academic-paper-reviewer
Has your agent review academic papers with different roles. It simulates a panel of reviewers to evaluate the paper from multiple perspectives.
What it does
This agent simulates a panel of reviewers for academic papers. It analyzes the paper from multiple perspectives, such as journal fit, methodology, and core arguments, to provide a comprehensive review.
When to use it
- ✓When you want a detailed opinion on a scientific paper.
- ✓To check if revisions to a paper have addressed previous comments.
- ✓To challenge the arguments in a paper and find weaknesses.
Example of what to ask your AI
“Review this academic paper on artificial intelligence, focusing on the methodology and clarity of arguments.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The agent sets up a team of 5 reviewers with different specialties and perspectives. Each reviewer analyzes the paper separately, and then an editor compiles the opinions into a decision and a revision roadmap.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add Imbad0202/academic-research-skills --skill academic-paper-reviewerOr download just the file:
curl -L https://raw.githubusercontent.com/Imbad0202/academic-research-skills/main/academic-paper-reviewer/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Academic Paper Reviewer v1.11.1 — Multi-Perspective Academic Paper Review Agent Team
Simulates a complete international journal peer review process: automatically identifies the paper's field, dynamically configures 4 card-backed identities (Journal-Fit Reviewer + 3 peer reviewers), and adds the fixed Devil's Advocate as the fifth execution seat. The five role-separated perspectives cover journal fit, methodology, domain expertise, cross-disciplinary viewpoints, and core argument challenges; a separate editorial synthesizer produces the structured Editorial Decision and Revision Roadmap.
v1.1 Improvements:
- Added Devil's Advocate Reviewer — specifically challenges core arguments, detects logical fallacies, and identifies the strongest counter-arguments
- Added
re-reviewmode — verification review, focused on checking whether revisions address the review comments - Expanded review team from 4 to 5 members
<!-- routing-core:begin -->Routing discipline (v3.9.2): plugin and skills-copy installs do not load this repository's
.claude/CLAUDE.md, so its routing core is repeated below, identical toshared/references/routing_core.md(#892). If routing has not settled when this skill loads, apply the core before dispatching any agent.
Step 0 — Escape hatch check (before any classification): If the user's first message begins with [direct-mode] (case-insensitive byte-0 token, optionally preceded by whitespace/newlines that are stripped on parse), record this fact, strip the prefix and surrounding whitespace from the message, and skip directly to Step 1 explicit-intent handling on the stripped content. The literal [direct-mode] is NOT passed through to the dispatched agent. If the stripped message itself has no clear skill named, Step 1 falls through to Step 3 clarification (the escape hatch bypasses cross-phase clarification (Step 2), not all routing). When the token is honored and the named agent or skill needs inputs the message does not supply, read that agent's or skill's file and ask for what it requires, in its terms. A [direct-mode] token after any other non-whitespace character is not the escape hatch, even when the message asks to skip questions: classify the message through Steps 1-3 as if the token were absent, and do not forward the token. Without the byte-0 token, naming an agent is not explicit intent: such a message goes through Steps 1-3 like any other, so cross-phase materials still get Step 2 clarification.
Otherwise, classify the user's input:
Explicit clear intent — user invokes a specific skill via
/ars-*slash command, or uses an unambiguous trigger keyword that maps to a single skill (e.g., "lit-review this", "review my paper", "draft an abstract"): → Route directly; no clarification, no orchestrator detour. → The request stays explicit when the mode's usual input is absent or a word in it has other everyday senses. A revision request with no reviewer comments is revision mode's "feel certain sections need improvement" case, and "revisar artículo" is the reviewer's trigger. Route to that mode and let the mode handle what is missing; do not reopen the choice of workflow.Cross-phase materials detected — user provides artifacts spanning ≥ 2 pipeline phases without naming a specific skill (e.g., pre-written abstract + pre-collected literature; full draft + reviewer comments + bibliography): → Clarify. Do NOT auto-route to a single-phase agent. List candidate workflows as a-d options in markdown body (NOT via AskUserQuestion tool). See
shared/references/intent_clarification_protocol.mdfor the message template. → Reason: clarification is the safest action when materials don't unambiguously identify intent. (v3.10 active conductor (#134) will handle this via structured intake; v3.9.2 asks.)Ambiguous intent, no materials — user provides no artifacts and no clear request: → Clarify per
shared/references/intent_clarification_protocol.md.
…
FAQ
What is the 'Devil's Advocate'?
This is a special reviewer focused on finding logical flaws and strong counter-arguments to challenge the paper's core ideas.
What are the different review modes?
There are modes for full review, re-review (verification), quick assessment, methodology focus, Socratic guided review, and calibration.
How does the agent know the paper's field?
The agent automatically identifies the paper's field of study to configure the most suitable reviewers.
Details
Original description by the author
Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Tr
Popularity
- GitHub stars
- 52k
- Ranking position
- #1822
- License
- NOASSERTION
- Updated
- 10/13/2026
Comments
- …
More skills from this repository
academic-paper
Helps your agent write academic papers in various modes and formats. It can generate plans, drafts, revisions, and check citations.
Imbad0202/academic-research-skills
academic-pipeline
Orchestrates the entire academic research process, from research to final review. It coordinates multiple steps to ensure quality and integrity.
Imbad0202/academic-research-skills
deep-research
Conducts in-depth academic research on any topic using a team of agents. It helps formulate questions, plan methodology, and review literature.
Imbad0202/academic-research-skills
sr-screener
Helps your agent screen studies for systematic reviews. It defines eligibility rules and blindly evaluates titles and full texts.
Imbad0202/academic-research-skills
Similar skills
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC