Academic Research Skills – Open-Source AI Agent for Academic Research

1. What is Academic Research Skills

Academic Research Skills is an open-source academic skill plugin developed by independent developer Cheng-I Wu for Claude Code. It covers the entire workflow of academic research, from topic selection to paper writing, peer review, and revision, through 39+ specialized AI agents. This plugin transforms fragmented research information into complete academic papers that adhere to scholarly standards. Its core innovation lies in introducing mechanisms such as quadruple citation verification, Socratic research guidance, anti-hallucination validation, and experimental trace registration, establishing a rigorous human-AI collaborative pipeline in the field of AI-assisted academic writing.

Technical Positioning and Domain: Belongs to the AI Agent plugin category, specifically designed for academic research scenarios, and operates within the command-line environment of Anthropic's Claude Code. Unlike general-purpose AI writing tools, it focuses on the full lifecycle of academic writing, emphasizing traceability, reproducibility, and logical rigor. It is particularly suitable for empirical research fields that require strict citation management and experimental auditing.

Development Background: Independently developed and open-sourced by Cheng-I Wu from Taiwan, with the aim of addressing the shortcomings of existing AI-assisted writing tools in terms of academic standards. The developer has long been concerned about academic integrity and AI hallucinations, and through the design of multi-layer verification mechanisms and mandatory human confirmation nodes, attempts to achieve a balance between efficiency and academic ethics.

Core Value: Addresses three major pain points in current AI-assisted academic writing: citation hallucination (fabricating non-existent references), non-traceable experiments (including experiments that were not conducted in the paper), and AI flattery (unprincipled alignment with user viewpoints). Academic Research Skills reduces the risk of academic misconduct to a minimum through cross-verification of citations, registration of experimental traces, and the devil's advocate protocol, while maintaining writing efficiency.

Technical Features: Utilizes a multi-agent collaborative architecture, breaking down the academic process into 39+ specialized roles (such as literature searcher, methodology auditor, devil's advocate), each with independent tasks and validation logic; introduces a "mandatory quality inspection gate" mechanism, requiring user confirmation at key stages to prevent AI ghostwriting; supports cross-model validation, allowing the invocation of an independent second model family to perform peer review, reducing systematic bias from a single model.

2. Key Features

• Deep Research (13 Agent Collaboration): Guides researchers through Socratic dialogue to refine vague concepts into falsifiable research questions. Supports the PRISMA systematic review protocol, automatically performing retrieval, inclusion/exclusion, and risk of bias assessment. Compared to traditional literature search methods, this feature not only returns a list of articles but also generates a structured question framework.

• Paper Writing (12 Agent Pipeline): Covers the entire process from outline, abstract, literature review, methodology, results and discussion to the final draft. Supports automatic filling of the IMRaD structure and provides style calibration (e.g., adjusting academic rigor) and bilingual abstract generation. Each section is written by a different agent, reducing inconsistencies in style.

• Peer Review (7 Agent Simulated Review Panel): Simulates a virtual review panel of 5 members (including a Devil's Advocate role), evaluating the clarity of research questions, the reasonableness of methods, the sufficiency of evidence, logical consistency, and ethical compliance through narrative-based evidence anchoring. The Devil's Advocate role prevents the AI from prematurely converging or catering to user preferences via a concession threshold protocol.

• Reference Verification (Quadruple Index Cross-Validation): Utilizes four academic indexing databases—Semantic Scholar, OpenAlex, Crossref, and arXiv—to verify the existence, metadata accuracy, and contextual relevance of each cited reference. Additional three-tier anchor point audits (claim fidelity checks) ensure that every assertion in the paper has a verifiable and authentic reference.

• Experimental Trace Registration (Material Passport): Records external experiment reproducibility locks (such as random seeds and dataset versions), negative results, and known limitations in a dedicated registration form. During the paper writing phase, the system automatically audits the alignment between claims in the paper and experimental evidence, preventing claims from being included in the main text without corresponding ablation experiments.

• Anti-Hallucination and Anti-Flattery (Intent Detection Layer): Built-in Devil's Advocate concession threshold protocol ensures that the AI does not unconditionally compromise when users insist on a certain viewpoint, instead requiring users to provide evidence or reconsider their logic. Additionally, the intent detection layer identifies if users are attempting to bypass guidelines (e.g., requesting the generation of fake references) and actively intercepts such attempts.

• Cross-Model Verification: Supports configuring a second model family (e.g., GPT-4 or local models) to independently perform review and quality inspection tasks, forming a cross-verification mechanism with the main model (Claude). This mechanism effectively reduces systematic cognitive blind spots in specific domains for a single model, enhancing the reliability of review conclusions.

• Multi-Format Delivery: One-click output in Markdown, DOCX, and LaTeX formats, supporting common academic citation styles such as APA 7.0, IEEE, and Chicago. It can also be directly compiled into PDF. A six-dimensional collaborative quality assessment report is generated simultaneously (covering reference accuracy, logical consistency, experimental alignment, etc.), for the author's final self-check.

3. How to Use

1. Environment Requirements: You need to install the official Anthropic CLI tool, Claude Code (supports macOS and Linux systems), and ensure you have a valid Anthropic API key (paid account). It is recommended to use the Claude 3.5 Sonnet or higher model version for optimal results.

2. Install the Plugin: Execute the following command in the Claude Code terminal to install the plugin with one click:

   /plugin marketplace add Imbad0202/academic-research-skills
   

   After installation, you can view all available commands by running /ars-help.

3. Topic Selection and Research Planning: Run /ars-plan to initiate a Socratic-style dialogue. Developers are advised to first provide 2-3 preliminary research directions. The AI will help refine the question through follow-up inquiries and automatically generate research questions, hypotheses, and a draft of the chapter structure. It is recommended to enable the full parameter in the "Deep Research" mode during this phase.

4. Literature Search and Systematic Review: Use the full or systematic-review mode of Deep Research. The plugin will automatically call APIs such as Semantic Scholar to retrieve literature and display the inclusion/exclusion process according to the PRISMA flowchart. Users must confirm the search terms and screening criteria at each step to avoid automation bias.

5. Experiment Registration and Paper Writing: If experiments are involved, first register the experimental parameters and negative results in the Material Passport. Then, invoke the full mode of Academic Paper, where 12 intelligent agents will sequentially generate sections such as the outline, introduction, methodology, results, and discussion in a pipeline fashion. After each section is generated, users can edit it online or request a rewrite.

6. Mid-term and Final Quality Checks: At Stage 2.5 (after methodology is completed) and Stage 4.5 (before the final draft), mandatory quality check gates will be triggered. The system will cross-verify the existence of references, logical consistency, and alignment between claims and experimental evidence. Users must confirm or correct each item individually before proceeding. It is advised to carefully address each warning and return for revisions if necessary.

7. Peer Review and Revisions: Run the full mode of Academic Paper Reviewer to initiate a virtual peer review panel. After receiving the review report, use the rebuttal-audit tool to generate an R&R trace matrix and respond to each reviewer's comment individually. The system will check for any missed critical issues (e.g., missing baseline comparisons).

8. Final Draft Submission: Choose the target journal or conference style (APA/IEEE/Chicago), and execute the output command to generate LaTeX or DOCX files. The plugin will automatically compile the PDF and generate a 6-dimensional quality assessment report. It is recommended to manually proofread the entire manuscript before submission, as AI cannot fully replace human academic judgment.

Notes: All API calls require an internet connection; reference verification depends on the availability of external databases; free accounts have limited API call quotas, so it is recommended to use a paid account to ensure a smooth experience; the plugin is currently optimized for English writing and has limited support for Chinese.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Selection Recommendations: For empirical researchers who need to build a complete paper from scratch, especially in fields such as computer science, psychology, and medicine, Academic Research Skills offers a clear advantage with its full-cycle workflow and citation verification mechanism. However, it requires users to have CLI operation skills and patience with the pipeline. If the primary need is language polishing and format checking before submission, Paperpal or Writefull offer lower learning costs. For citation management during the literature review phase, Scite AI's citation context analysis is more direct. Academic Research Skills is better suited for researchers with extremely high demands for academic rigor who are willing to invest in the learning curve.

Hybrid Usage Recommendations: Consider using Academic Research Skills for topic selection, experiment registration, and initial draft writing, then using Paperpal or Writefull for language refinement and format checking. For citation verification, reference Scite AI's citation classification simultaneously to form multiple layers of assurance.

6. Editor's Summary

Academic Research Skills demonstrates unique technological innovation in the field of AI-assisted academic writing. Its multi-agent collaborative architecture does not simply assign tasks to multiple roles, but instead constructs a "traceable writing process" through mandatory quality inspection gates and anti-flattery protocols. This design philosophy surpasses most similar tools—those typically focus only on generation efficiency while neglecting the most critical aspects of traceability and ethical compliance in academic writing. The quadruple citation verification mechanism and experimental trace registration functionality directly address current industry pain points in AI-generated content, such as "hallucinated citations" and "non-reproducible experiments," offering practical application value.

In terms of practical value, this plugin is particularly suitable for research fields that require strict methodologies (such as computer science, medicine, and psychology), as well as researchers who need systematic reviews or meta-analyses. For writing a single paper, it can reduce the time spent on literature retrieval, writing, peer review, and revisions by 30%-50%. However, the trade-off is that users must adapt to its strict pipeline rhythm. For academic writing education, its 10-stage pipeline and virtual peer review panel can serve as tools for training students in academic standards.

The target audience is clearly defined: researchers with some CLI usage experience, familiar with academic writing standards, and who have extremely high demands for citation accuracy. For complete beginners or users who only need simple polishing, the learning cost may outweigh the benefits. Future development potential lies in expanding the plugin to more IDE platforms (such as VS Code) and enhancing support for Chinese literature, which would significantly expand its user base. Additionally, as Anthropic continues to update Claude Code, the plugin's foundational capabilities are likely to be further strengthened.

Overall, Academic Research Skills is a rare open-source tool in the current ecosystem that deeply integrates academic ethics into product design. It does not pursue the fantasy of "one-click generation of a perfect paper," but instead adheres to the pragmatic philosophy that "AI is a co-pilot," achieving a commendable balance between efficiency and rigor.

7. Application Scenarios

• Computational Empirical Research: Researchers start from the vague observation that "the model performs better," and through Socratic dialogue, break it down into falsifiable hypotheses and ablation experiment designs. During the IMRaD structure writing process, the quality control gate will intercept reference hallucinations and statistical errors, while experimental trace registration ensures that all claims are supported by experiments. The final output is a LaTeX paper that conforms to conference formatting standards.

• Systematic Literature Review: Execute retrieval, inclusion/exclusion, risk of bias assessment, and compliance report generation according to the PRISMA protocol. The plugin automatically displays a flowchart and requires user confirmation at each screening stage, ensuring the reproducibility of the review. Directly outputs a review or meta-analysis paper that complies with academic standards, saving a significant amount of time on manual organization.

• Paper Revision and Review Response: Parse review comments to generate a revision roadmap, and use rebuttal-audit to verify whether the responses address all concerns point by point. The system checks for any missed key baseline comparisons or additional experiments, preventing rejection due to oversight. Particularly suitable for journal papers undergoing multiple rounds of review.

• Interdisciplinary Topic Exploration: When the research direction is not yet clear, exploratory Socratic dialogue prevents the AI from converging too early. The plugin guides researchers in extracting research questions with theoretical contributions from fragmented information, and automatically generates an initial chapter structure and reference list. Ideal for doctoral thesis proposals or grant applications.

• Academic Writing Instruction: Integrate the 10-stage pipeline into graduate courses. After students submit their initial drafts, they are reviewed by a virtual five-person review panel. Classroom revision workshops are then conducted based on structured review reports. Instructors can view students' revision processes through the quality control gate to evaluate their academic judgment.

8. FAQ

Q: Does Academic Research Skills support writing academic papers in Chinese?
A: The current version is primarily optimized for English academic writing. Support for Chinese literature in the citation verification section is limited (databases such as Semantic Scholar mainly contain English literature), and the style calibration does not include Chinese journal formats. It is recommended to continue using traditional writing tools for Chinese papers, or to use this plugin only for English abstracts and literature reviews.

Q: Is the citation verification feature completely free?
A: The plugin itself is open-source and free, but citation verification requires calling APIs from Semantic Scholar, OpenAlex, Crossref, and arXiv. These APIs typically have free quotas (e.g., Semantic Scholar allows 100 requests per minute), and heavy usage may trigger rate limiting or require registration for an API Key. Please check the free policies of each API in the documentation.

Q: Will the plugin modify my existing paper content?
A: No. Academic Research Skills uses a "user confirmation before proceeding" model, where all content generated by the agents must be reviewed and confirmed by the user. The plugin will not automatically overwrite or modify existing user documents. At the quality inspection gate, the system will flag potential issues but will not enforce changes; the final decision remains with the user.

Q: How can I ensure that the citations generated by the plugin are real?
A: The plugin cross-checks citations using four indexes, meaning that the same citation must be found in Semantic Scholar, OpenAlex, Crossref, and arXiv simultaneously. If a citation cannot be found in any two of these databases, the system will mark it as "high risk" and require manual verification. In addition, a three-tier anchor point audit checks whether the context of the citation is consistent with the original literature.

Q: Does the plugin support custom peer review standards?
A: Yes. Users can adjust the concession threshold of the devil's advocate, the weight of peer review dimensions, and the strictness of the quality inspection gate in the configuration file. Specific parameters can be viewed and modified using the /ars-config command. Advanced users can also write custom agent roles to expand the peer review dimensions.

Q: If I don't have a Claude Code account, can I use this plugin?
A: No. The plugin entirely relies on the plugin system of Claude Code and the Anthropic API. Users need to have an Anthropic account and generate an API Key. Free accounts have limited API call quotas; it is recommended to upgrade to a paid account for a stable writing experience.

Q: How often is the plugin updated? How can I get the latest version?
A: The GitHub repository (Imbad0202/academic-research-skills) will release updates irregularly. In Claude Code, you can check and install the latest version using the /plugin marketplace update command. Developers usually describe update details on the GitHub Release page; it is recommended to follow the repository for updates.

9. Project Links

• GitHub Repository: https://github.com/Imbad0202/academic-research-skills