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Extract Peer Review Comments

Extract and classify reviewer comments from academic peer review documents into structured JSON.

1|Updated Jan 19, 2026
Search Tags:#nlp#comments#classification#text-extraction#peer-review#document-analysis
One-click install
npx skills add https://github.com/genomewalker/rebuttr --skill extract-peer-review-comments
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Extract Peer Review Comments
Source: https://github.com/genomewalker/rebuttr/tree/main/skills/extract-reviews
Command: npx skills add https://github.com/genomewalker/rebuttr --skill extract-peer-review-comments

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Extracts and classifies reviewer comments from academic peer review documents to enable structured analysis and reporting.

Core Features & Use Cases

  • Automatically detect and group reviewer feedback from documents (DOCX, PDF, TXT).
  • Classify comments into major and minor categories with reviewer attribution.
  • Output a structured JSON summary suitable for downstream analytics and reporting.

Quick Start

Load a peer review document (DOCX, PDF, or text) and run the extraction to produce a structured JSON of reviewer comments.

Frequently Asked Questions about Extract Peer Review Comments

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

FAQPage Schema
How do I extract reviewer comments from a peer review document?▼

Yes, peer review comment extraction supports DOCX, PDF, and plain-text formats. It processes journal decision letters, reviewer reports, and editor feedback to classify comments into structured JSON data ready for downstream analytics.

How are reviewer comments classified during text extraction?▼

Peer review comment extraction categorizes feedback into major and minor classifications with reviewer attribution. It groups comments by reviewer label in a structured JSON format, making it suitable for downstream analytics and reporting tasks.

What is the best way to structure peer review feedback for downstream analytics?▼

To structure peer review feedback for analytics, use automated text extraction to group comments by reviewer and classify them by severity. This produces a structured JSON output suitable for downstream analytics and reporting.

Can I extract comments from editor feedback and decision letters?▼

Yes, text extraction works on editor feedback and journal decision letters. It processes these documents alongside reviewer reports to deliver grouped feedback with reviewer labels and major or minor classifications in a structured JSON format.

Do I need any external dependencies to classify peer review comments?▼

No external dependencies are required to classify peer review comments. The tool operates independently to extract and categorize reviewer feedback from DOCX, PDF, or plain-text documents into a structured JSON output.

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