  • Supports physics and mechanics calculations.
  • Generates executable code in Python, C, and Fortran.
  • Produces formatted mathematical output including LaTeX.
  • Provides tools for number theory and geometric computations.

When to use it

  • When exact mathematical results are required instead of numerical approximations.
  • For solving algebraic, differential, or systems of equations symbolically.
  • When performing calculus operations like derivatives, integrals, or limits.
  • For converting symbolic expressions into executable code or formatted documentation.
  • When working with symbolic physics or geometry problems.

Advantages

  • Enables exact arithmetic and symbolic manipulation.
  • Supports explicit assumptions to simplify expressions correctly.
  • Integrates with scientific stacks like NumPy and SciPy for hybrid workflows.
  • Provides multiple solvers for different types of mathematical equations.
  • Facilitates the generation of LaTeX and source code from symbolic expressions.

Usage examples

“Solve the quadratic equation x^2 - 5x + 6 = 0 using SymPy.”
The agent defines the symbol x, uses the solve function to find the roots [2, 3], and returns the exact symbolic solutions.
“Calculate the derivative of sin(x^2) with respect to x.”
The agent uses the diff function to compute the derivative, returning 2*x*cos(x^2).
“Convert a symbolic expression x^2 + 2x + 1 into a NumPy-compatible function.”
The agent uses lambdify to create a callable function that can be evaluated efficiently over NumPy arrays.
“Find the eigenvalues of a 2x2 matrix symbolically.”
The agent constructs a Matrix object and uses the eigenvals method to return the symbolic eigenvalues.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/k-dense-ai/sympy/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip sympy.zip -d .agents/skills

Documentation by K-Dense Inc.

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

Reviewed against current official documentation and executed with SymPy 1.14.0 on Python 3.13.3 (2026-10-01). Core SymPy requires Python 3.9+; the tested NumPy 2.5.3 / SciPy 1.18.1 stack needs Python 3.12+. SymPy 1.14.0 requires mpmath>=1.1,<1.4; use the compatible 1.3.0, not the newer 1.4.x release. See verification and official sources for coverage.

# Install SymPy using uv
uv pip install "sympy==1.14.0"

# Optional: for lambdify and plotting examples
uv pip install numpy scipy matplotlib

Check your version:

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, 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/core_capabilities.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
from sympy import symbols
x, y, z = symbols('x y z')
# Now x, y, z can be used in expressions
2. Use Assumptions for Better Simplification
from sympy import symbols, sqrt
x = symbols('x', positive=True, real=True)
sqrt(x**2)  # Returns x (not Abs(x)) due to positive assumption

Common assumptions: real, positive, negative, integer, rational, complex, even, odd

3. Use Exact Arithmetic
from sympy import Rational, S
# Correct (exact):
expr = Rational(1, 2) * x
expr = S(1)/2 * x

# Approximate (appropriate for measured/numerical inputs):
expr = 0.5 * x  # Creates approximate value
4. Numerical Evaluation When Needed
from sympy import pi, sqrt
result = sqrt(8) + pi
result.evalf()    # 5.96371554103586
result.evalf(50)  # Request 50 decimal digits; cannot recover precision lost in inputs
5. Convert to NumPy for Performance
from sympy import symbols, lambdify
import numpy as np
x = symbols("x")
expr = x**2 + 1
# Slow for many evaluations:
for x_val in range(1000):
    result = expr.subs(x, x_val).evalf()

# Fast:
f = lambdify(x, expr, 'numpy')
results = f(np.arange(1000))
6. Use Appropriate Solvers
  • solveset: Algebraic equations (primary)

  • linsolve: Linear systems

  • nonlinsolve: Nonlinear systems

  • dsolve: Differential equations

  • solve: General purpose; supports some problems solveset does not

Declare the solution domain: solveset defaults to complex numbers, so use domain=S.Reals for real-only questions. A returned ConditionSet means an unresolved solution condition, not that no solutions exist; distinguish it from EmptySet. A numerical nsolve result is a local root found from a starting point, not proof that every root was found.

7. Preserve mathematical meaning and input trust

Use assumptions only when justified by the problem. An unconstrained symbol is complex; sqrt(x**2) need not equal x, and logarithm/power identities depend on branches. Assumption predicates can return None (unknown). Keep excluded denominator zeros when cancelling factors, and verify candidate solutions in the original expression and requested domain. == compares symbolic structure; use Eq to build an equation and targeted simplification to verify an identity.

parse_expr, string sympify, and lambdify can execute code. Accept only trusted expressions there. A regex, local_dict, or evaluate=False is not a security boundary; untrusted input needs a separate allowlisted grammar that constructs SymPy objects, plus resource limits. See the code-generation reference.

Reference Files Structure

This skill uses modular reference files for different capabilities:

  1. core-capabilities.md: Symbols, algebra, calculus, simplification, equation solving

    • Load when: Basic symbolic computation, calculus, or solving equations

  2. matrices-linear-algebra.md: Matrix operations, eigenvalues, linear systems

    • Load when: Working with matrices or linear algebra problems

  3. physics-mechanics.md: Classical mechanics, quantum mechanics, vectors, units

    • Load when: Physics calculations or mechanics problems

  4. advanced-topics.md: Geometry, number theory, combinatorics, logic, statistics

    • Load when: Advanced mathematical topics beyond basic algebra and calculus

  5. code-generation-printing.md: Lambdify, codegen, LaTeX output, printing

    • Load when: Converting expressions to code or generating formatted output

Common Use Case Patterns

Pattern 1: Solve and Verify
from sympy import symbols, solve, simplify
x = symbols('x')

# Solve equation
equation = x**2 - 5*x + 6
solutions = solve(equation, x)  # [2, 3]

# Verify solutions
for sol in solutions:
    result = simplify(equation.subs(x, sol))
    assert result == 0
Pattern 2: Symbolic to Numeric Pipeline
from sympy import symbols, sin, cos, simplify, diff, lambdify
import numpy as np
x_data = np.linspace(0, 1, 5)
y_data = np.linspace(1, 2, 5)
# 1. Define symbolic problem
x, y = symbols('x y')
expr = sin(x) + cos(y)

# 2. Manipulate symbolically
simplified = simplify(expr)
derivative = diff(simplified, x)

# 3. Convert to numerical function
f = lambdify((x, y), derivative, 'numpy')

# 4. Evaluate numerically
results = f(x_data, y_data)
Pattern 3: Document Mathematical Results
from sympy import symbols, Integral, latex, pretty
x = symbols("x")
# Compute result symbolically
integral_expr = Integral(x**2, (x, 0, 1))
result = integral_expr.doit()

# Generate documentation
print(f"LaTeX: {latex(integral_expr)} = {latex(result)}")
print(f"Pretty: {pretty(integral_expr)} = {pretty(result)}")
print(f"Numerical: {result.evalf()}")

Integration with Scientific Workflows

With NumPy
import numpy as np
from sympy import symbols, lambdify

x = symbols('x')
expr = x**2 + 2*x + 1

f = lambdify(x, expr, 'numpy')
x_array = np.linspace(-5, 5, 100)
y_array = f(x_array)
With Matplotlib
import matplotlib.pyplot as plt
import numpy as np
from sympy import symbols, lambdify, sin

x = symbols('x')
expr = sin(x) / x

f = lambdify(x, expr, 'numpy')
x_vals = np.linspace(-10, 10, 1000)
y_vals = f(x_vals)

plt.plot(x_vals, y_vals)
plt.show()
With SciPy
from scipy.optimize import fsolve
from sympy import symbols, lambdify

# Define equation symbolically
x = symbols('x')
equation = x**3 - 2*x - 5

# Convert to numerical function
f = lambdify(x, equation, 'numpy')

# Solve numerically with initial guess
solution, info, status, message = fsolve(f, 2, full_output=True)
assert status == 1, message
assert abs(f(solution[0])) < 1e-10
# A converged local root is not a complete root set.

Quick Reference: Most Common Functions

# Symbols
from sympy import symbols, Symbol
x, y = symbols('x y')

# Basic operations
from sympy import simplify, expand, factor, collect, cancel
from sympy import sqrt, exp, log, sin, cos, tan, pi, E, I, oo

# Calculus
from sympy import diff, integrate, limit, series, Derivative, Integral

# Solving
from sympy import solve, solveset, linsolve, nonlinsolve, dsolve

# Matrices
from sympy import Matrix, eye, zeros, ones, diag

# Logic and sets
from sympy import And, Or, Not, Implies, FiniteSet, Interval, Union

# Output
from sympy import latex, pprint, lambdify, init_printing

# Utilities
from sympy import N, nsimplify  # expr.evalf() is a method

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, oo
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])

Troubleshooting Common Issues

  1. "NameError: name 'x' is not defined"

    • Solution: Always define symbols using symbols() before use

  2. Unexpected numerical results

    • Issue: Using floating-point numbers like 0.5 instead of Rational(1, 2)

    • Solution: Use Rational() or S() for exact arithmetic

  3. Slow performance in loops

    • Issue: Using subs() and evalf() repeatedly

    • Solution: Use lambdify() to create a fast numerical function

  4. "Can't solve this equation"

    • Try different solvers: solve, solveset, nsolve (numerical)

    • Check if the equation is solvable algebraically

    • Use numerical methods if no closed-form solution exists

  5. Simplification not working as expected

    • Try different simplification functions: simplify, factor, expand, trigsimp

    • State justified assumptions at symbol creation (e.g., positive=True)

    • Prefer targeted cancel, factor, or trigsimp. simplify has no general branch-safe force=True mode; forced power/log rewrites can change the result

Additional Resources

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

Files

references/advanced-topics.md

SymPy Advanced Topics

Examples in this reference are ordered session fragments: run earlier imports and setup first. Tested with SymPy 1.14.0; exceptions are explicitly marked illustrative.

This document covers SymPy's advanced mathematical capabilities including geometry, number theory, combinatorics, logic and sets, statistics, polynomials, and special functions.

Geometry

2D Geometry
from sympy.geometry import Point, Line, Circle, Triangle, Polygon

# Points
p1 = Point(0, 0)
p2 = Point(1, 1)
p3 = Point(1, 0)

# Distance between points
dist = p1.distance(p2)

# Lines
line = Line(p1, p2)
line_from_eq = Line(Point(0, 0), slope=2)

# Line properties
line.slope       # Slope
line.equation()  # Equation of line
line.length      # oo (infinite for lines)

# Line segment
from sympy.geometry import Segment
seg = Segment(p1, p2)
seg.length       # Finite length
seg.midpoint     # Midpoint

# Intersection
line2 = Line(Point(0, 1), Point(1, 0))
intersection = line.intersection(line2)  # [Point(1/2, 1/2)]

# Circles
circle = Circle(Point(0, 0), 5)  # Center, radius
circle.area           # 25*pi
circle.circumference  # 10*pi

# Triangles
tri = Triangle(p1, p2, p3)
tri.area       # Area
tri.perimeter  # Perimeter
tri.angles     # Dictionary of angles
tri.vertices   # Tuple of vertices

# Polygons
poly = Polygon(Point(0, 0), Point(1, 0), Point(1, 1), Point(0, 1))
poly.area
poly.perimeter
poly.vertices
Geometric Queries
# Check if point is on line/curve
from sympy import Rational
point = Point(Rational(1, 2), Rational(1, 2))
line.contains(point)

# Check if parallel/perpendicular
line1 = Line(Point(0, 0), Point(1, 1))
line2 = Line(Point(0, 1), Point(1, 2))
line1.is_parallel(line2)  # True
line1.is_perpendicular(line2)  # False

# Tangent lines
from sympy.geometry import Circle, Point
circle = Circle(Point(0, 0), 5)
point = Point(5, 0)
tangents = circle.tangent_lines(point)
3D Geometry
from sympy.geometry import Point3D, Line3D, Plane

# 3D Points
p1 = Point3D(0, 0, 0)
p2 = Point3D(1, 1, 1)
p3 = Point3D(1, 0, 0)

# 3D Lines
line = Line3D(p1, p2)

# Planes
plane = Plane(p1, p2, p3)  # From 3 points
plane = Plane(Point3D(0, 0, 0), normal_vector=(1, 0, 0))  # From point and normal

# Plane equation
plane.equation()

# Distance from point to plane
point = Point3D(2, 3, 4)
dist = plane.distance(point)

# Intersection of plane and line
intersection = plane.intersection(line)
Curves and Ellipses
from sympy.geometry import Ellipse, Curve
from sympy import sin, cos, pi

# Ellipse
ellipse = Ellipse(Point(0, 0), hradius=3, vradius=2)
ellipse.area          # 6*pi
ellipse.eccentricity  # Eccentricity

# Parametric curves
from sympy.abc import t
curve = Curve((cos(t), sin(t)), (t, 0, 2*pi))  # Circle

Number Theory

Prime Numbers
from sympy.ntheory import isprime, primerange, prime, nextprime, prevprime

# Check if prime
isprime(7)    # True
isprime(10)   # False
# Results are definitive below 2**64; larger inputs use a probable-prime test.

# Generate primes in range
list(primerange(10, 50))  # [11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]

# nth prime
prime(10)     # 29 (10th prime)

# Next and previous primes
nextprime(10)  # 11
prevprime(10)  # 7
Prime Factorization
from sympy import factorint, primefactors, divisors

# Prime factorization
factorint(60)  # {2: 2, 3: 1, 5: 1} means 2^2 * 3^1 * 5^1

# List of prime factors
primefactors(60)  # [2, 3, 5]

# All divisors
divisors(60)  # [1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 60]
GCD and LCM
from sympy import gcd, lcm, igcd, ilcm

# Greatest common divisor
gcd(60, 48)   # 12
igcd(60, 48)  # 12 (integer version)

# Least common multiple
lcm(60, 48)   # 240
ilcm(60, 48)  # 240 (integer version)

# Multiple arguments
igcd(60, 48, 36)  # 12; gcd accepts two expressions or a sequence
Modular Arithmetic
from sympy import mod_inverse, totient
from sympy.ntheory import is_primitive_root

# Modular inverse (find x such that a*x ≡ 1 (mod m))
mod_inverse(3, 7)  # 5 (because 3*5 = 15 ≡ 1 (mod 7))

# Euler's totient function
totient(10)  # 4 (numbers less than 10 coprime to 10: 1,3,7,9)

# Primitive roots
is_primitive_root(2, 5)  # True
Diophantine Equations
from sympy.solvers.diophantine import diophantine
from sympy.abc import x, y, z

# Linear Diophantine: ax + by = c
diophantine(3*x + 4*y - 5)  # {(4*t_0 - 5, -3*t_0 + 5)}

# Quadratic forms
diophantine(x**2 + y**2 - 25)  # Pythagorean-type equations

# More complex equations
diophantine(x**2 - 4*x*y + 8*y**2 - 3*x + 7*y - 5)
Continued Fractions
from sympy import nsimplify, continued_fraction_iterator
from sympy import Rational, pi

# Convert to continued fraction
cf = continued_fraction_iterator(Rational(415, 93))
list(cf)  # [4, 2, 6, 7]

# Approximate irrational numbers
cf_pi = continued_fraction_iterator(pi.evalf(20))

Combinatorics

Permutations and Combinations
from sympy import factorial, binomial, factorial2
from sympy.functions.combinatorial.numbers import nC, nP

# Factorial
factorial(5)  # 120

# Binomial coefficient (n choose k)
binomial(5, 2)  # 10

# Permutations nPk = n!/(n-k)!
nP(5, 2)  # 20

# Combinations nCk = n!/(k!(n-k)!)
nC(5, 2)  # 10

# Double factorial n!!
factorial2(5)  # 15 (5*3*1)
factorial2(6)  # 48 (6*4*2)
Permutation Objects
from sympy.combinatorics import Permutation

# Create permutation (cycle notation)
p = Permutation([1, 2, 0, 3])  # Sends 0->1, 1->2, 2->0, 3->3
p = Permutation(0, 1, 2, size=4)    # Cycle notation: (0 1 2)(3)

# Permutation operations
p.order()       # Order of permutation
p.is_even       # True if even permutation
p.inversions()  # Number of inversions

# Compose permutations
q = Permutation([2, 0, 1, 3])
r = p * q       # Composition: (p*q)(i) == q(p(i))
Partitions
from sympy.utilities.iterables import partitions
from sympy.functions.combinatorial.numbers import partition

# Number of integer partitions
partition(5)  # 7 (5, 4+1, 3+2, 3+1+1, 2+2+1, 2+1+1+1, 1+1+1+1+1)

# Generate all partitions
list(partitions(4))
# {4: 1}, {3: 1, 1: 1}, {2: 2}, {2: 1, 1: 2}, {1: 4}
Catalan and Fibonacci Numbers
from sympy import catalan, fibonacci, lucas

# Catalan numbers
catalan(5)  # 42

# Fibonacci numbers
fibonacci(10)  # 55
lucas(10)      # 123 (Lucas numbers)
Group Theory
from sympy.combinatorics import PermutationGroup, Permutation

# Create permutation group
p1 = Permutation([1, 0, 2])
p2 = Permutation([0, 2, 1])
G = PermutationGroup(p1, p2)

# Group properties
G.order()        # Order of group
G.is_abelian     # Check if abelian
G.is_cyclic      # Boolean property
G.elements       # All group elements

Logic and Sets

Boolean Logic
from sympy import symbols, And, Or, Not, Xor, Implies, Equivalent
from sympy.logic.boolalg import truth_table, simplify_logic

# Define boolean variables
x, y, z = symbols('x y z')

# Logical operations
expr = And(x, Or(y, Not(z)))
expr = Implies(x, y)  # x -> y
expr = Equivalent(x, y)  # x <-> y
expr = Xor(x, y)  # Exclusive OR

# Simplification
expr = (x & y) | (x & ~y)
simplified = simplify_logic(expr)  # Returns x

# Truth table
expr = Implies(x, y)
print(list(truth_table(expr, [x, y])))
Sets
from sympy import FiniteSet, Interval, Union, Intersection, Complement
from sympy import S  # For special sets

# Finite sets
A = FiniteSet(1, 2, 3, 4)
B = FiniteSet(3, 4, 5, 6)

# Set operations
union = Union(A, B)              # {1, 2, 3, 4, 5, 6}
intersection = Intersection(A, B)  # {3, 4}
difference = Complement(A, B)     # {1, 2}

# Intervals
I = Interval(0, 1)              # [0, 1]
I_open = Interval.open(0, 1)    # (0, 1)
I_lopen = Interval.Lopen(0, 1)  # (0, 1]
I_ropen = Interval.Ropen(0, 1)  # [0, 1)

# Special sets
S.Reals        # All real numbers
S.Integers     # All integers
S.Naturals     # Natural numbers
S.EmptySet     # Empty set
S.Complexes    # Complex numbers

# Set membership
3 in A  # True
7 in A  # False

# Subset and superset
A.is_subset(B)    # False
A.is_superset(B)  # False
Set Theory Operations
from sympy import ImageSet, Lambda
from sympy.abc import x

# Image set (set of function values)
squares = ImageSet(Lambda(x, x**2), S.Integers)
# {x^2 | x ∈ ℤ}

# Power set
from sympy.sets import FiniteSet
A = FiniteSet(1, 2, 3)
power_set = A.powerset()  # FiniteSet of all eight subsets

Polynomials

Polynomial Manipulation
from sympy import Poly, symbols, factor, expand, roots, div
x, y = symbols('x y')

# Create polynomial
p = Poly(x**2 + 2*x + 1, x)

# Polynomial properties
p.degree()       # 2
p.coeffs()       # [1, 2, 1]
p.as_expr()      # Convert back to expression

# Arithmetic
p1 = Poly(x**2 + 1, x)
p2 = Poly(x + 1, x)
p3 = p1 + p2
p4 = p1 * p2
q, r = div(p1, p2)  # Quotient and remainder
Polynomial Roots
from sympy import roots, real_roots, count_roots

p = Poly(x**3 - 6*x**2 + 11*x - 6, x)

# Roots expressible by radicals; complete for this cubic
r = roots(p)  # {1: 1, 2: 1, 3: 1}
assert sum(r.values()) == p.degree()
# roots() can return a partial dictionary or {} for irreducible quintics.
all_roots = p.all_roots()  # Includes exact CRootOf objects where necessary

# Real roots only
r = real_roots(p)

# Count roots in interval
count_roots(p, 0, 4)  # 3 distinct roots in this closed interval
Polynomial GCD and Factorization
from sympy import gcd, lcm, factor, factor_list

p1 = Poly(x**2 - 1, x)
p2 = Poly(x**2 - 2*x + 1, x)

# GCD and LCM
g = gcd(p1, p2)
l = lcm(p1, p2)

# Factorization
f = factor(x**3 - x**2 + x - 1)  # (x - 1)*(x**2 + 1)
factors = factor_list(x**3 - x**2 + x - 1)  # List form
Groebner Bases
from sympy import groebner, symbols

x, y, z = symbols('x y z')
polynomials = [x**2 + y**2 + z**2 - 1, x*y - z]

# Compute Groebner basis
gb = groebner(polynomials, x, y, z)

Statistics

Random Variables
from sympy.stats import (
    Normal, Uniform, Exponential, Poisson, Binomial,
    P, E, variance, density, sample
)

# Define random variables
X = Normal('X', 0, 1)  # Normal(mean, std)
Y = Uniform('Y', 0, 1)  # Uniform(a, b)
Z = Exponential('Z', 1)  # Exponential(rate)

# Probability
P(X > 0)  # 1/2
P((X > 0) & (X < 1))

# Expected value
E(X)  # 0
E(X**2)  # 1

# Variance
variance(X)  # 1

# Density function
density(X)(x)  # sqrt(2)*exp(-x**2/2)/(2*sqrt(pi))
Discrete Distributions
from sympy.stats import Die, Bernoulli, Binomial, Poisson
from sympy import Eq, Rational

# Die
D = Die('D', 6)
P(D > 3)  # 1/2

# Bernoulli
B = Bernoulli('B', Rational(1, 2))
P(Eq(B, 1))  # 1/2

# Binomial
X = Binomial('X', 10, Rational(1, 2))
P(Eq(X, 5))  # 63/256; X == 5 is Python structural equality, not an event

# Poisson
Y = Poisson('Y', 3)
P(Y < 2)  # Probability of less than 2 events
Joint Distributions
from sympy.stats import Normal, P, E
from sympy import symbols

# Independent random variables
X = Normal('X', 0, 1)
Y = Normal('Y', 0, 1)

# Joint probability
P(X > 0) * P(Y > 0)  # 1/4, because these separately created variables are independent
# SymPy 1.14.0 can fail on P((X > 0) & (Y > 0)) for independent normals.
# Do not factor joint probabilities when variables are dependent.

# Covariance
from sympy.stats import covariance
covariance(X, Y)  # 0 (independent)

Special Functions

Common Special Functions
from sympy import (
    gamma,      # Gamma function
    beta,       # Beta function
    erf,        # Error function
    besselj,    # Bessel function of first kind
    bessely,    # Bessel function of second kind
    hermite,    # Hermite polynomial
    legendre,   # Legendre polynomial
    laguerre,   # Laguerre polynomial
    chebyshevt, # Chebyshev polynomial (first kind)
    zeta        # Riemann zeta function
)

# Gamma function
gamma(5)  # 24 (equivalent to 4!)
gamma(Rational(1, 2))  # sqrt(pi); Python 1/2 would produce a Float

# Bessel functions
besselj(0, x)  # J_0(x)
bessely(1, x)  # Y_1(x)

# Orthogonal polynomials
hermite(3, x)    # 8*x**3 - 12*x
legendre(2, x)   # (3*x**2 - 1)/2
laguerre(2, x)   # x**2/2 - 2*x + 1
chebyshevt(3, x) # 4*x**3 - 3*x
Hypergeometric Functions
from sympy import hyper, meijerg

# Hypergeometric function
hyper([1, 2], [3], x)

# Meijer G-function
meijerg([[1, 1], []], [[1], [0]], x)

Common Patterns

Pattern 1: Symbolic Geometry Problem
from sympy.geometry import Point, Triangle
from sympy import symbols

# Define symbolic triangle
a, b = symbols('a b', positive=True)
tri = Triangle(Point(0, 0), Point(a, 0), Point(0, b))

# Compute properties symbolically
area = tri.area  # a*b/2
perimeter = tri.perimeter  # a + b + sqrt(a**2 + b**2)
Pattern 2: Number Theory Calculation
from sympy import factorint, totient, isprime

# Factor and analyze
n = 12345
factors = factorint(n)
phi = totient(n)
is_prime = isprime(n)
Pattern 3: Combinatorial Generation
from sympy.utilities.iterables import multiset_permutations
from itertools import combinations

# Generate all permutations
perms = list(multiset_permutations([1, 2, 3]))

# Generate combinations
combs = list(combinations([1, 2, 3, 4], 2))
Pattern 4: Probability Calculation
from sympy.stats import Normal, P, E, variance

from sympy import symbols
mu, a = symbols('mu a', real=True)
sigma = symbols('sigma', positive=True)
X = Normal('X', mu, sigma)

# Compute statistics
mean = E(X)
var = variance(X)
prob = P(X > a)

Important Notes

  1. Assumptions: Many operations benefit from symbol assumptions (e.g., positive=True, integer=True).

  2. Symbolic vs Numeric: These operations are symbolic. Use evalf() for numerical results.

  3. Performance: Complex symbolic operations can be slow. Consider numerical methods for large-scale computations.

  4. Exact arithmetic: SymPy maintains exact representations (e.g., sqrt(2) instead of 1.414...).

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs network access, Needs Python

The author says: Requires Python 3.9+ and SymPy 1.14.0. Optional NumPy/SciPy/Matplotlib, IPython/ipywidgets, or ANTLR 4.11 parser runtime for relevant examples. Compiled wrappers need a C/Fortran compiler and backend packages; emitting source needs no compiler. Network only for installation/docs.

Security

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  • Instructions that use the network

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Attribution and licence

Created by
K-Dense Inc.
Publisher
K-Dense (Community)
Source
K-Dense-AI/scientific-agent-skills · skills/sympy · commit a25cf9e
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE.md)
  • ATTRIBUTION.md (written by AutoGineer)
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