Matlab
MATLAB and GNU Octave numerical computing for matrix operations, data analysis...
Install
npx skills add zLanqing/codex-claude-academic-skills --skill matlab -g -a claude-code -y- Repository
- zLanqing/codex-claude-academic-skills5.5k
- Category
- Data and ML
- Updated
- 6mo ago
- License
- MIT
- Context cost
- Mediumabout 2,098 tokens
- Runs scripts
- No, instructions only
What it does
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
MATLAB/Octave Scientific Computing
MATLAB is a numerical computing environment optimized for matrix operations and scientific computing. GNU Octave is a free, open-source alternative with high MATLAB compatibility.
Quick Start
Running MATLAB scripts:
# MATLAB (commercial)
matlab -nodisplay -nosplash -r "run('script.m'); exit;"
# GNU Octave (free, open-source)
octave script.m
Install GNU Octave:
# macOS
brew install octave
# Ubuntu/Debian
sudo apt install octave
# Windows - download from https://octave.org/download
Core Capabilities
1. Matrix Operations
MATLAB operates fundamentally on matrices and arrays:
% Create matrices
A = [1 2 3; 4 5 6; 7 8 9]; % 3x3 matrix
v = 1:10; % Row vector 1 to 10
v = linspace(0, 1, 100); % 100 points from 0 to 1
% Special matrices
I = eye(3); % Identity matrix
Z = zeros(3, 4); % 3x4 zero matrix
O = ones(2, 3); % 2x3 ones matrix
R = rand(3, 3); % Random uniform
N = randn(3, 3); % Random normal
% Matrix operations
B = A'; % Transpose
C = A * B; % Matrix multiplication
D = A .* B; % Element-wise multiplication
E = A \ b; % Solve linear system Ax = b
F = inv(A); % Matrix inverse
For complete matrix operations, see references/matrices-arrays.md.
2. Linear Algebra
% Eigenvalues and eigenvectors
[V, D] = eig(A); % V: eigenvectors, D: diagonal eigenvalues
% Singular value decomposition
[U, S, V] = svd(A);
% Matrix decompositions
[L, U] = lu(A); % LU decomposition
[Q, R] = qr(A); % QR decomposition
R = chol(A); % Cholesky (symmetric positive definite)
% Solve linear systems
x = A \ b; % Preferred method
x = linsolve(A, b); % With options
x = inv(A) * b; % Less efficient
For comprehensive linear algebra, see references/mathematics.md.
