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academic-plotting

Generate publication-quality ML paper figures with matplotlib and seaborn.

Updated May 4, 2026
Search Tags:#matplotlib#diagram generation#seaborn#academic plotting#machine learning figures#publication visuals#research plotting
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill academic-plotting-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: academic-plotting
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/academic-plotting
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill academic-plotting-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the friction of turning research context, experiment results, and system descriptions into polished paper figures that are clear, reproducible, and venue-ready.

Core Features & Use Cases

  • Architecture and workflow diagrams: Generate publication-quality technical diagrams for models, pipelines, and system overviews.
  • Data-driven charts: Create line plots, grouped bars, heatmaps, scatter plots, and leaderboard figures from quantitative results.
  • Paper-ready styling: Apply conference-friendly typography, colorblind-safe palettes, and consistent export settings for ML publications.
  • Use Case: You have ablation results, training curves, or a method description and want a clean figure for a conference paper without hand-designing every visual element.

Quick Start

Use the academic-plotting skill to turn my experiment results or system description into a publication-ready figure for an ML paper.

Frequently Asked Questions about academic-plotting

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

FAQPage Schema
How do I create publication-ready figures for machine learning papers from experiment tables?▼

To create publication-ready figures for machine learning papers, you can use this skill to process research descriptions and experiment tables into polished charts. It generates conference-ready visual elements like training curves and leaderboards.

Can I generate architecture diagrams and system overviews using matplotlib and seaborn?▼

Yes, you can generate architecture diagrams and system overviews using matplotlib and seaborn. This skill translates system descriptions into publication-quality technical diagrams for models and pipelines.

How do I ensure my research plotting uses colorblind-safe palettes for conference visuals?▼

To ensure research plotting uses colorblind-safe palettes for conference visuals, this skill applies conference-friendly typography and accessibility settings automatically. It handles styling to meet academic publication standards.

What is the best way to export reproducible ablation study charts to PDF or PNG?▼

The best way to export reproducible ablation study charts to PDF or PNG is through this skill's consistent export settings. It generates data-driven heatmaps and scatter plots with reproducible outputs.

Do I need to manually design training curves and leaderboard charts for ML publications?▼

No, you do not need to manually design training curves and leaderboard charts for ML publications. This skill removes the friction by turning quantitative results into clean, automated figures.

What types of data-driven charts can I produce for machine learning figures?▼

For machine learning figures, you can produce data-driven charts including line plots, grouped bars, heatmaps, scatter plots, and leaderboard charts. These are generated from quantitative experiment results.

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