Convert experimental metrics into publication-ready figures and tables.
npx skills add https://github.com/SALTYf1SH/md-sci-skill --skill data-storyteller
Please help me install this Agent Skill. Skill: data-storyteller Source: https://github.com/SALTYf1SH/md-sci-skill/tree/main/.claude/skills/data-storyteller Command: npx skills add https://github.com/SALTYf1SH/md-sci-skill --skill data-storyteller
Turn raw experimental data into publication-ready figures and tables, enabling researchers to quickly translate logs, metrics, and results into polished visuals and concise summaries.
Point the workflow at your latest metrics.csv and run it to generate publication-ready figures and a results_summary.
High-intent search queries and answers about installing and using this skill.
To turn raw metrics CSVs into publication-ready figures, you can point the workflow at your metrics file to automatically generate polished visuals, clear axis labeling with units, and a concise results summary.
Yes, you can visualize TensorBoard logs and JSON metrics using pandas and matplotlib. The workflow processes these formats to create deterministic plots and publication-quality training curves.
The best way to create LaTeX tables with significance testing from experimental data is to use a workflow that automatically formats your results and summarizes metrics into markdown or LaTeX outputs.
Yes, this data visualization approach enforces reproducibility for machine learning experiments by utilizing deterministic plotting scripts to ensure consistent results across multiple runs.
Yes, you need numpy and seaborn installed to generate research study plots. These dependencies are required to support the underlying data manipulation and visualization functions.
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