Summary

This repository provides journal-specific AI "skill packs" for Claude Code/Codex, designed to accelerate academic publishing. Targeting researchers across disciplines, it offers tailored workflows from topic selection to manuscript revision, addressing the unique requirements of over 200 mainstream journals and conferences.

How It Works

The project's core innovation lies in creating distinct "skill packs" for individual academic journals and conferences. These packs encode specific editorial preferences, formatting guidelines, and review cultures, enabling AI models to provide highly tailored assistance. This journal-centric approach surpasses generic academic writing tools by addressing the nuanced differences in publication standards and common rejection points across various top-tier venues.

Quick Start & Requirements

Installation is primarily achieved through the Claude Code plugin marketplace using commands like /plugin marketplace add [URL] and /plugin install [skill-name]. Users require access to Claude Code or Codex. Detailed installation instructions and usage examples are provided within the README.

Highlighted Details

• Extensive Coverage: Encompasses over 200 journals and conferences across Economics, Social Sciences, Natural Sciences, Clinical Medicine, AI/Computer Science, Engineering, and Humanities.
• Depth and Breadth: Offers comprehensive "deep packs" for end-to-end publication workflows (e.g., 18 skills for 《经济研究》) and "broad collections" for journal selection and style guidance.
• Journal-Specific Customization: Each pack is meticulously coded for a single journal, capturing its unique editorial nuances, formatting rules, and review culture.
• Full Publication Lifecycle Support: Skills cover topic selection, literature review, empirical design, manuscript drafting (introduction, tables, figures), submission preparation, and reviewer response strategies.

Maintenance & Community

The project is curated and maintained by Stanford REAP (Empirical Methods Team) and CoPaper.AI, with the underlying AI engine powered by StatsPAI. Community engagement is primarily facilitated through CoPaper.AI's WeChat Official Account.

Licensing & Compatibility

The project is released under the MIT License, permitting broad use, modification, and distribution, including for commercial purposes and integration into closed-source projects.

Limitations & Caveats

The effectiveness of these skill packs is dependent on the capabilities of the Claude Code/Codex AI models. Users must independently verify dynamic journal-specific information such as impact factors, page fees, and submission deadlines against official sources. While designed to significantly aid the publication process, these tools do not guarantee manuscript acceptance.