HuiyuLi-2000

HuiyuLi-2000 / gen-pseudocode-skill

Public

Reconstruct publication-quality LaTeX pseudocode (algorithm2e) from academic papers, source code, and research projects.

16
1
100% credibility
Found May 12, 2026 at 16 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 guidelines, notation mappings, style tips, and a compilation script to help researchers create publication-quality pseudocode from papers and code.

How It Works

1
📰 Discover the Tool

You find this helpful guide while looking for ways to turn your research notes and code into neat, professional algorithm descriptions like those in top papers.

2
📚 Gather Your Materials

You collect sections from your paper, any related code, or notes that describe your method.

3
🔍 Spot the Key Idea

You pinpoint the main new idea in your work, focusing on the core steps rather than everyday details.

4
✨ Draft the Clean Version

Following simple tips on symbols and styles, you write a clear, concise description of your algorithm that highlights what's special.

5
✅ Make It Picture-Perfect

You use the easy tool to turn your description into a polished, printable format that looks ready for a journal.

🎉 Publish-Ready Success

Now you have a beautiful, professional algorithm box with analysis, all set to include in your research paper.

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AI-Generated Review

What is gen-pseudocode-skill?

This Python project helps reconstruct publication-quality LaTeX pseudocode using the algorithm2e package from academic papers, source code, and research projects on GitHub. It provides workflows to turn messy methodology sections or code into clean, compilable pseudocode with complexity analysis, matching top conference standards. Run a simple CLI script to compile the generated .tex files to PDF for immediate submission checks.

Why is it gaining traction?

It stands out with venue-specific style guides (NeurIPS, ICML, etc.) and notation mappings that convert code variables like "model(x)" to proper LaTeX like \(f_{\theta}(\mathbf{x})\), skipping engineering clutter for method-level abstraction. Developers notice the quality checklist and source priority rules that ensure pseudocode highlights novelty without low-level details. The focus on "gen pseudocode skill" delivers ready-to-publish results faster than starting from scratch.

Who should use this?

AI/ML researchers drafting papers for NeurIPS or ICML who need to distill source code and paper descriptions into polished algorithm2e pseudocode. Academic devs handling research projects where code conflicts with prose, prioritizing paper logic. Teams prepping supplements for TPAMI or AAAI submissions requiring formal, compilable LaTeX.

Verdict

Grab the notation and style guides if you're manually reconstructing pseudocode—they're practical for publication-quality output despite 16 stars and 1.0% credibility score signaling early maturity. Skip if you need full automation; it's more skill than tool, but the compile script adds real utility.

(198 words)

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