Imbad0202/academic-research-skills is an open-source academic research tool designed to assist researchers in the full academic research pipeline, from initial research to final publication.
Source: README View on GitHub →This project is gaining attention due to its comprehensive approach to academic research, integrating AI assistance for various stages of the research process. It addresses the pain points of researchers who need help with literature review, paper writing, and peer review. The unique technical choice of human-in-the-loop design stands out, aiming to mitigate the limitations of fully autonomous AI research systems.
Source: Synthesis of README and project traitsEnables comprehensive literature review with Socratic dialogue, PRISMA systematic review, and intent detection, among other features.
Source: READMEFacilitates paper writing with style calibration, writing quality checks, LaTeX hardening, and anti-leakage protocols.
Source: READMESupports multi-perspective peer review with quality rubrics and optional cross-model critique, ensuring thorough review processes.
Source: READMEOrchestrates a 10-stage pipeline with adaptive checkpoints, claim verification, and integrity verification to ensure the quality of research outputs.
Source: READMEIntroduces a system to declare data access levels for each skill, ensuring transparency and integrity in data handling.
Source: READMEEach skill declares its task type, aiding in the categorization and understanding of the tool's capabilities.
Source: READMEEnables honest benchmark comparisons with a JSON Schema and linting tool.
Source: READMEIntroduces a lockfile for artifact reproducibility, ensuring that outputs can be reliably reproduced.
Source: READMEThe architecture is modular, with distinct components for research, paper writing, review, and pipeline orchestration. It uses a human-in-the-loop approach, integrating AI assistance with human oversight. Key technical decisions include the use of Claude Code for AI assistance and a comprehensive set of quality gates to ensure research integrity.
Source: Code tree + READMEinfra: Not enough information. | key_deps: Claude Code, Semantic Scholar API, Pandoc | language: Python | framework: Claude Code
Source: README + Code treeThis project is for academic researchers who need assistance with the research process, including literature review, paper writing, and peer review. It is useful in scenarios where researchers require AI assistance to streamline the research process and ensure the quality of their publications.
Source: READMEv3.7.0 (2026-05-05): ARS v3.7.0 — Claude Code Plugin Packaging. Highlights include one-line installation on Claude Code CLI / VS Code / JetBrains and integration with Material Passport.
Source: GitHub ReleasesImbad0202/academic-research-skills is a valuable tool for academic researchers seeking to integrate AI into their research process. Its comprehensive features and focus on research integrity make it a strong candidate for researchers looking to enhance their productivity and ensure the quality of their publications.