skill-1-official-concern-extraction

Extract atomic concerns, severities, and AC treatment from OpenReview reviews into structured sheets.

1|Updated Apr 9, 2026

Search Tags:#structured-data #review-analysis #openreview #concern-sheet #official-concern-extraction #rebuttal-analysis

Ready to run

npx skills add https://github.com/jinming99/reviewer-under-review --skill skill-1-official-concern-extraction

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-1-official-concern-extraction
Source: https://github.com/jinming99/reviewer-under-review/tree/main/.claude/skills/skill-1-official-concern-extraction
Command: npx skills add https://github.com/jinming99/reviewer-under-review --skill skill-1-official-concern-extraction

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

OpenReview reviews and meta-reviews can be laboriously converted into a structured, auditable concern sheet. This skill automates the extraction of atomic concerns, severities, and treatment decisions to enable reproducible review audits.

Core Features & Use Cases

Extracts atomic concerns, severity, and AC/meta-review treatment to form a consistent concern sheet.
Aggregates reviewer feedback into deduplicated records with provenance (raised_by).
Supports generation of an OfficialConc ernSheet ready for QA, downstream analysis, and benchmarking.

Quick Start

Provide the OpenReview PDFs (reviews + meta-review) and run the extractor to generate the official concern sheet.

Frequently Asked Questions about skill-1-official-concern-extraction

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

FAQPage Schema

How do I extract structured concerns from OpenReview reviews?▼

To extract structured concerns from OpenReview reviews, you provide the review and meta-review PDFs to the extractor, which identifies atomic concerns, severities, and AC treatment to generate a structured concern sheet.

What is an official concern sheet for review analysis?▼

An official concern sheet for review analysis is a structured record aggregating deduplicated atomic concerns, severities, and provenance like raised_by, ready for downstream QA and benchmarking.

How do I parse AC decision drivers and decisive negative IDs from meta-reviews?▼

Parsing AC decision drivers and decisive negative IDs from meta-reviews is handled automatically by analyzing the meta-review text to build compliant concern records that capture the AC's decisive factors.

Can I extract reviewer concerns from both accepted and rejected papers?▼

Yes, you can extract reviewer concerns from both accepted and rejected papers by parsing meta-reviews, rebuttals, and reviewer comments to build structured concern records with full provenance.

What is the best way to automate OpenReview rebuttal analysis?▼

The best way to automate OpenReview rebuttal analysis is using an extractor that parses reviewer comments and rebuttals to deduplicate concerns and map AC treatment decisions into a structured sheet.

Are there limitations to automated concern extraction from academic reviews?▼

Automated concern extraction requires complete OpenReview PDFs including reviews and meta-reviews to accurately identify atomic concerns and AC treatment, lacking inputs may result in incomplete concern records.