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plotting

Generate publication-quality matplotlib/seaborn figures with paperops.plotting presets.

Updated May 29, 2025
Search Tags:#academic#visualization#plotting#matplotlib#seaborn#figures#paperops
One-click install
npx skills add https://github.com/Lincyaw/paperops --skill plotting-lincyaw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plotting
Source: https://github.com/Lincyaw/paperops/tree/main/.claude/skills/plotting
Command: npx skills add https://github.com/Lincyaw/paperops --skill plotting-lincyaw

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generate publication-quality figures for academic papers using matplotlib/seaborn presets provided by paperops.plotting. This tool helps researchers create consistent, publication-ready visuals without manual styling.

Core Features & Use Cases

  • Preconfigured styles and themes via apply_plot_config ("classic", "modern", "grayscale") and figure helpers to produce consistent layouts for single- and multi-panel figures.
  • Access a full palette and style presets (COLORS, MARKER_STYLES, LINE_STYLES, HATCH_PATTERNS) to craft clear, accessible visuals.
  • Save figures in publication-ready formats (PDF/PNG) for inclusion in papers, slides, or supplementary materials.

Quick Start

Create a publication-ready figure from your dataset using figure and save it to a PDF.

Frequently Asked Questions about plotting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate publication-ready figures for an academic paper?▼

To generate publication-ready figures, use this Skill to apply matplotlib and seaborn presets via paperops.plotting. It provides preconfigured themes like classic, modern, and grayscale to produce consistent layouts and output vector PDFs without manual styling.

How do I create consistent multi-panel layouts in matplotlib for research papers?▼

Create consistent multi-panel layouts by using the figure helper functions exposed through paperops.plotting. These helpers apply standardized styling across single-column and double-column formats, ensuring visual consistency throughout your academic publication.

Does this plotting tool support accessible color schemes and style presets for seaborn?▼

Yes, this plotting tool supports accessible visual design by exposing a full palette of style presets. You can directly access COLORS, MARKER_STYLES, LINE_STYLES, and HATCH_PATTERNS to craft clear, accessible charts using seaborn and matplotlib.

Can I save matplotlib figures directly to vector PDF for publication?▼

Yes, you can save matplotlib figures directly to vector PDF for publication. The Skill exposes a save front-end API that outputs figures in publication-ready formats like PDF and PNG for inclusion in papers, slides, or supplementary materials.

What is the best way to apply consistent figure styling across multiple academic charts?▼

The best way to apply consistent figure styling is using the apply_plot_config API from paperops.plotting. It applies uniform themes such as classic, modern, and grayscale across all your charts, eliminating the need for manual styling adjustments.

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