Generate publication-ready Matplotlib figure templates with deterministic styling.
npx skills add https://github.com/wenhaochai/claude-plugins --skill plot-wenhaochai
Please help me install this Agent Skill. Skill: plot Source: https://github.com/wenhaochai/claude-plugins/tree/main/writing/skills/plot Command: npx skills add https://github.com/wenhaochai/claude-plugins --skill plot-wenhaochai
This Skill eliminates the repetitive, error-prone work of manually styling plots for papers by providing ready-to-run Matplotlib figure templates with consistent fonts and a publication-friendly color palette.
Use the plot skill to generate a single publication-ready figure by running the appropriate template (for example, 20_line_multi.py) in your figures folder, replacing placeholder labels and data with your own, and adding fig.savefig('out.pdf') before execution.
High-intent search queries and answers about installing and using this skill.
You can create publication-quality charts by running ready-to-use Matplotlib templates that apply consistent Palatino and STIX typography with a soft Google brand color palette for paper-ready figures.
Styling a log-log plot for scientific writing is handled by specific line and scaling variant templates that automatically apply deterministic arXiv-like typography and publication-friendly colors.
Yes, you can generate ablation-friendly horizontal bar chart templates that include illustrative placeholder data and clear instructions to replace labels and data with your own results.
The plotting skill supports broken y-axis line charts through its multiple line and scatter variants, ensuring deterministic styling requirements are applied consistently across all figure types.
You customize colors in Matplotlib figures for academic reports by adjusting the controllable softness tier within the shared style.py file, which modifies the paper-friendly Google brand color palette.
No external dependencies are required to use these paper styling templates, as each template is a single Python file with a shared style.py that relies on standard Matplotlib functionality.
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