review-training

Extracts structured feedback units, source evidence, and categorization from matched academic review/source pairs for LLM fine-tuning.

3|4|Updated Mar 11, 2026

Search Tags:#data extraction #academic review #natural language processing #peer review #llm training #feedback generation

Ready to run

npx skills add https://github.com/nicsuzor/academicOps --skill review-training

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: review-training
Source: https://github.com/nicsuzor/academicOps/tree/main/archived/skills/review-training
Command: npx skills add https://github.com/nicsuzor/academicOps --skill review-training

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the creation of high-quality training data for LLMs by extracting structured feedback-evidence pairs from academic reviews and their source documents.

Core Features & Use Cases

Automated Pair Extraction: Processes matched review/source document pairs to identify specific feedback, locate supporting evidence, and categorize the feedback.
Structured Data Generation: Outputs training data in JSONL format, suitable for fine-tuning LLMs to perform academic peer review.
Use Case: A research lab needs to train an AI assistant to provide constructive feedback on grant proposals. This Skill can process hundreds of past reviews and proposals to build a dataset that teaches the AI to identify common issues, suggest improvements, and cite relevant sections of the proposal.

Quick Start

Run the slash command /review-training to process the next matched pair of review and source documents.

Frequently Asked Questions about review-training

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

FAQPage Schema

How do I generate LLM training data from academic peer reviews?▼

To generate LLM training data from academic peer reviews, this Skill automates the extraction of structured feedback-evidence pairs from matched review and source documents, outputting categorized JSONL datasets suitable for fine-tuning.

What file formats are supported for extracting peer review training data?▼

The peer review training data extraction supports PDF, DOCX, and TXT source formats. It processes these matched academic review and source document pairs to identify specific text matches and generate structured feedback units.

How does natural language processing automate academic review data extraction?▼

Natural language processing automates academic review data extraction by applying pattern recognition and high-level synthesis to locate supporting evidence in source documents, categorizing the matched feedback for LLM fine-tuning datasets.

Can I use extracted peer review data to train an AI for grant proposal feedback?▼

Yes, you can use extracted peer review data to train an AI for grant proposal feedback. The Skill processes past reviews and proposals to build datasets teaching the AI to identify issues, suggest improvements, and cite relevant sections.

What is the best way to structure academic review feedback for LLM fine-tuning?▼

The best way to structure academic review feedback for LLM fine-tuning is generating JSONL files containing feedback units, source evidence, and categorization. This structured format directly supports training models to perform automated peer review tasks.