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skill-2-agentic-concern-extraction

Extract structured agentic concern sheets from agentic review outputs.

1|Updated Apr 9, 2026
Search Tags:#automation#yaml-frontmatter#review-analysis#agentic-concerns#verdict-drivers#conclusion-sheets
One-click install
npx skills add https://github.com/jinming99/reviewer-under-review --skill skill-2-agentic-concern-extraction
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-2-agentic-concern-extraction
Source: https://github.com/jinming99/reviewer-under-review/tree/main/.claude/skills/skill-2-agentic-concern-extraction
Command: npx skills add https://github.com/jinming99/reviewer-under-review --skill skill-2-agentic-concern-extraction

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates turning agentic review outputs into a single, structured concern sheet to streamline evidence synthesis and verdict justification.

Core Features & Use Cases

  • Structured extraction: pulls decisiveness, major/minor concerns, goals, and decision drivers from review outputs (summary.yaml, review.md, adversarial_brief.md, gates.md, scorecard.md) and preserves provenance.
  • Normalization & deduplication: normalizes severities, consolidates duplicates across sources, and records source details for traceability.
  • Output formatting: produces an AgenticConcernSheet compliant with the calibration schema for downstream analysis and auditing.
  • Use Case: use this when building concern-alignment data for a paper, ensuring consistent representation of concerns and verdict drivers across reviews.

Quick Start

Run the agentic concern extractor on a paper's result directory to produce a structured concern sheet.

Frequently Asked Questions about skill-2-agentic-concern-extraction

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

FAQPage Schema
How do I extract structured concerns from agentic review outputs?▼

To extract structured concerns from agentic review outputs, run the agentic concern extractor on a paper's result directory. It integrates major/minor concerns, verdict drivers, and provenance from summary.yaml, review.md, adversarial_brief.md, gates.md, and scorecard.md into a single report.

What is an agentic concern sheet and when do I need one?▼

An agentic concern sheet is a schema-compliant report that normalizes severities, deduplicates concerns, and records source details for traceability. You need one when building concern-alignment data for a paper to ensure consistent representation of concerns across reviews.

How do I normalize severities and deduplicate concerns across multiple review files?▼

Normalize severities and deduplicate concerns by running the extractor across summary.yaml, review.md, adversarial_brief.md, gates.md, and scorecard.md. It consolidates duplicate concerns across sources and produces a schema-compliant AgenticConcernSheet with full provenance for downstream analysis.

Can I use the agentic concern extractor on multiple paper versions or methods?▼

The agentic concern extractor applies to single paper, single method, and single version result directories. It is designed to integrate decisive drivers, major/minor concerns, and decision drivers into a single report for one specific review scope.

What file formats do I need to generate an AgenticConcernSheet?▼

Generating an AgenticConcernSheet requires agentic review outputs in YAML and Markdown formats, specifically summary.yaml, review.md, adversarial_brief.md, gates.md, and scorecard.md. These files provide the decisive drivers, concerns, and origins extracted into the final report.

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