K-Dense-AI/scientific-agent-skills
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
Before you install
- Last update
- Files in folder
- 20
- Scripts
- Contains scripts. Review them before running.
- SKILL.md size
- 16 KB (about 4,138 tokens of context each time it loads)
- License
- MIT
- Source on GitHub
- K-Dense-AI/scientific-agent-skills
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
Install
npx skills add K-Dense-AI/scientific-agent-skills@matlabRuns in a terminal with Node.js 18 or newer. Add -y to skip the prompts and -g to install for every project instead of this one.
Install for your agent
npx skills add K-Dense-AI/scientific-agent-skills@matlab --agent claude-code -y- Project install writes to
.claude/skills/matlab- Global install writes to
~/.claude/skills/matlab
Start a new session afterwards so the agent picks the skill up. In Claude Code you can also call it directly with /matlab.
What it does
MATLAB and GNU Octave Use this skill to design or review numerical code, migrate MATLAB releases, prepare reproducible projects, and plan trusted execution. MATLAB and GNU Octave are distinct products: compatibility is partial, not a license or behavior guarantee. Review scope Reviewed against current MathWorks R2026b documentation and GNU Octave 11.3.0 sources on 2026-10-01. The bundled Engine planner and MATLAB examples retain an explicit R2026a baseline. R2026b supports CPython 3.10-3.14; do not reuse the R2026a Engine package for it. MATLAB/Octave examples are illustrative and were not…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
Categories
Related skills
- literature-review
K-Dense-AI/scientific-agent-skills
Conducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.
- citation-management
K-Dense-AI/scientific-agent-skills
Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
- matplotlib
K-Dense-AI/scientific-agent-skills
Creates and customizes scientific plots with Matplotlib. Used for fine-grained control over plot elements, novel plot types, and scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
- database-lookup
K-Dense-AI/scientific-agent-skills
Queries documented public database APIs with explicit endpoints, filters, pagination, and provenance. Used when a scientific, regulatory, financial, or other database-backed fact must be retrieved reproducibly from a named source rather than inferred from general knowledge.
- exploratory-data-analysis
K-Dense-AI/scientific-agent-skills
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain formats are reference-only and unknown formats fail closed.
- hypothesis-generation
K-Dense-AI/scientific-agent-skills
Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.