generate-figures

To generate publication-ready figures and LaTeX tables, provide input folders containing analysis results and scoring data, specify an output directory, and run the skill to automatically produce formatted plots and tables.

Yes, you can create JAMA-formatted plots with colorblind-safe palettes using matplotlib. The skill enforces golden-ratio dimensions and consistent JAMA styling on all generated figures for manuscript readiness.

Yes, you need numpy, matplotlib, and scipy installed in your Python environment to automate figure generation from JSON, along with a Jama-style styling module for applying the required formatting.

The best way to automate manuscript-ready visuals in a reproducible workflow is reading standardized analysis_results.json inputs and writing outputs to a dedicated figures directory, generating a manifest of all created artifacts.

Figure generation fails when the required input directories do not contain the expected standardized analysis_results.json and scoring data, or when the Python environment lacks the required numpy, matplotlib, and scipy dependencies.

Generated tables are output in LaTeX format for publication. The skill reads JSON analysis results and writes these LaTeX tables alongside publication-quality figures into a designated output directory.