Research Writing Skill is a Python-based AI-assisted writing system designed to support academic paper composition through structured workflows rather than single-turn conversations.
The tool addresses the inefficiency and rework common in academic writing by establishing an engineering-oriented collaboration process that tracks progress, preserves context, and enables recovery and reuse. It begins with a seven-round brainstorming phase to align on paper type, discipline, research background, methodology, and chapter structure, then routes tasks to discipline-specific modules. The system emphasizes preserving research substance—data ranges, methodological constraints, indicator definitions, and conclusion boundaries—rather than compressing text for brevity. It supports writing across multiple stages from topic selection through pre-submission review, generates reproducible data visualizations via Python, and handles LaTeX template integration for direct compilation.
This tool suits undergraduate and graduate researchers, as well as early-career academics working on theses, course papers, or journal submissions. It is particularly valuable for those who benefit from structured process documentation and want to avoid losing context between writing sessions. The system includes discipline-specific modules for engineering, social sciences, medicine, and law, plus dedicated support for literature reviews and bilingual workflows. Unlike general conversational writing assistants, it prioritizes maintaining factual precision and methodological rigor over stylistic compression, and it outputs project files—markdown, plain text, or LaTeX—rather than finished Word documents, enabling version control and iterative refinement.
Development activity shows sustained engagement with the project's core mission. The codebase maintains comprehensive documentation across multiple platform integrations including Claude Code, Cursor, Codex, OpenCode, and Gemini CLI, indicating ongoing effort to support diverse user environments. The project includes detailed boundary specifications for its de-AI-ification approach, demonstrating thoughtful iteration on how to balance automation with research integrity. Environment automation features for Miniconda setup, virtual environment creation, and dependency management suggest attention to reducing friction in the user's local setup process.
