literature-review

Search arXiv and Semantic Scholar to extract and synthesize scholarly literature.

310|45|Updated Feb 22, 2026

Search Tags:#synthesis #screening #data-extraction #arxiv #gap-analysis #literature-review #semantic-scholar

Ready to run

npx skills add https://github.com/Mathews-Tom/armory --skill literature-review-mathews-tom

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: literature-review
Source: https://github.com/Mathews-Tom/armory/tree/main/skills/literature-review
Command: npx skills add https://github.com/Mathews-Tom/armory --skill literature-review-mathews-tom

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Systematic discovery, extraction, and synthesis of academic research on a defined topic, enabling efficient landscape mapping and gap identification.

Core Features & Use Cases

Thematic synthesis across papers to reveal consensus and tensions.
Structured data extraction for questions, methods, results, and references to enable cross-paper comparison.
Use Case: Craft a rigorous related-work section or a comprehensive literature map for a grant proposal.

Quick Start

Provide a topic and run a structured search, screening, extraction, and synthesis to produce a themes-based literature map and gap analysis.

Frequently Asked Questions about literature-review

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

FAQPage Schema

How do I conduct a systematic literature review for AI research using arXiv and Semantic Scholar?▼

To conduct a systematic literature review, this Skill searches arXiv and Semantic Scholar to find papers, then applies structured screening and extraction to organize methods and results. This yields a synthesized literature map with citations and gap analysis.

What is the best way to map the research landscape and identify gaps in ML literature?▼

The best way to map the research landscape involves thematic synthesis across academic papers to reveal consensus and tensions. This process outputs a structured extraction highlighting underexplored areas, making gap analysis and scoping reviews highly efficient.

Can I extract data from multiple papers to compare methods and results for a related-work section?▼

Yes, you can perform structured data extraction for questions, methods, and results across discovered papers. This enables direct cross-paper comparison, providing the organized thematic synthesis needed to craft a rigorous related-work section.

How do I screen and extract academic papers to draft a comprehensive scoping review?▼

You provide a defined research question, and the Skill executes a structured search, screening, and extraction phase. It organizes the scholarly literature thematically, outputting a narrative-ready synthesis with citations suitable for scoping reviews.

Does this literature review approach work for landscape mapping outside of AI and ML domains?▼

This approach is tailored for AI and ML domains, applying systematic discovery and extraction across sources like arXiv and Semantic Scholar. While optimized for AI research, its thematic synthesis mechanism can technically process general scholarly literature.

Why do I need a structured extraction for systematic literature discovery instead of manual searching?▼

A structured extraction replaces manual searching by automating the organization of questions, methods, and references. It prevents missing critical papers during landscape mapping and directly outputs a tabular synthesis with gap analysis, saving research time.