matlab
This skill provides MATLAB and GNU Octave capabilities for matrix operations, linear algebra, signal processing, image processing, and scientific visualization. Use it when writing numerical computing scripts for solving linear systems, performing eigenvalue decompositions, creating scientific plots, or converting between MATLAB and Python code.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/matlab && cp -r /tmp/matlab/skills/matlab ~/.claude/skills/matlabSKILL.md
# 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.
## 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](references/octave-compatibility.md) and
[execution/product boundaries](references/executing-scripts.md).
## 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.
- Avoid `clear all`, broad `addpath(genpath(...))`, dependence on `pwd`, global
variables, and silent name shadowing. Use project roots and `fullfile`.
- Validate sizes, classes, and values in `arguments` blocks. Remember that
type declarations can convert inputs; validators check without converting.
- A main function file should match the main function name. Local functions
are private to the file; since R2024a they can appear anywhere in a script
outside conditional contexts.
```matlab
function y = scaleSignal(x, options)
arguments
x (:,1) double {mustBeFinite}
options.Scale (1,1) double {mustBeFinite, mustBeNonzero} = 1
end
y = x .* options.Scale;
end
```
Read [programming](references/programming.md).
### Arrays, indexing, and numerics
- MATLAB uses 1-based, column-major indexing. `A(i,j)`, `A(k)`, `A(:,j)`,
`A{...}`, and `A.(name)` have different semantics.
- `*`, `/`, `\`, and `^` are matrix operations; dotted forms are
element-wise. Use `A\b`, not `inv(A)*b`.
- Since R2016b, compatible dimensions expand implicitly. Assert intended shape
before operations that could accidentally form an outer result.
- Preallocate when output size is known, but do not vectorize at the cost of
huge temporaries or unreadable code. Measure with `timeit` or the profiler.
- Compare floating-point results with domain-chosen absolute and relative
tolerances, not blanket `==` or a magic multiple of `eps`.
- Pin both random algorithm and seed. Use named `RandStream` substreams for
independent parallel work; do not use time-based `rng("shuffle")` for a
reproducibility claim.
Read [arrays](references/matrices-arrays.md) and
[mathematics](references/mathematics.md).
### Tables, timetables, and missing values
- A `table` has named, equal-height variables that may have different types.
`T(rows,vars)` returns a table; `T{rows,vars}` extracts contents; `T.Var`
selects one variable.
- A `timetable` additionally has row times. Sort, validate time zones and
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