Generate and run MATLAB unit tests using matlab.unittest and matlab.uitest. Parameterized tests, fixtures, mocking, coverage analysis, CI/CD with buildtool, app testing with gestures. Use when creating tests, writing test classes, running test suites, checking coverage, testing apps, or validating MATLAB code.
npx skills add https://github.com/matlab/matlab-agentic-toolkit --skill matlab-testing
Generate, structure, and run MATLAB unit tests using the matlab.unittest framework. Covers class-based tests, parameterized testing, fixtures, mocking, coverage analysis, CI/CD integration, and app testing via MCP.
matlab.unittest.TestCase. Never use script-based testsif, switch, for, or try/catch. Follow Arrange-Act-Assert. If a test needs conditionals, split into separate methodsrun_matlab_test_file or evaluate_matlab_code to run testsrun_matlab_test_file MCP tool| Category | Functions | Purpose |
|----------|-----------|---------|
| Equality | verifyEqual, verifyNotEqual | Compare values (use AbsTol for floats) |
| Boolean | verifyTrue, verifyFalse | Check logical conditions |
| Size/type | verifySize, verifyClass, verifyEmpty | Structural checks |
| Errors | verifyError | Confirm error is thrown with correct ID |
| Warnings | verifyWarning, verifyWarningFree | Check warning behavior |
| Infra | runtests, TestSuite, TestRunner | Run and organize tests |
| Coverage | CodeCoveragePlugin, CoverageResult | Measure test coverage |
| Level | On failure | When to use |
|-------|-----------|-------------|
| verify | Continues test | Default — most assertions |
| assert | Stops current test | Setup validation |
| fatal | Stops entire suite | Environment preconditions |
| assume | Skips test | Conditional execution (e.g., toolbox check) |
classdef computeAreaTest < matlab.unittest.TestCase
%computeAreaTest Tests for the computeArea function.
methods (Test)
function testSquare(testCase)
result = computeArea(5, 5);
testCase.verifyEqual(result, 25);
end
function testFloatingPoint(testCase)
result = computeArea(1/3, 3);
testCase.verifyEqual(result, 1, AbsTol=1e-12);
end
function testNegativeInputErrors(testCase)
testCase.verifyError( ...
@() computeArea(-1, 5), 'computeArea:negativeInput');
end
end
end
Parameterize only when assertion logic is identical across all cases — only the data varies. Use struct for readable test names:
classdef unitConverterTest < matlab.unittest.TestCase
properties (TestParameter)
conversionCase = struct( ...
'freezing', struct('input', 0, 'expected', 32), ...
'boiling', struct('input', 100, 'expected', 212), ...
'bodyTemp', struct('input', 37, 'expected', 98.6));
end
methods (Test)
function testCelsiusToFahrenheit(testCase, conversionCase)
result = celsiusToFahrenheit(conversionCase.input);
testCase.verifyEqual(result, conversionCase.expected, AbsTol=1e-10);
end
end
end
For advanced parameterization (combinations, dynamic parameters, ClassSetupParameter), see reference/parameterized-tests-guidance.md.
Prefer addTeardown over TestMethodTeardown blocks. Use PathFixture to add source folders:
classdef fileProcessorTest < matlab.unittest.TestCase
methods (TestClassSetup)
function addSourceToPath(testCase)
srcFolder = fullfile(fileparts(fileparts(mfilename('fullpath'))), 'src');
testCase.applyFixture(matlab.unittest.fixtures.PathFixture(srcFolder, ...
IncludingSubfolders=true));
end
end
methods (Test)
function testProcessFile(testCase)
tmpDir = string(tempname);
mkdir(tmpDir);
testCase.addTeardown(@() rmdir(tmpDir, 's'));
testFile = fullfile(tmpDir, "data.csv");
writematrix(rand(10, 3), testFile);
result = processFile(testFile);
testCase.verifySize(result, [10 3]);
end
end
end
For built-in fixtures, custom fixtures, and shared fixtures, see reference/fixtures-guidance.md.
Seed the RNG and restore it in teardown for reproducible tests:
methods (TestMethodSetup)
function resetRandomSeed(testCase)
originalRng = rng;
testCase.addTeardown(@() rng(originalRng));
rng(42, "twister");
end
end
Use TestTags for selective execution:
methods (Test, TestTags = {'Unit'})
function testFastCalculation(testCase)
% ...
end
end
methods (Test, TestTags = {'Integration', 'Slow'})
function testFullPipeline(testCase)
% ...
end
end
Run by tag: runtests('tests', Tag='Unit') or runtests('tests', ExcludeTag='Slow').
Use the run_matlab_test_file MCP tool for test files. For inline runs with filtering:
results = runtests('tests'); % all tests in folder
results = runtests('tests', Tag='Unit'); % by tag
results = runtests('tests', Name='*Calculator*'); % by name pattern
results = runtests('tests', UseParallel=true); % parallel execution
results = runtests('tests', Strict=true); % warnings = failures
disp(results);
for r = results([results.Failed])
fprintf('\nFAILED: %s\n', r.Name);
disp(r.Details.DiagnosticRecord.Report);
end
import matlab.unittest.TestRunner
import matlab.unittest.plugins.CodeCoveragePlugin
import matlab.unittest.plugins.codecoverage.CoverageResult
import matlab.unittest.plugins.codecoverage.CoverageReport
runner = TestRunner.withTextOutput;
covFormat = CoverageResult;
runner.addPlugin(CodeCoveragePlugin.forFolder('src', ...
Producing=[covFormat, CoverageReport('coverage-report')]));
results = runner.run(testsuite('tests'));
covResults = covFormat.Result;
disp(covResults);
For coverage gap analysis, use the printCoverageGaps script in reference/test-execution-guidance.md.
Use buildtool with a buildfile.m for CI pipelines. See reference/test-execution-guidance.md for buildfile.m templates and CI configs (GitHub Actions, Azure DevOps, GitLab CI).
For testing apps with programmatic UI gestures (press, choose, type, drag), see reference/app-testing-guidance.md.
Key points:
matlab.uitest.TestCase (not matlab.unittest.TestCase)drawnow after app creation, before first gestureuilabel.Text with char ('text'), not string ("text").Enable with matlab.lang.OnOffSwitchState.on/.offLoad these on demand — most tests only need what's in this file.
| Load when... | Reference |
|---|---|
| Tests need setup/teardown, temp dirs, path management, shared state | reference/fixtures-guidance.md |
| Floating-point tolerance selection, constraint objects, custom constraints | reference/constraints-guidance.md |
| Multiple parameters, dynamic parameters, combination strategies | reference/parameterized-tests-guidance.md |
| Code depends on external services, needs mock objects or dependency injection | reference/mocking-guidance.md |
| Running tests in CI, buildtool config, coverage gap analysis | reference/test-execution-guidance.md |
| Testing App Designer apps with gestures, dialogs, async callbacks | reference/app-testing-guidance.md |
matlab.unittest.TestCase<functionName>Test.m and place in tests/ directoryverify qualifications by default — they let all tests run even if one failsAbsTol for every floating-point comparison — never rely on exact equalityaddTeardown for cleanup — it runs even if the test failsTestParameter for readable parameterized test namesrun_matlab_test_file MCP tool for automatic result capture----
Copyright 2026 The MathWorks, Inc.
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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take matlab/matlab-testing from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.