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paper-review

Extract methodology, findings, and limitations from academic papers into a knowledge graph.

1|2|Updated Feb 15, 2026
Search Tags:#data extraction#research#knowledge graph#literature review#academic papers#arxiv
One-click install
npx skills add https://github.com/jaminitachi/SuperClaw --skill paper-review-jaminitachi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: paper-review
Source: https://github.com/jaminitachi/SuperClaw/tree/main/skills/paper-review
Command: npx skills add https://github.com/jaminitachi/SuperClaw --skill paper-review-jaminitachi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill transforms raw academic papers into structured, queryable knowledge, preventing information loss and enabling deeper understanding and cross-referencing of research.

Core Features & Use Cases

  • Structured Extraction: Extracts key details like methodology, findings, limitations, and contributions from papers.
  • Knowledge Graph Integration: Stores extracted information and links papers within a persistent knowledge base.
  • Multi-Source Support: Handles papers from Arxiv, DOIs, and local PDF files.
  • Use Case: When you read a new research paper, this Skill will automatically pull out its core methodology, key statistical findings, and any stated limitations, then add it to your personal research knowledge graph.

Quick Start

Use the paper-review skill to analyze the paper located at https://arxiv.org/abs/2301.12345.

Frequently Asked Questions about paper-review

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

FAQPage Schema
How do I extract methodology and statistical findings from an academic paper?▼

To extract methodology and statistical findings from an academic paper, this Skill automatically pulls out core details like limitations and contributions, structuring the data for queryable access.

What is the best way to build a knowledge graph from literature reviews?▼

Building a knowledge graph from literature reviews involves extracting critical information from academic papers and linking related research into a persistent, queryable knowledge base to prevent information loss and enable cross-referencing.

Can I analyze local PDF files and Arxiv URLs for structured data extraction?▼

Yes, you can analyze local PDF files, Arxiv URLs, and DOIs for structured data extraction, allowing comprehensive research analysis by pulling methodology, statistical findings, and limitations from multiple sources.

How do I structure extracted research data to enable cross-referencing?▼

To structure extracted research data for cross-referencing, the Skill integrates critical information like methodology and findings into a knowledge graph, linking related papers within a persistent knowledge base for queryable access.

Does this literature review tool support querying limitations and contributions across multiple papers?▼

Yes, this literature review tool supports querying limitations and contributions across multiple papers by storing extracted information in a persistent knowledge graph that links related research and enables queryable access to scientific literature.

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