heyu-233

heyu-233 / engineering-figure-banana

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Engineering Figure Banana is a Codex skill for generating publication-style engineering and computer science figures, with support for Chinese academic papers, bilingual labeling, and editable-figure handoff workflows.

73
2
100% credibility
Found Apr 14, 2026 at 73 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository offers scripts, templates, and workflows for generating publication-quality figures tailored for engineering and computer science papers, distinguishing between AI-assisted conceptual diagrams and locally rendered precise quantitative plots.

How It Works

1
🔍 Discover the tool

While working on your research paper, you stumble upon Engineering Figure Banana, a helper for creating professional diagrams and charts specifically for engineering and computer science topics.

2
📥 Add to your AI assistant

You download the files and place them in your AI helper's skills folder, making it ready to use within your research workflow.

3
🔗 Connect a picture service

You link it to an AI service that draws images, so your assistant can now create visuals just by describing what you need.

4
🔄 Refresh your assistant

You restart your AI helper, and it recognizes the new skill, everything feels set up and smooth.

5
Pick your figure type
🎨
Conceptual diagram

Tell it about a system architecture or workflow, and watch it generate a clear illustration.

📊
Data chart

Share your numbers and layout wishes, and get an exact, publication-ready plot.

✨ Get your perfect figure

Your high-quality diagram or chart appears, ready to drop into your paper with clean labels and professional polish.

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Star Growth

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AI-Generated Review

What is engineering-figure-banana?

Engineering Figure Banana is a Python-based Codex skill that generates publication-ready figures for engineering and CS papers, splitting conceptual diagrams like system architectures and algorithm workflows from precise quantitative plots like benchmarks and heatmaps. It uses Gemini for AI-driven image generation in "image mode" and local matplotlib rendering in "plot mode," supporting bilingual labels for Chinese academic papers and exporting to PNG, PDF, or SVG. Researchers get editable handoffs and prompt templates to fit figures into their workflows without generic image-gen guesswork.

Why is it gaining traction?

It stands out by treating engineering figures as distinct from broad illustration tools—offering agent-native integration for Codex users, precise local plotting for numeric accuracy, and templates tuned for CS domains like electronics schematics or ablation studies. Developers notice the bilingual support, white-background publication styles, and CLI scripts for quick prompt building and generation, making it a targeted engineering figure maker over one-size-fits-all options. With 73 stars, it's pulling interest via github engineering prompts and academic workflows.

Who should use this?

CS grad students drafting systems papers need it for quick architecture diagrams and bar charts. Algorithm researchers handling ablations or heatmaps will appreciate the plot mode's fidelity. Electronics and embedded devs authoring bilingual submissions—especially those in Chinese journals—get value from schematic redraws and multi-panel figures.

Verdict

Try it if you're in engineering figure-heavy research; the bilingual docs and CLI make setup straightforward on Windows or manual on macOS/Linux. At 1.0% credibility and 73 stars, it's early-stage with solid examples but unproven scale—pair with upstream planning tools for best results.

(198 words)

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