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crazymsn / academic-shcolar-skills

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深度云创科技，专注开发 AI 智能体、AI 应用辅助科研、工作、学习！

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85% credibility
Found May 30, 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

Academic Scholar Skills is a comprehensive research-to-paper workflow suite that helps researchers write academic papers with AI assistance while maintaining transparency and integrity. It provides a 10-stage pipeline covering deep research, literature review, paper writing, peer review simulation, and revision — all designed to catch AI hallucinations, verify citations, and keep humans in charge of critical decisions. The system includes multiple AI agents that work together as a research team, with built-in checks to prevent the AI from fabricating sources or making unsupported claims.

How It Works

1
🔬 You start with a research question

You have an idea for a research paper — maybe about AI in education, climate change, or public health — but you're not sure where to begin.

2
🤖 Your AI research team springs to life

You type a simple command like 'I want to write a paper on AI's impact on higher education,' and a team of 13 AI research assistants begins working together to explore your topic.

3
📚 The team searches the literature

The AI researchers scan academic databases, verify that cited sources actually exist, and flag any references that might be made up or unreliable.

4
✍️ You collaborate on writing the paper

A 12-agent writing team helps you draft sections, check your citations, and format everything for your target journal — APA, IEEE, or whatever style you need.

5
The paper gets reviewed
✅
Your paper passes review

The reviewers find your work sound, and you get specific feedback on how to strengthen it.

🔄
You revise based on feedback

You work through reviewer comments with guided revision coaching until the paper is ready.

6
📋 A complete record is created

The system generates a 'Material Passport' documenting your entire research process — sources verified, decisions explained, and collaboration quality assessed.

🎉 Your paper is ready to submit

You have a complete, verified research paper with proper citations, a transparency disclosure statement, and a full audit trail showing how AI helped — and how you stayed in control.

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AI-Generated Review

What is academic-shcolar-skills?

This is a comprehensive research-to-paper workflow suite designed for AI coding agents like Claude Code, Codex, and OpenCode. It provides a structured pipeline that takes you from initial research through literature review, paper writing, peer review simulation, and final formatting. The system uses a multi-agent architecture where specialized agents handle different stages: a 13-agent research team for deep investigation, a 12-agent writing team for drafting, and a 7-agent reviewer panel for simulated peer review. Built in Python, it supports multiple citation formats (APA 7.0, IEEE, Chicago, MLA, Vancouver) and outputs papers in Markdown, LaTeX, DOCX, and PDF formats.

Why is it gaining traction?

The project addresses a real pain point: researchers struggling to use AI effectively without sacrificing academic integrity. Its anti-sycophancy protocols and claim-faithfulness auditing catch AI-generated errors before they become problems. The Material Passport system tracks provenance throughout the pipeline, while built-in compliance checks (PRISMA-trAIce, RAISE) help meet publication standards. The Socratic mentoring mode guides users through research without rushing to conclusions, and the collaboration depth observer scores the human-AI partnership quality. Multi-language support (Chinese and English) with intent-based activation makes it accessible beyond English-only tools.

Who should use this?

Graduate students and early-career researchers who want structured guidance through the paper-writing process. Academic writers who need help with literature reviews and systematic reviews. Researchers concerned about AI hallucination in citations and want built-in verification. Teams collaborating with AI coding agents who need accountability and audit trails. Note: the non-commercial license (CC BY-NC 4.0) prohibits commercial use.

Verdict

With a credibility score of 0.85% and only 16 stars, this is a young, niche project with limited community validation. The extensive documentation, multi-version changelog, and thorough testing infrastructure suggest serious development, but the low adoption rate means you are an early adopter assuming some risk. Try it for literature review and research planning tasks where the anti-hallucination features provide genuine value. For high-stakes publication work, validate outputs carefully until the community grows.

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