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ARAVINDAN20
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self-review

Extract research paper text and run triple NeurIPS-style reviews with a weighted meta-review.

4|1|Updated Apr 8, 2026
Search Tags:#pdf#text-extraction#peer-review#self-review#academic-review#neurips
One-click install
npx skills add https://github.com/ARAVINDAN20/Claude-Research-Paper-OS --skill self-review-aravindan20
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-review
Source: https://github.com/ARAVINDAN20/Claude-Research-Paper-OS/tree/main/.claude/skills/agent-research-skills/skills/self-review
Command: npx skills add https://github.com/ARAVINDAN20/Claude-Research-Paper-OS --skill self-review-aravindan20

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymupdf4llm, pymupdf, pypdf, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates the critical evaluation of academic papers by extracting text from PDFs, applying structured NeurIPS-style reviews from multiple personas, and generating actionable feedback to improve manuscripts.

Core Features & Use Cases

  • Three-Persona Review: Runs independent reviews from harsh, critical, and open-minded personas and aggregates insights.
  • Automated Text Extraction: Extracts titles and section text from PDFs or LaTeX sources to enable thorough analysis.
  • Actionable Meta-Report: Produces a consolidated score, consensus findings, and concrete suggestions for revisions.

Quick Start

Provide a PDF of your manuscript and let Self-Review generate a NeurIPS-style critique.

Frequently Asked Questions about self-review

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

FAQPage Schema
How do I automate NeurIPS-style peer review for an academic paper?▼

Automate NeurIPS-style peer review by providing a PDF or LaTeX manuscript to trigger triple persona critiques and an aggregated meta-review with actionable feedback. The process extracts text and runs harsh, critical, and open-minded evaluations automatically.

Can I extract text from a PDF for academic review using Python?▼

Extract text from PDFs for academic review using Python dependencies like pymupdf4llm, pymupdf, and pypdf. These libraries parse the document to isolate titles and section text, enabling structured manuscript analysis and critique generation.

What is a multi-persona meta-review and how does it improve manuscript evaluation?▼

A multi-persona meta-review improves manuscript evaluation by running independent critiques from harsh, critical, and open-minded perspectives. It aggregates these insights into a consolidated score with consensus findings and concrete revision suggestions.

Do I need Python 3 to run automated academic paper critiques?▼

Python 3 is required to run automated academic paper critiques, along with dependencies like pymupdf4llm and pypdf. These packages parse PDF sources and format the structured NeurIPS-style review forms.

Does automated peer review work with LaTeX sources or only PDF documents?▼

Automated peer review works with both PDF documents and LaTeX sources. The extraction process parses the input files to summarize content and apply structured NeurIPS-style evaluations.

What are the limitations of automated academic paper reviews?▼

Limitations of automated academic paper reviews include dependency on PDF parsing accuracy and Python 3 environments. Complex formatting may hinder text extraction, and the generated critiques function as structured feedback rather than definitive publication decisions.

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