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.

Quality

75%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/matlab/SKILL.md

Quality

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 covers specific capabilities comprehensively, includes a clear 'Use when' clause with diverse trigger scenarios, and uses natural keywords users would employ. It is well-differentiated from other programming or data analysis skills through its explicit MATLAB/Octave focus and mentions of domain-specific applications like signal processing and differential equations.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: matrix operations, data analysis, visualization, scientific computing, linear algebra, signal processing, image processing, differential equations, optimization, statistics, scientific visualizations, syntax help, and code conversion between MATLAB and Python.	3 / 3
Completeness	Clearly answers both 'what' (MATLAB/Octave numerical computing for matrix operations, data analysis, visualization, scientific computing) and 'when' (explicit 'Use when' clause covering writing scripts, specific domains, syntax help, and code conversion). Also includes execution context about MATLAB and GNU Octave interpreter.	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'MATLAB', 'Octave', 'linear algebra', 'signal processing', 'image processing', 'differential equations', 'optimization', 'statistics', 'scientific visualizations', 'MATLAB syntax', 'convert between MATLAB and Python'. These are all terms users would naturally use when seeking this kind of help.	3 / 3
Distinctiveness Conflict Risk	Clearly occupies a distinct niche around MATLAB/Octave numerical computing. The specific mention of MATLAB syntax, GNU Octave, and MATLAB-to-Python conversion makes it highly distinguishable from general Python data science or other programming skills.	3 / 3
	Total	12 / 12 Passed

Implementation

50%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is a comprehensive MATLAB/Octave reference but suffers significantly from verbosity — it reads more like a tutorial or cheat sheet than a skill file optimized for Claude's context window. The code examples are high quality and executable, but much of the content covers basic programming concepts and standard library functions that Claude already knows. The progressive disclosure structure is partially implemented with reference file links, but the main file retains too much detail that belongs in those references.

Suggestions

Drastically reduce the main SKILL.md to a quick-start section, key patterns, best practices, and reference file links — move all the basic syntax, control flow, statistics functions, and standard operations into the reference files where they belong.

Remove explanations of basic concepts Claude already knows (conditionals, loops, anonymous functions, basic statistics functions like mean/std/var) and focus only on MATLAB-specific idioms and gotchas.

Add validation/verification steps to the batch processing and numerical simulation patterns (e.g., check for NaN values after computation, verify output file was written correctly).

Remove the 'Additional Resources' section with external URLs — these add minimal value and consume tokens.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~300 lines, covering basic MATLAB syntax (conditionals, loops, functions), basic matrix creation, and fundamental concepts that Claude already knows well. Sections like control flow, basic statistics functions (mean, std, var), and data import/export are standard knowledge that don't need explicit instruction. The 'Additional Resources' section with external links adds little value.	1 / 3
Actionability	The content provides fully executable, copy-paste ready code examples throughout. Commands for running scripts, installing Octave, and complete code patterns (data analysis pipeline, numerical simulation, batch processing) are all concrete and specific.	3 / 3
Workflow Clarity	The common patterns section shows reasonable multi-step workflows (data analysis pipeline, numerical simulation), but there are no validation checkpoints or error recovery steps. The batch processing pattern lacks any verification that files were processed correctly or error handling for malformed data.	2 / 3
Progressive Disclosure	The skill references multiple detailed reference files (matrices-arrays.md, mathematics.md, etc.) with clear links, which is good structure. However, no bundle files were provided to verify these references exist, and the main SKILL.md itself contains too much inline content that should be in those reference files — the core capabilities sections essentially duplicate what the references should contain.	2 / 3
	Total	8 / 12 Passed

Validation

90%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	10 / 11 Passed

Repository: K-Dense-AI/claude-scientific-skills
Commit: cbcae7b

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

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