matlab-review-code
matlab-review-code systematically analyzes MATLAB code for quality, correctness, and adherence to MathWorks coding standards using static analysis and manual inspection. Use it when reviewing code for style violations, code smells, and quality issues before handoff or publication, or when users explicitly request code audits or improvements. Do not use it for runtime debugging, performance optimization, or test generation, which require specialized skills instead.
git clone --depth 1 https://github.com/matlab/matlab-agentic-toolkit /tmp/matlab-review-code && cp -r /tmp/matlab-review-code/skills-catalog/matlab-core/matlab-review-code ~/.claude/skills/matlab-review-codeSKILL.md
# Code Review
Systematically review MATLAB code for quality, correctness, performance, and adherence to MathWorks coding conventions using static analysis and manual inspection patterns.
## When to Use
- User asks to review, audit, or improve code quality
- User wants to check adherence to MathWorks coding standards
- Preparing code for handoff, publication, or open-source release
- After a significant implementation — verify before committing
- User reports "code smells" or asks for cleanup suggestions
## When NOT to Use
- User wants to debug a runtime error — use `matlab-debug-code` instead
- User wants to optimize performance — use performance profiling skills
- User wants to generate tests — use `matlab-write-test` instead
## Workflow
1. **Run static analysis** — Use `check_matlab_code` MCP tool on all target files
2. **Load coding standards** — Read the `matlab_coding_guidelines` MCP resource
3. **Check naming** — Verify functions, classes, variables, and files follow conventions
4. **Review function signatures** — Arguments blocks, input/output counts, name-value patterns
5. **Assess structure** — Function length, nesting depth, complexity
6. **Check patterns** — Vectorization, preallocation, modern API usage
7. **Summarize** — Report findings by severity: errors > warnings > suggestions
## Step 1: Static Analysis
Use the `check_matlab_code` MCP tool on each file. Then inspect results programmatically:
```matlab
info = checkcode("src/computeArea.m", "-struct");
for k = 1:numel(info)
fprintf('Line %d (col %d-%d): %s\n', ...
info(k).line, info(k).column(1), info(k).column(end), info(k).message);
end
```
For directory-wide analysis (R2022b+):
```matlab
issues = codeIssues("src");
disp(issues.Issues);
```
## Step 2: Load Coding Standards
Read the `matlab_coding_guidelines` MCP resource to get the authoritative MathWorks coding standards. Use these as the baseline for all naming, formatting, and structural checks.
## Review Checklist
### Naming
| Element | Convention | Example |
|---------|-----------|---------|
| Functions | lowerCamelCase, verb phrase | `computeArea`, `loadData` |
| Classes | PascalCase | `SensorReader`, `DataProcessor` |
| Variables | lowerCamelCase, descriptive | `sampleRate` not `sr` |
| Constants | UPPER_SNAKE or `Constant` property | `MAX_ITERATIONS` |
| Test files | `t` prefix | `tComputeArea.m` |
| App files | PascalCase | `DashboardApp.m` |
| File = function | File name matches primary function | `computeArea.m` → `function computeArea` |
### Function Quality
| Check | Standard | Severity |
|-------|----------|----------|
| Input count | Max 6 positional inputs | Warning |
| Output count | Max 4 outputs | Warning |
| Validation | `arguments` block present | Warning |
| Name-value args | `options.Name` pattern (not `varargin`) | Suggestion |
| Length | Flag if >50 lines | Suggestion |
| Nesting | Flag if >3 levels deep | Warning |
| `end` keyword | All functions terminated with `end` | Warning |
| Help text | H1 line present for public functions | Suggestion |
### Code Patterns
| Check | Modern | Legacy (flag it) |
|-------|--------|-------------------|
| Multi-panel figures | `tiledlayout`/`nexttile` | `subplot` |
| Date/time | `datetime` | `datenum`/`datestr` |
| Strings | `string` type | char arrays for text |
| Vectorization | `.*`, `./`, logical indexing | Loops over elements |
| Preallocation | `zeros(n,1)` before loop | Growing arrays in loops |
| Data containers | `table`/`timetable` | Raw matrices for named data |
| Dynamic eval | Direct function calls | `eval`, `evalin`, `assignin` |
### High-Severity Flags
These should always be reported as errors:
- Use of `eval`, `assignin`, or `evalin` — security and maintainability risk
- Growing arrays inside loops without preallocation — performance
- Shadowing built-in functions — `sum = 5` shadows `sum()`
- Missing `arguments` block in public-facing functions
- Hardcoded file paths with backslashes
### What checkcode Misses
`check_matlab_code` does NOT catch all issues. After running static analysis, **always scan the source code** for these common problems that require visual inspection:
- **`subplot` usage** — not flagged by checkcode, but should use `tiledlayout`/`nexttile`
- **Shadowed builtin variables** — `sum = 0` shadows `sum()`, checkcode may not flag it
- **Deep nesting** (>3 levels) — checkcode does not measure nesting depth
- **Hardcoded backslash paths** — checkcode flags unused variables but not path style
- **Magic numbers** — unlabeled constants in code (e.g., `if length(x) > 10`)
- **Missing H1 help text** — checkcode does not require help text
Do not skip Steps 3-6 of the workflow just because checkcode returns few results.
## Patterns
### Complexity Assessment
```matlab
function complexity = assessComplexity(filePath)
%assessComplexity Estimate cyclomatic complexity of a MATLAB function.
arguments
filePath (1,1) string {mustBeFile}
end
code = fileread(filePath);
branchKeywords = ["if " "elseif " "case " "while " "for " "catch "];
complexity = 1;
for kw = branchKeywords
complexity = complexity + numel(strfind(code, kw));
end
end
```
### Check Toolbox Dependencies
```matlab
[files, products] = matlab.codetools.requiredFilesAndProducts('src/myFunction.m');
fprintf('Required products:\n');
for k = 1:numel(products)
fprintf(' %s (ID: %d)\n', products(k).Name, products(k).ProductNumber);
end
```
### Review Report Format
Present findings in this format:
```
## Code Review: computeArea.m
### Static Analysis (checkcode)
- 2 warnings, 0 errors
### Naming ✓
- [x] Function: lowerCamelCase
- [x] Variables: descriptive
- [x] File name matches function
### Structure
- [x] arguments block present
- [x] Function under 50 lines
- [ ] ⚠ Nesting depth reaches 4 levels (line 32)
### Patterns
- [x] Vectorized
- [x] Modern graphics API
- [ ] ⚠ Uses datenum (line 18) — migrate to datetime
### Suggestions
1. Ext>
Analyze AMS waveform data using Mixed-Signal Blockset utilities: phase noise measurement, clock jitter, anti-aliased resampling, timing measurements, lock time, INL/DNL, ADC/DAC calibration, HSpice import. Use when analyzing time-domain voltage from PLL/VCO/clock simulations, measuring phase noise from variable-step solver output, computing jitter, or resampling non-uniform data.
Analyze data using MATLAB. Use when the task involves tables, timetables, time-series data, numeric arrays, sensor matrices, or gridded data — including but not limited to exploring, filtering, sorting, cleaning, transforming, aggregating, smoothing, padding, trimming, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.
Analyze the effective toolbox file set to produce a Dependency Manifest — classify all transitive dependencies as included, product, add-on, or external-unresolved, then present resolution options with tradeoffs. Use after matlab-define-toolbox-api when the spec is approved.
S-parameters, insertion loss, fields, currents, mesh control, and solver selection for RF PCB performance validation. TRIGGER: user asks to compute S-parameters, analyze insertion/return loss, extract fields or currents, compare MoM vs FEM, or control mesh for any RF PCB component. Invoke BEFORE writing sparameters() or solver code — API is non-obvious. SKIP: designing or creating components (use the specific matlab-design-pcb-* skill), material/stackup setup only (use matlab-manage-pcb-material), optimization sweeps (use matlab-optimize-pcb-design), PDN/IR-drop analysis (use matlab-analyze-pcb-pdn).
Analyze antennas installed on electrically large conducting platforms using MATLAB Antenna Toolbox. Loads platform geometry from STL/STEP/IGES, installs antenna elements, selects electromagnetic solvers (MoM-PO, FMM, MoM), and computes patterns, impedance, coupling, and efficiency. Use when the user wants to model an antenna on a vehicle, aircraft, ship, satellite, or other large structure.
PDN DC voltage/current analysis, IR drop, design rule checking, and multi-net batch analysis on imported PCB layouts. TRIGGER: user asks about power integrity, PDN analysis, IR drop, voltage distribution, current density, power nets, or design rule checking on a PCB. Invoke BEFORE writing code — the PDN API chain is specialized and non-obvious. SKIP: importing a PCB file (use matlab-read-pcb-layout), EM field/S-parameter extraction (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material), transmission line design (use matlab-design-pcb-txline).
Calculate and visualize monostatic and bistatic radar cross section (RCS) using MATLAB Antenna Toolbox. Computes RCS of platforms, antennas, and arrays with PO, MoM, and FMM solvers, supporting HH/VV/HV/VH polarization, GPU acceleration, and near-field observation. Use when the user wants to compute, plot, or analyze radar cross section.