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This tool helps researchers and developers generate high-resolution charts by mapping user data to a catalog of academic style templates. It streamlines the creation of complex visualizations, such as t-SNE clusters and confidence band line graphs, ensuring consistent formatting for paper submissions.
WHO IT'S FOR
ML researchers
generate publication-quality figures from experimental data
Academic writers
reproduce figures from existing research papers
Data scientists
create professional multi-dimensional comparison charts
Graduate students
standardize plot aesthetics for conference submissions
Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.
Make sure plot-from-data is installed in your agent before trying this task.
Use the plot-from-data skill to create a dpi=300 PNG from my data using the bar_grouped_hatch style. Inputs: provide the category labels, series names, numeric values, x-axis label, y-axis label, and desired output filename. Preserve the supplied data dimensions and adjust the colors and bar-width calculation if the number of categories changes.
Make sure plot-from-image is installed in your agent before trying this task.
Use the plot-from-image skill to reproduce my attached academic-paper figure as Python matplotlib code. Input: attach the source PNG or screenshot. Measure its aspect ratio, check whether it matches a pre-built style, and produce a first-pass script that matches the proportions, fonts, colors, axes, grid, legend, and annotations.
Claude