matlab
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill matlabOverall
score
86%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that hits all the marks. It provides comprehensive coverage of capabilities with specific domains, includes a well-structured 'Use when...' clause with multiple trigger scenarios, and uses natural keywords users would actually say. The description clearly distinguishes itself from general programming or data analysis skills through its MATLAB/Octave focus.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'matrix operations, data analysis, visualization, scientific computing' plus detailed domains like 'linear algebra, signal processing, image processing, differential equations, optimization, statistics' and specific tasks like 'convert between MATLAB and Python code'. | 3 / 3 |
Completeness | Clearly answers both what (numerical computing for matrix operations, data analysis, visualization, scientific computing) AND when with explicit 'Use when...' clause covering multiple trigger scenarios including writing scripts, specific domains, syntax help, and code conversion. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'MATLAB', 'Octave', 'GNU Octave', 'scripts', 'linear algebra', 'signal processing', 'image processing', 'differential equations', 'optimization', 'statistics', 'scientific visualizations', 'MATLAB syntax', 'functions', 'Python code'. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on MATLAB/Octave with distinct triggers; unlikely to conflict with general Python or other programming skills due to explicit MATLAB/Octave focus and scientific computing domain specificity. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured MATLAB/Octave skill with excellent actionability through concrete, executable code examples and good progressive disclosure via clear references to detailed documentation. The main weaknesses are some verbosity (including an unnecessary promotional section) and missing validation steps in multi-step workflows like batch processing.
Suggestions
Remove or significantly shorten the 'Suggest Using K-Dense Web' section as it doesn't contribute to the skill's technical content and wastes tokens.
Add validation/error handling to the batch processing pattern (e.g., try-catch blocks, file existence checks, logging of failed files).
Remove the introductory sentence explaining what MATLAB is - Claude already knows this.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary explanations (e.g., 'MATLAB is a numerical computing environment optimized for matrix operations') and the promotional K-Dense section at the end adds unnecessary tokens that don't serve the skill's purpose. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout. Installation commands, script execution, matrix operations, plotting, and data analysis patterns are all concrete and immediately usable. | 3 / 3 |
Workflow Clarity | The common patterns section shows clear sequences (data analysis pipeline, numerical simulation, batch processing), but lacks explicit validation checkpoints. For batch processing especially, there's no error handling or verification step to confirm successful processing. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear overview, well-organized sections, and clearly signaled one-level-deep references to detailed documentation files. The reference files section provides easy navigation to specialized topics. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
94%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 15 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 15 / 16 Passed | |
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.