literature-review

Plan and execute literature reviews across multiple databases with Markdown and PDF outputs.

11|3|Updated Feb 2, 2026
One-click install
npx skills add https://github.com/gqy20/IssueLab --skill literature-review-gqy20
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: literature-review
Source: https://github.com/gqy20/IssueLab/tree/main/agents/daiduo2/.claude/skills/literature-review
Command: npx skills add https://github.com/gqy20/IssueLab --skill literature-review-gqy20

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Researchers often face fragmented literature scattered across databases and labor-intensive screening workflows. This Skill streamlines the process by aggregating results from multiple databases, organizing screening steps, and producing publication-ready outputs (Markdown and PDF) with verified citations.

Core Features & Use Cases

  • Multi-database search orchestration: Integrates PubMed, arXiv, bioRxiv, Semantic Scholar, and more to gather relevant literature.
  • End-to-end review workflow: Planning, searching, de-duplication, title/abstract/full-text screening, data extraction, thematic synthesis, and reporting.
  • Output generation: Produces professional Markdown and PDF deliverables with citation verification.

Quick Start

  • Use the included scripts to search databases, deduplicate results, verify citations, and generate a formatted literature-review report.
  • Example workflow: run the search_databases.py script to collect results, then verify_citations.py on the Markdown, and finally generate_pdf.py to produce a PDF.

Frequently Asked Questions about literature-review

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

FAQPage Schema
How do I automate a systematic literature review across multiple databases?

To automate a systematic literature review, you can orchestrate multi-database searches across PubMed, arXiv, and bioRxiv, then handle deduplication, screening, and synthesis to produce publication-ready Markdown and PDF reports.

What is the best way to generate a PRISMA-compliant literature review report?

Generating a PRISMA-compliant literature review report involves planning, multi-database searching, de-duplication, and sequential screening to produce verified citation outputs in Markdown and PDF formats.

Can I use this workflow to search PubMed and Semantic Scholar simultaneously?

Yes, you can search PubMed and Semantic Scholar simultaneously. The workflow orchestrates multi-database search aggregation, gathering fragmented literature into a unified set for screening.

How do I verify citations and format a systematic review for publication?

To verify citations and format a systematic review for publication, run citation verification scripts on your Markdown file and use pandoc-based generation to produce a final PDF deliverable.

Do I need any specific Python libraries to run the end-to-end review workflow?

Yes, you need the requests library installed. The workflow also relies on tools like gget, bioservices, CrossRef, and pandoc to execute database searches and generate PDF outputs.

What are the limitations of using automated scripts for full-text screening?

While automated scripts handle planning, searching, and data extraction efficiently, full-text screening often requires researcher intervention to ensure thematic synthesis accuracy and contextual relevance before final publication-ready outputs are generated.