/literature-review

name: literature-review
description: Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
allowed-tools: Read Write Edit Bash
license: MIT license
metadata:
    skill-author: K-Dense Inc.

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来源仓库: https://github.com/K-Dense-AI/claude-scientific-skills 项目名称: claude-scientific-skills 开源协议: MIT License 收录日期: 2026-04-02

声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权，请联系删除。 -->

Literature Review

Overview

Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature databases, synthesize findings thematically, verify all citations for accuracy, and generate professional output documents in markdown and PDF formats.

This skill integrates with multiple scientific skills for database access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation.

When to Use This Skill

Use this skill when:

• Conducting a systematic literature review for research or publication
• Synthesizing current knowledge on a specific topic across multiple sources
• Performing meta-analysis or scoping reviews
• Writing the literature review section of a research paper or thesis
• Investigating the state of the art in a research domain
• Identifying research gaps and future directions
• Requiring verified citations and professional formatting

Visual Enhancement with Scientific Schematics

**⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**

This is not optional. Literature reviews without visual elements are incomplete. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., PRISMA flow diagram for systematic reviews) 2. Prefer 2-3 figures for comprehensive reviews (search strategy flowchart, thematic synthesis diagram, conceptual framework)

**How to generate figures:**

• Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the schematic

**How to generate schematics:**

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

**When to add schematics:**

• PRISMA flow diagrams for systematic reviews
• Literature search strategy flowcharts
• Thematic synthesis diagrams
• Research gap visualization maps
• Citation network diagrams
• Conceptual framework illustrations
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

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Core Workflow

Literature reviews follow a structured, multi-phase workflow:

Phase 1: Planning and Scoping

1. **Define Research Question**: Use PICO framework (Population, Intervention, Comparison, Outcome) for clinical/biomedical reviews

• Example: "What is the efficacy of CRISPR-Cas9 (I) for treating sickle cell disease (P) compared to standard care (C)?"

2. **Establish Scope and Objectives**:

• Define clear, specific research questions
• Determine review type (narrative, systematic, scoping, meta-analysis)
• Set boundaries (time period, geographic scope, study types)

3. **Develop Search Strategy**:

• Identify 2-4 main concepts from research question
• List synonyms, abbreviations, and related terms for each concept
• Plan Boolean operators (AND, OR, NOT) to combine terms
• Select minimum 3 complementary databases

4. **Set Inclusion/Exclusion Criteria**:

• Date range (e.g., last 10 years: 2015-2024)
• Language (typically English, or specify multilingual)
• Publication types (peer-reviewed, preprints, reviews)
• Study designs (RCTs, observational, in vitro, etc.)
• Document all criteria clearly

Phase 2: Systematic Literature Search

1. **Multi-Database Search**:

Select databases appropriate for the domain:

**Biomedical & Life Sciences:**

• Use `gget` skill: `gget search pubmed "search terms"` for PubMed/PMC
• Use `gget` skill: `gget search biorxiv "search terms"` for preprints
• Use `bioservices` skill for ChEMBL, KEGG, UniProt, etc.

**General Scientific Literature:**

• Search arXiv via direct API (preprints in physics, math, CS, q-bio)
• Search Semantic Scholar via API (200M+ papers, cross-disciplinary)
• Use Google Scholar for comprehensive coverage (manual or careful scraping)

**Specialized Databases:**

• Use `gget alphafold` for protein structures
• Use `gget cosmic` for cancer genomics
• Use `datacommons-client` for demographic/statistical data
• Use specialized databases as appropriate for the domain

2. **Document Search Parameters**:

   ## Search Strategy

   ### Database: PubMed
   - **Date searched**: 2024-10-25
   - **Date range**: 2015-01-01 to 2024-10-25
   - **Search string**:

("CRISPR"[Title] OR "Cas9"[Title]) AND ("sickle cell"[MeSH] OR "SCD"[Title/Abstract])