chingswy

chingswy / Skill-Research-Figure

Public

论文流程图画图指南

42
0
100% credibility
Found Mar 03, 2026 at 42 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Claude Code skill that generates professional flowchart drafts for academic papers from simple descriptions of research methods.

How It Works

1
📖 Discover the Helper

You hear about a friendly AI skill that turns your fuzzy research ideas into clear diagrams just by chatting.

2
💬 Chat Your Idea

In your AI conversation, you simply describe your paper's method, like inputs flowing through steps to results.

3
🔍 Assistant Gets It

The skill wakes up, understands your pipeline, spots your key innovations, and sketches a draft structure.

4
✅ Review and Tweak

You glance at the proposed layout and colors, give quick feedback, and it adjusts until it feels right.

5
🎨 Magic Diagram Appears

Voila! A polished, professional flowchart pops out with neat boxes, arrows, and highlights on your contributions.

6
💾 Grab Your Picture

You download the ready-to-use image or PDF to drop straight into your research paper.

✨ Paper Figure Ready

Your method is now visually stunning and easy to understand, saving hours of manual drawing.

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AI-Generated Review

What is Skill-Research-Figure?

This Python-based Claude Code skill turns vague paper abstracts or method descriptions into polished flowchart drafts via simple chat prompts. Feed it a pipeline like "text input → CLIP encoding → diffusion denoising → output image," and it extracts structure, highlights novel contributions, picks layouts and colors, then spits out compilable TikZ code as PDF/PNG figures. It solves the drudgery of manual diagramming in tools like Draw.io or TikZ editors, delivering academic-ready visuals in minutes.

Why is it gaining traction?

Unlike generic diagramming apps, it auto-detects research pipelines, emphasizes your contributions with bold frames, and offers five layout templates (linear, branched, looped) plus low-sat color schemes tailored for papers. Auto-compilation and iterative tweaks via feedback make it dead simple—no LaTeX expertise needed. Developers love the "prompt once, refine fast" workflow that fits right into Claude chats.

Who should use this?

ML researchers drafting NeurIPS/ICML papers needing method overviews. PhD students prototyping figure ideas from lit reviews. Anyone in vision/NLP writing up pipelines tired of aligning boxes in PowerPoint.

Verdict

Grab it if you're in research and want quick figure prototypes—works great for drafts, but polish in Inkscape for finals. With 42 stars and 1.0% credibility, it's early-stage (lab-internal vibes, solid examples/docs), so expect some TeX setup hassles; not production-ready yet.

(187 words)

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