How to use academic-research-skills? Claude Code Academic Research Skill Kit

Sat, 06 Jun 2026 22:26:00 +0800

Imbad0202/academic-research-skills is a set of academic research skills for Claude Code. It covers the complete process from research, write, review, revise to finalize. The goal is not to let AI write papers for you, but to tool the tedious research assistance work.

There is a sentence in the README that is very accurate: AI is your copilot, not the pilot. It can help you find literature, organize citations, check logic, do review simulations and format conversions, but the real problem definition, method selection, result interpretation and argumentation sentences should still be the responsibility of the researcher.

What capabilities does it contain?

This project is not a single prompt, but a whole set of Claude Code Skills and command systems. The core modules listed in the README include:

Deep Research: 13-agent research team supporting Socratic facilitation, PRISMA systematic review, intent detection, cross-model checking, and Semantic Scholar API validation;
Academic Paper: 12-agent paper writing process, including style calibration, writing quality check, LaTeX reinforcement, visualization, revision coaching, citation conversion, etc.;
Academic Paper Reviewer: 7-agent multi-perspective peer review, including editor-in-chief, dynamic reviewers and Devil’s Advocate;
Academic Pipeline: 10-stage pipeline with adaptive checkpoints, claim verification, Material Passport and integrity gatekeeping;
Data Access Level Metadata: Mark raw, redacted, verified_only and other data access levels for the skill;
Benchmark Report Schema: Constrain benchmark reports to reduce dishonest comparisons;
Artifact Reproducibility Lockfile: records the configuration related to the reproduction experiment.

It has great ambitions: it is not about “writing an abstract for me”, but about breaking down the academic workflow into stages that can be inspected, tracked, and reviewed.

How to install

README recommends using the Claude Code plug-in to install:

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/plugin marketplace add Imbad0202/academic-research-skills
/plugin install academic-research-skills

After installation, you can try:

`1`	`/ars-plan`

It will help you structure your paper through Socratic dialogue. You can also test the literature review directly:

`1`	`/ars-lit-review "your topic"`

The prerequisites are mainly:

Latest version of Claude Code;
ANTHROPIC_API_KEY;
Optional Pandoc, for DOCX;
Optional tectonic and Source Han Serif TC for APA 7.0 PDF.

The README also mentions that if you use the Codex CLI, you can look at its sibling distribution: academic-research-skills-codex. In other words, the main repository is Claude Code native, but the workflow content has a Codex version.

Why emphasize human-in-the-loop

This project clearly rejects the “fully automated AI scientist” illusion. The README mentions that fully automated research systems will inherit a bunch of failure modes: implementation bugs, illusion of results, misunderstanding bugs as discoveries, method falsification, citation illusion, etc.

academic-research-skills are designed to focus on human-machine collaboration, rather than removing humans. It uses integrity gates, claim verification, citation anchors, cross-model verification and other mechanisms to remind you: AI can help with the hard work, but it cannot assume academic responsibilities for you.

This is important. In academic writing, the most dangerous thing about AI is not that the writing style is machine-like, but that the quotes, data, and conclusions look real but are actually untenable.

Who is it suitable for?

It’s more suitable for these people:

Researchers who are writing papers, reviews, proposals or rebuttals;
People who need to systematically organize literature and citations;
People who want to use Claude Code to manage their academic writing process;
People who want AI to do review simulation and logic checking;
People who need assistance with APA, LaTeX, citation conversion and formatting;
People who can accept human-machine collaboration, but do not want AI to deliver the manuscript fully automatically.

It is not suitable for “cleaning manuscripts” or “hiding traces of AI usage.” The README also clearly states that it is not a humanizer. The goal is to improve quality, not help you cheat.

Risk of use

Be extra cautious with academic AI tools:

Quotations must be manually checked against the original text;
Facts and data cannot be relied upon solely in models;
Thesis opinions must come from your own research judgment;
Schools, journals, and conferences may have disclosure requirements for the use of AI;
Undisclosed data and subject information cannot be handed over to the model casually;
Automatically generated LaTeX, statistical explanations and graphs are also checked.

My advice: use it as a research assistant and review assistant, not as a writing tool. Let it help you ask questions, find loopholes, check formats, and organize citations, but you must control the key arguments yourself.

Summary

academic-research-skills is currently a complete set of academic research AI Skill workflows. The most valuable part of it is not “being able to write a paper”, but splitting literature, writing, review, revision, citation verification and integrity checking into clear stages.

If you are already using Claude Code and have real academic writing tasks, you can seriously look into it. But remember: the AI is the co-pilot, not the driver. Academic responsibility ultimately remains with the author.

Reference sources

Imbad0202/academic-research-skills - GitHub

How to use open-notebook? The open source version of NotebookLM is more suitable for self-built knowledge learning

Sat, 06 Jun 2026 22:26:00 +0800

lfnovo/open-notebook is an open source NotebookLM implementation. The project description says it provides more flexibility and features. It is aimed at learning, note-taking, knowledge organization and private information Q&A.

Products like NotebookLM solve the problem of “learning around materials”: instead of chatting in general, they put papers, documents, notes, web pages and other materials into a space, and let AI answer, summarize and organize around these materials.

What is it suitable for?

open-notebook is more suitable for:

Build your own personal learning database;
Manage courses, papers, and technical documents;
Q&A on private notes;
Organize reading and research materials;
Give the team space to share knowledge;
Want a more flexible deployment than hosted NotebookLM.

If you have a lot of PDFs, Markdown, web excerpts, and notes, folders alone can quickly become cluttered. The meaning of AI notebook is to turn data into conversational, outlineable, and reviewable learning objects.

Differences from ordinary RAG

Ordinary RAG is more focused on engineering components, while open-notebook is more focused on application experience.

RAG solves “how to search and answer”; Notebook tools also need to solve:

How the data is organized;
How to accumulate notes;
How to display cited sources;
How the learning process continues;
How to compare multiple data.

So it’s not just a question and answer interface, it’s a learning workbench.

Use boundaries

Self-built knowledge tools also have pitfalls:

PDF parsing quality will affect answers;
OCR, tables, and image content may not be stable;
Cited sources must be checked;
For private data, please pay attention to the model API and deployment location;
The larger the knowledge base, the more important the organization rules are.

Don’t treat AI notebooks as automatic truth machines. It is more suitable to help you read, help you organize, and help you find clues.

Summary

open-notebook is suitable for people who want to build their own NotebookLM-style knowledge learning tools. If you care about data control, functional flexibility, and scalability, it has more room for compromise than a pure hosting product.

The key to a truly useful tool is not how much data is crammed in, but whether the data is clean, the structure is clear, and whether the answers can be returned to the source.

Reference sources

lfnovo/open-notebook - GitHub

AI工具 on KnightLi Blog

How to use academic-research-skills? Claude Code Academic Research Skill Kit

What capabilities does it contain?

How to install

Why emphasize human-in-the-loop

Who is it suitable for?

Risk of use

Summary

Reference sources

How to use open-notebook? The open source version of NotebookLM is more suitable for self-built knowledge learning

What is it suitable for?

Differences from ordinary RAG

Use boundaries

Summary

Reference sources