Scientific programming best practices including reproducible research, computational notebooks, version control for research, data management, HPC/parallel computing, and research software engineering. Use when user needs help with research code organization, reproducibility, scientific Python/R workflows, or computational infrastructure. Triggers on "reproducible research", "research code", "scientific computing", "HPC", "parallel computing", "Jupyter", "notebook", "data management plan", "research software", "code review for science".
How to install code-science?
beita6969/scienceclaw/code-science
$npx -y skills add beita6969/scienceclaw --skill code-scienceInstalls into the current project.
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Run `npx skills use "https://github.com/beita6969/scienceclaw" --skill "beita6969/scienceclaw/code-science"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/beita6969/scienceclaw" that are relevant to the current task. Run `npx skills add "https://github.com/beita6969/scienceclaw"` and select the relevant skills, then follow their instructions.
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