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General coding51k · #1904

matlab

Lets your agent build, review, and plan numerical workflows in MATLAB or GNU Octave. It handles data, tests, and graphics.

What it does

This tool helps you work with programming codes called MATLAB and GNU Octave. It can review, organize, and plan how these codes function, especially for numerical calculations and data manipulation.

When to use it

  • ✓When you need to check if MATLAB/Octave code is correct.
  • ✓When planning how to organize a numerical programming project.
  • ✓If you need to move code from an old version to a new one.

Example of what to ask your AI

“Review this MATLAB code to ensure it correctly calculates the average of a dataset.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/matlab/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/matlab/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI analyzes your code, checks if it follows best practices and safety rules. It doesn't run the code directly but examines it to find potential issues or improvements.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add K-Dense-AI/scientific-agent-skills --skill matlab

Or download just the file:

curl -L https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/matlab/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

MATLAB and GNU Octave

Use this skill to design or review numerical code, migrate MATLAB releases, prepare reproducible projects, and plan trusted execution. MATLAB and GNU Octave are distinct products: compatibility is partial, not a license or behavior guarantee.

Review scope

Reviewed against current MathWorks R2026b documentation and GNU Octave 11.3.0 sources on 2026-10-01. The bundled Engine planner and MATLAB examples retain an explicit R2026a baseline. R2026b supports CPython 3.10-3.14; do not reuse the R2026a Engine package for it. MATLAB/Octave examples are illustrative and were not executed in this review; the Python helpers have local synthetic tests. This skill uses local runtime APIs, with no remote service endpoint or API key.

Product and license gate

  • MATLAB R2026a is proprietary. Do not assume MATLAB, MATLAB Online, a named toolbox, MATLAB Test, MATLAB Compiler, MATLAB Coder, Parallel Computing Toolbox, or an add-on is installed, licensed, or available to the user.
  • MATLAB Runtime is not MATLAB. It runs compatible applications produced with MATLAB Compiler; it cannot run arbitrary source or host MATLAB Engine for Python. Building artifacts needs the applicable licensed compiler and every product used by the source.
  • GNU Octave 11.3.0 is free software under GPLv3+. Octave packages are not MATLAB toolboxes. Similar names do not imply API, numerical, graphics, or licensing equivalence.
  • Ask which runtime, release, platform, installed products, and license context the user actually has. Treat availability as unknown until confirmed.

See Octave compatibility and execution/product boundaries.

Nonnegotiable safety boundary

Never run an untrusted .m, .mlx, MEX binary, MAT file, project startup or shutdown action, package installer, or generated artifact. Static review does not prove safety.

Treat these as execution or code-loading surfaces:

  • eval, evalin, assignin, text-derived feval, str2func, callbacks, timers, app callbacks, and dynamically modified paths;
  • system, unix, dos, shell escape !, Java, .NET, Python (py.*, pyrun, pyrunfile), MEX, and native libraries;
  • mex, codegen, MATLAB Compiler, build tasks, package/project startup, and generated code;
  • load, object deserialization (loadobj, custom serialization), function handles, Java/System objects, and class code reachable from MAT files.

.mlx is an opaque archive for this toolkit and MEX is native executable code. Do not use Python pickle for exchange. Inspect first, isolate when appropriate, obtain explicit approval, then invoke a user-confirmed executable and license. Bundled scripts are static or dry-run tools: none launches MATLAB, Octave, Python Engine, a compiler, or a subprocess.

Default workflow

  1. Clarify target. Record MATLAB release or Octave version, OS/architecture, base product versus required toolboxes/packages, expected inputs/outputs, numerical tolerances, and whether execution is authorized.
  2. Inventory statically. Scan .m files, opaque artifacts, project paths, required products, and MAT headers before any runtime loads them.
  3. Choose code form. Prefer functions with an arguments block for automation. Use scripts only for controlled orchestration and live scripts for reviewed interactive narratives.
  4. Make semantics explicit. Record shapes, classes, units, missing-value rules, indexing, implicit expansion, RNG algorithm/seed, tolerances, and output formats.
  5. Test without hidden state. Keep fixtures synthetic, paths project-local, graphics deterministic, and tests independent of base-workspace residue.
  6. Plan execution. Generate an argv plan, review startup/path effects and licenses, and launch only after explicit approval outside these helpers.
  7. Capture provenance. Hash named inputs/code and record release, products, RNG policy, tolerances, and command plan without dumping the environment.

Language and data checklist

Scripts, functions, and live scripts

  • Scripts share the caller/base workspace and leave variables behind. Functions have local workspaces and explicit inputs/outputs.
  • Live scripts (.mlx) mix code and rich output but are not plain-text review artifacts. Export reviewed code to .m for static inspection.

…

FAQ

What is the difference between MATLAB and GNU Octave?

MATLAB is a paid, proprietary software, while GNU Octave is a free, open-source alternative. Although they are similar, they are not identical and may have compatibility differences.

Can the AI run my code?

No, this tool is designed for analysis and planning, not for executing code. It safely inspects the code without running it.

What are 'numerical workflows'?

These are sequences of steps or instructions used in programming to perform mathematical calculations and data analysis, especially with numbers.

Details

Original description by the author

Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

repo: K-Dense-AI/scientific-agent-skills

Popularity

GitHub stars
51k
Ranking position
#1904
License
MIT
Updated
10/15/2026

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