Matlab Modernize Code
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a systematic approach to modernizing MATLAB codebases. It acts as a resolver for issues identified by the check_matlab_code MCP tool, which flags functions that are removed, discouraged, or slated for future removal. By replacing outdated APIs and anti-patterns with current, performant, and secure alternatives, this skill helps maintain code longevity and compatibility with modern MATLAB releases.
What it does
- Identifies and replaces removed or discouraged MATLAB functions.
- Refactors anti-patterns like eval, clear all, and str2num.
- Provides migration paths for file I/O, UI components, and signal processing.
- Integrates with static analysis results from check_matlab_code.
- Offers domain-specific guidance for deep learning, audio/video, and communications.
- Promotes modern design patterns like table-based workflows and arguments blocks.
When to use it
- When check_matlab_code reports lifecycle diagnostics (info, warning, or error).
- When migrating legacy scripts to newer MATLAB versions.
- When replacing discouraged patterns like containers.Map or bsxfun.
- When updating code to use modern APIs like readmatrix or datetime.
- When starting new projects to ensure best-practice implementation from the start.
Advantages
- Ensures code compatibility with current and future MATLAB releases.
- Improves code performance and readability by using optimized modern APIs.
- Enhances security by eliminating dangerous patterns like unrestricted eval.
- Reduces technical debt by systematically removing deprecated dependencies.
- Provides clear, tested migration paths for common domain-specific tasks.
Usage examples
- “Modernize this legacy file I/O code: [M, t] = xlsread('data.xlsx');”
- The agent replaces the call with the modern readtable function: T = readtable('data.xlsx');
- “How do I replace this discouraged eval pattern: eval(['A' int2str(n) '=magic(n);']);”
- The agent suggests using a cell array for dynamic indexing: A = cell(10,1); for n = 1:10, A{n} = magic(n); end
- “Update my audio code that uses the removed wavread function.”
- The agent provides the modern replacement using audioread, including handling for sample ranges and native formats.
- “Replace my old BPSK modulator object with the modern function call.”
- The agent replaces the comm.BPSKModulator object instantiation with the direct pskmod(data, 2) function call.
Install
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Documentation by MathWorks
Code Modernization
Replace removed or discouraged MATLAB functions and anti-patterns with modern equivalents. This skill is the resolver — the MCP tool check_matlab_code (MATLAB's Code Analyzer) is the detector.
Terms used in this skill:
Removed — the function is no longer in MATLAB; calling it errors.
Discouraged — the function still works, but a better API exists. It may or may not be slated for removal in a future release.
When to Use
check_matlab_codereports a lifecycle diagnostic — any of these three wordings, matched case-insensitively (STRMATCH is not recommendedis the same signal as the lowercase form):'X' is not recommended...(severity: info)'X' will be removed in a future release...(severity: warning)'X' has been removed...(severity: error — the code errors today)
Code uses a discouraged pattern Code Analyzer does not flag — e.g.
containers.Map,bsxfun,str2num,eval,clear all— scan the source, not just the diagnostics (see Conventions)User asks to modernize, migrate, or update old MATLAB code
Code uses functions listed in the quick reference table below
After static analysis reveals removed or discouraged API usage
Writing new code in a domain that has known removed or discouraged patterns
When NOT to Use
This skill swaps removed or discouraged APIs for modern equivalents. It does not:
Review code quality broadly
Debug runtime behavior (removed-function errors excepted — those are modernization fixes)
Optimize already-correct code for speed → hand off to
matlab-optimize-performanceWrite or migrate function input/output validation (
argumentsblocks,inputParser/validateattributes→mustBe*validators, repeating args, name-value forwarding) → hand off tomatlab-validate-function-arguments
Quick Reference: Removed, Discouraged & Anti-Patterns
| Topic | Recommendation | Since | Category | Rationale |
|---|---|---|---|---|
csvread / dlmread | readmatrix | R2019a | File I/O | |
csvwrite / dlmwrite | writematrix | R2019a | File I/O | |
xlsread | readtable, readmatrix | R2019a | File I/O | |
xlswrite | writetable, writematrix | R2019a | File I/O | |
datenum / datestr / now | datetime (use string/char to format) | R2022b | Date/Time (Not recommended) | Flagged by Code Analyzer |
eval / evalc / evalin | Dynamic field names s.(name), function handles / feval, cells for sequential names | — | Anti-pattern | Not compiled (slow); can overwrite workspace vars; hard to debug; injection risk |
str2num | str2num(text, Evaluation="restricted") | R2022a | Security | Unrestricted form runs input via eval (injection risk); see references |
uicontrol | uibutton, uidropdown, etc. | R2016a | UI/App | |
guide | appdesigner | R2025a | UI (Removed) | |
strmatch | startsWith, matches | R2019b | Strings | |
clear all | clearvars | — | Workspace | Clears functions from memory; forces recompilation |
datestrnuance:datestr's job was to format a date as text, so its modern replacement isstring(dt)/char(dt)or settingdt.Format— not a baredatetime(...)call. Usedatetimeto replacedatenum/now(the numeric/serial-date path). Neither is removed; both are Code Analyzer "not recommended" as of R2022b.s = datestr(t, 'yyyy-mm-dd HH:MM:SS'); % old s = string(t, 'yyyy-MM-dd HH:mm:ss'); % new — note the specifiers change: % datetime uses MM=month, mm=minute (datestr had mm=month, MM=minute)
Modern Design Patterns
Prefer these in all new code:
Table-Based Workflows
data = readtable('sensors.csv');
data.Timestamp = datetime(data.Timestamp);
data.Status = categorical(data.Status);
recentData = data(data.Timestamp > datetime('today') - days(7), :);
summary = groupsummary(recentData, 'SensorID', 'mean', 'Value');String Arrays (not char arrays)
name = "John"; % not 'John'
names = ["John", "Jane", "Bob"]; % not {'John','Jane','Bob'}
fullName = firstName + " " + lastName; % not [first,' ',last]
idx = contains(names, "Jo"); % not cellfun + strfindArguments Block (not nargin/varargin)
function result = processData(data, options)
arguments
data (:,:) double
options.Method (1,1) string {mustBeMember(options.Method, ["fast","accurate"])} = "fast"
options.Verbose (1,1) logical = false
end
endKey Migrations
File I/O: csvread/xlsread → readmatrix/readtable
% Old → Modern
M = csvread('data.csv'); % M = readmatrix("data.csv");
M = dlmread('data.txt','\t'); % M = readmatrix("data.txt", Delimiter="\t");
[n,t,r] = xlsread('f.xlsx'); % T = readtable("f.xlsx");
csvwrite('out.csv', M); % writematrix(M, "out.csv");
xlswrite('out.xlsx', data); % writetable(T, "out.xlsx");eval → Structured Alternatives
Avoid eval/evalc/evalin: the text isn't compiled (slower), can silently overwrite workspace
variables, and is hard to read/debug. Match the intent to a construct — see
references/core-functions-guidance.md for the full set (sequential names, sprintf filenames,
try/catch).
% Old: eval([varName ' = 42;']); -> dynamic field name
s.(varName) = 42;
% Old: result = eval(['process_' method '(x)']); -> handle dispatch table
handlers.fast = @processFast;
handlers.slow = @processSlow;
result = handlers.(method)(x);
% Old: for n=1:10, eval(['A' int2str(n) '=magic(n);']); end -> index a cell
A = cell(10,1);
for n = 1:10, A{n} = magic(n); endReferences
Load these when working in a specific domain:
| Load when... | Reference |
|---|---|
Core MATLAB functions (file I/O, strings, UI); security anti-patterns (eval/str2num), containers.Map→dictionary, bsxfun→implicit expansion | references/core-functions-guidance.md |
| Deep Learning (trainNetwork→trainnet, LayerGraph/SeriesNetwork/DAGNetwork→dlnetwork, classify, activations) | references/deep-learning-guidance.md |
| Signal processing functions | references/signal-processing-guidance.md |
| Audio/video I/O migration (wavread, aviread) | references/audio-video-guidance.md |
| Optimization toolbox (optimset, optimtool) | references/optimization-guidance.md |
| Control systems plot options | references/control-systems-guidance.md |
| Image processing ROI objects | references/image-processing-guidance.md |
| Statistics/ML (svmtrain, dataset, classregtree) | references/statistics-ml-guidance.md |
| Communications System objects | references/communications-guidance.md |
| Mapping Toolbox (webmap, wmmarker, wmline, geotiffread, mfwdtran, makerefmat) | references/mapping-guidance.md |
Conventions
Always run
check_matlab_codefirst — let static analysis find removed or discouraged usageAfter running it, scan the source for patterns the Code Analyzer may under-flag:
str2num(framed only as a perf tip);containers.Map(see next point).containers.Mapis not flagged bycheck_matlab_code. A plain script using it returns zero diagnostics; acontainers.Mapproperty default surfaces only a handle-default-sharing warning ("all instances share the same object data"). The detect-first workflow will not find this migration — treat anycontainers.Mapusage, and that handle-sharing warning on a property, as acontainers.Map→dictionarytrigger.subplotis not deprecated or discouraged. It is fully supported and is never flagged bycheck_matlab_code.tiledlayout/nexttileis a preferred alternative for new code, not a required migration — do not tell the user to replace workingsubplotcode.Fix removed or discouraged patterns before other code quality issues
When writing new code, use the modern pattern from the start — don't write legacy code and fix it later
A
'X' has been removeddiagnostic (severityerror) means the function errors today — treat it as a break-fix modernization, ahead of "not recommended" (info) and "will be removed" (warning) casesWhen migrating, test the modern replacement against the old behavior to confirm equivalence
Legacy
.mfiles are often Windows-1252 encoded. After an edit, verify non-ASCII characters (°,µ,±) survived — a diff showing an unrelated one-line change on a comment or string is usually a re-encoded byte, not your edit.Consult the domain-specific reference file for detailed migration patterns with code examples
Copyright 2026 The MathWorks, Inc.
Files
- ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
- LICENSELicence file added by AutoGineer1.4 KiB
- SKILL.md9.7 KiB
- manifest.yaml1.1 KiB
- references/audio-video-guidance.md9.4 KiB
- references/communications-guidance.md8.5 KiB
- references/control-systems-guidance.md5.0 KiB
- references/core-functions-guidance.md22.1 KiB
- references/deep-learning-guidance.md3.3 KiB
- references/image-processing-guidance.md6.9 KiB
- references/mapping-guidance.md14.5 KiB
- references/optimization-guidance.md6.0 KiB
- references/signal-processing-guidance.md5.0 KiB
- references/statistics-ml-guidance.md8.0 KiB
references/statistics-ml-guidance.md
Statistics/ML Modernization
Quick Reference: Function Mappings
| Topic | Recommendation | Since | Status |
|---|---|---|---|
classregtree | fitctree / fitrtree | R2018a | Removed |
svmtrain | fitcsvm | R2018a | Removed |
svmclassify | predict (ClassificationSVM) | R2018a | Removed |
dataset | table | R2013b | Not Recommended |
nominal | categorical | R2013b | Not Recommended |
ordinal | categorical(..., Ordinal=true) | R2013b | Not Recommended |
fitcsvm/fitctree shipped in R2014a — several releases before svmtrain/classregtree were removed in R2018a — so you can migrate to the modern API well before you are forced to.
Removed Machine Learning Functions (R2018a)
classregtree → fitctree / fitrtree
Status: Removed in R2018a. Use fitctree for classification or fitrtree for regression.
Old Pattern (No Longer Works):
% Legacy classification tree
tree = classregtree(X, Y, 'Method', 'classification');
predictions = eval(tree, XTest);Modern Pattern (Use This):
% Modern classification tree
tree = fitctree(X, Y);
predictions = predict(tree, XTest);
% For regression trees
tree = fitrtree(X, Y);
predictions = predict(tree, XTest);Key API Changes:
| Old (classregtree) | New (fitctree/fitrtree) |
|---|---|
eval(tree, X) | predict(tree, X) |
eval(tree, X, subtree) | predict(tree, X, 'Subtrees', subtree) |
tree.numnodes | tree.NumNodes (property) |
view(tree) | view(tree) (still works) |
Complete Migration Example:
% Old code (won't work in R2018a+)
% tree = classregtree(X, Y, 'names', varNames);
% yfit = eval(tree, Xnew);
% err = test(tree, 'test', Xtest, Ytest);
% Modern equivalent
tree = fitctree(X, Y, PredictorNames=varNames);
yfit = predict(tree, Xnew);
cvtree = crossval(tree);
err = kfoldLoss(cvtree);svmtrain → fitcsvm
Status: Removed in R2018a. Use fitcsvm instead.
Old Pattern (No Longer Works):
% Legacy SVM training
SVMStruct = svmtrain(XTrain, YTrain);
SVMStruct = svmtrain(XTrain, YTrain, 'kernel_function', 'rbf', 'showplot', true);Modern Pattern (Use This):
% Modern SVM training
svmModel = fitcsvm(XTrain, YTrain);
svmModel = fitcsvm(XTrain, YTrain, KernelFunction="rbf");
% For visualization (no built-in showplot in fitcsvm)
% Use custom plotting or Classification Learner appKey Differences:
fitcsvmreturns aClassificationSVMobject, not a structNo built-in 'showplot' option - use custom plotting
Name-value pairs use different naming conventions
fitcsvmrequires both X and Y as separate arguments (no single-argument call)
Kernel Function Mapping:
| svmtrain | fitcsvm |
|---|---|
| 'linear' | 'linear' |
| 'quadratic' | 'polynomial' (Degree=2) |
| 'polynomial' | 'polynomial' |
| 'rbf' | 'rbf' or 'gaussian' |
| 'mlp' | Use custom kernel |
svmclassify → predict
Status: Removed in R2018a. Use predict method of ClassificationSVM instead.
Old Pattern (No Longer Works):
% Legacy SVM classification
SVMStruct = svmtrain(XTrain, YTrain);
YPred = svmclassify(SVMStruct, XTest);Modern Pattern (Use This):
% Modern SVM classification
svmModel = fitcsvm(XTrain, YTrain);
yPred = predict(svmModel, XTest);
% With scores (probability-like values)
[yPred, scores] = predict(svmModel, XTest);Complete SVM Migration Example:
% Old code (won't work in R2018a+)
% SVMStruct = svmtrain(X, Y, 'kernel_function', 'rbf', 'rbf_sigma', 1);
% predictions = svmclassify(SVMStruct, Xnew);
% Modern equivalent
svmModel = fitcsvm(X, Y, KernelFunction="rbf", KernelScale=1);
predictions = predict(svmModel, Xnew);
% Cross-validation
cvSvmModel = crossval(svmModel);
classLoss = kfoldLoss(cvSvmModel);Discouraged Data Types
dataset → table
Status: Not recommended. Use MATLAB's built-in table data type instead.
Old Pattern (Not Recommended):
% Legacy dataset (requires Statistics Toolbox)
ds = dataset(age, weight, height, 'VarNames', {'Age', 'Weight', 'Height'});
ds.BMI = ds.Weight./(ds.Height/100).^2;
subds = ds(ds.Age > 30, :);Modern Pattern (Use This):
% Modern table (core MATLAB, no toolbox required)
T = table(age, weight, height, VariableNames=["Age" "Weight" "Height"]);
T.BMI = T.Weight./(T.Height/100).^2;
subT = T(T.Age > 30, :);Migration Function:
% Convert existing dataset to table
T = dataset2table(ds);Why table is Better:
Part of core MATLAB (no Statistics Toolbox required)
Better integration with modern MATLAB functions
More consistent syntax
Better datetime and categorical support
Improved display and indexing options
Property/Method Name Changes:
| dataset | table |
|---|---|
| 'VarNames' | 'VariableNames' |
| ds.Properties.VarNames | T.Properties.VariableNames |
| export(ds, ...) | writetable(T, ...) |
| join(ds1, ds2) | join(T1, T2) or outerjoin(T1, T2) |
nominal → categorical
Status: Not recommended. Use categorical instead.
Old Pattern (Not Recommended):
% Legacy nominal array (requires Statistics Toolbox)
colors = nominal({'red', 'blue', 'red', 'green', 'blue'});
counts = countlevels(colors);Modern Pattern (Use This):
% Modern categorical array (core MATLAB)
colors = categorical(["red" "blue" "red" "green" "blue"]);
counts = countcats(colors);Key Changes:
| nominal | categorical |
|---|---|
nominal({'a','b'}) | categorical({'a','b'}) |
getlevels(x) | categories(x) |
countlevels(x) | countcats(x) |
droplevels(x) | removecats(x) |
mergelevels(x) | mergecats(x) |
ordinal → categorical (with ordering)
Status: Not recommended. Use categorical with 'Ordinal', true instead.
Old Pattern (Not Recommended):
% Legacy ordinal array
sizes = ordinal({'small', 'medium', 'large', 'medium'}, ...
{'small', 'medium', 'large'});Modern Pattern (Use This):
% Modern ordinal categorical array
sizes = categorical(["small" "medium" "large" "medium"], ...
["small" "medium" "large"], Ordinal=true);
% Comparisons work as expected
largeSizes = sizes > 'medium';Key Differences:
categoricalwith'Ordinal', trueallows comparison operators (<,>,<=,>=)Use
isordinal(x)to check if a categorical array is ordinalUse
categories(x)to get ordered category list
Modern Machine Learning Workflow
Recommended Pattern for Classification
% 1. Prepare data
data = readtable('data.csv');
X = data{:, 1:end-1};
Y = categorical(data.Label);
% 2. Partition data
cv = cvpartition(Y, HoldOut=0.3);
XTrain = X(cv.training, :);
YTrain = Y(cv.training);
XTest = X(cv.test, :);
YTest = Y(cv.test);
% 3. Train model (choose appropriate function)
% For decision trees:
model = fitctree(XTrain, YTrain);
% For SVM:
model = fitcsvm(XTrain, YTrain, KernelFunction="rbf");
% For ensemble methods:
model = fitcensemble(XTrain, YTrain, Method="Bag");
% 4. Evaluate
yPred = predict(model, XTest);
accuracy = mean(yPred == YTest);
confusionchart(YTest, yPred);
% 5. Cross-validation
cvModel = crossval(model);
cvLoss = kfoldLoss(cvModel);Function Reference: fit* Family
The modern Statistics and Machine Learning Toolbox uses a consistent fit* naming convention:
| Function | Purpose |
|---|---|
fitctree | Classification tree |
fitrtree | Regression tree |
fitcsvm | Support vector machine (classification) |
fitrsvm | Support vector machine (regression) |
fitcensemble | Classification ensemble |
fitrensemble | Regression ensemble |
fitcknn | k-nearest neighbors (classification) |
fitcnb | Naive Bayes (classification) |
fitcdiscr | Discriminant analysis |
fitclinear | Linear classification (high-dimensional) |
fitrgp | Gaussian process regression |
fitglm | Generalized linear model |
fitlm | Linear model |
Copyright 2026 The MathWorks, Inc.
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
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Security
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Attribution and licence
- Created by
- MathWorks
- Publisher
- MathWorks (Official)
- Source
- matlab/matlab-agentic-toolkit · skills-catalog/matlab-software-development/matlab-modernize-code · commit 2a7f215
- Licence
- BSD 2-Clause License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE.md)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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