A systematic framework for planning and writing academic papers, this project provides two Claude Code skills: academic-paper-strategist and academic-paper-composer. It aims to transform research ideas into submission-ready manuscripts through structured workflows, quality checkpoints, and reviewer simulation, benefiting PhD students, independent researchers, and those working on interdisciplinary projects or preparing for preprint submission.

How It Works

The project employs a two-skill workflow. The academic-paper-strategist guides users through platform analysis, theoretical framework development (including literature and gap analysis), and outline optimization, culminating in a reviewer-assessed outline. The academic-paper-composer then takes this outline to draft the manuscript, starting with foundational style and chapter planning, proceeding through systematic writing with quality checks, and finishing with final evaluation and submission preparation. This approach leverages platform-specific style learning and evidence-based gap identification, ensuring a rigorous and structured writing process.

Quick Start & Requirements

1. Clone the repository: git clone https://github.com/yourusername/academic-paper-skills.git
2. Copy skills to your Claude Code skills directory:
   • cp -r strategist ~/.claude/skills/academic-paper-strategist
   • cp -r composer ~/.claude/skills/academic-paper-composer
3. Restart Claude Code to load the skills.

Prerequisites include Claude Code installed and configured, and Python 3.8+ for verification scripts.

Highlighted Details

• End-to-End Pipeline: Manages the entire process from initial idea generation to a polished, submission-ready manuscript.
• Evidence-Based Gap Identification: Ensures every identified research gap is supported by 3-5 citations.
• Reviewer Simulation: Utilizes a 7-dimension, 35-point assessment system for evaluating outlines.
• Quality Assurance: Incorporates 3 validation gates and 2 Python verification scripts for manuscript quality.
• Preprint Platform Support: Tailored for submission to platforms like PhilArchive, arXiv, PhilSci-Archive, and PsyArXiv.

Maintenance & Community

The project is authored by Li Shixiong, an independent researcher with an ORCID (0009-0008-2001-2865). Contributions are welcome, with guidelines provided in CONTRIBUTING.md. No specific community channels (e.g., Discord, Slack) are listed.

Licensing & Compatibility

The project is released under the MIT License, which is permissive and generally suitable for commercial use and linking within closed-source projects.

Limitations & Caveats

The core functionality relies on the Claude Code AI model, which is a proprietary platform. The provided Python scripts serve as verification tools rather than core components of the writing process itself. No explicit limitations or unsupported platforms are detailed in the README.