review

Launch a multi-agent editorial review of research papers and update state files.

2|1|Updated Feb 18, 2026
One-click install
npx skills add https://github.com/queelius/claude-anvil --skill review-queelius
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: review
Source: https://github.com/queelius/claude-anvil/tree/main/papermill/skills/review
Command: npx skills add https://github.com/queelius/claude-anvil --skill review-queelius

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured, multi-agent system for comprehensive editorial review of research papers, identifying areas for improvement and ensuring readiness for submission.

Core Features & Use Cases

  • Multi-Agent Review: Employs 8 specialist agents (2 literature scouts, 6 domain reviewers) orchestrated by an area chair.
  • Contextual Analysis: Leverages existing paper state (thesis, venue, history) for targeted feedback.
  • Comprehensive Feedback: Evaluates logic, novelty, methodology, prose, citations, and formatting.
  • Use Case: Submit your draft manuscript for a thorough review before sending it to a journal, receiving actionable feedback across multiple dimensions.

Quick Start

Initiate a comprehensive multi-agent review of your paper by running the review skill.

Frequently Asked Questions about review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get automated editorial feedback on a research paper before journal submission?

Yes, a multi-agent review system provides comprehensive manuscript assessment by deploying specialist agents for literature scouting and domain review. These agents evaluate logic, methodology, and prose to deliver targeted feedback for journal readiness.

How does a multi-agent editorial review system evaluate research paper methodology?

A multi-agent editorial review system evaluates methodology by orchestrating six domain reviewer agents alongside two literature scouts, analyzing logic and novelty while referencing existing paper state files to produce a comprehensive, context-aware review report.

Can I integrate existing paper state files to provide context for an academic review?

Integrating existing paper state files for academic review is fully supported. The multi-agent system reads thesis, venue, and history context from the state file to target its editorial feedback, subsequently updating the same file with the completed review history.

What do I need to set up a multi-agent review for an academic manuscript?

Setting up a multi-agent review requires deploying agents for literature scouting, domain review, and orchestration, alongside existing paper state files containing thesis, venue, and history. These components provide the contextual environment needed to execute the review.

What are the limitations of using an automated system for research paper review?

Limitations of this automated research paper review system include its strict dependency on existing paper state files and its requirement for dedicated literature scouting, domain review, and orchestration agents. Without these, the system cannot produce its unified editorial report.