nature-figure

To create publication-quality scientific figures meeting Nature style guidelines, this Skill automates compliance with academic design principles. It supports Python and R backends, ensuring your plots are journal-ready for high-impact publications.

Yes, you can use matplotlib and seaborn to generate Nature-style scientific figures. This Skill uses these Python libraries as its backend to produce high-quality, publication-ready plots adhering to strict journal standards.

Yes, it supports R for plotting scientific figures with ggplot2. You can select the R backend to utilize ggplot2, patchwork, and ComplexHeatmap for generating multi-panel figures complying with Nature publication standards.

The best way to plan a multi-panel scientific figure is using the included figure contract. This feature guides you through defining objectives, evidence logic, and review risks before generating the final publication-quality output.

Yes, a limitation is that backend usage must be exclusive; you cannot mix Python and R plotting libraries. You must choose either matplotlib/seaborn or ggplot2/patchwork/ComplexHeatmap for your scientific figures.