/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.

Literature Review

Overview

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

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

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 PubMed/PMC for biomedical literature retrieval
• Include bioRxiv/medRxiv preprints when the review scope requires recent non-peer-reviewed evidence
• Use specialized biomedical databases for pathway, structure, variant, and target evidence when relevant

**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:**

• Include protein structure evidence when needed
• Include cancer genomics evidence when needed
• Include demographic, economic, or health statistics when needed
• 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]) AND 2015:2024[Publication Date]

   - **Results**: 247 articles

Repeat for each database searched.

3. **Export and Aggregate Results**:

• Export results in JSON format from each database
• Combine all results into a single file
• Use `scripts/search_databases.py` for post-processing:

     python search_databases.py combined_results.json \
       --deduplicate \
       --format markdown \
       --output aggregated_results.md

Phase 3: Screening and Selection

1. **Deduplication**:

   python search_databases.py results.json --deduplicate --output unique_results.json

• Removes duplicates by DOI (primary) or title (fallback)
• Document number of duplicates removed

2. **Title Screening**:

• Review all titles against inclusion/exclusion criteria
• Exclude obviously irrelevant studies
• Document number excluded at this stage

3. **Abstract Screening**:

• Read abstracts of remaining studies
• Apply inclusion/exclusion criteria rigorously
• Document reasons for exclusion

4. **Full-Text Screening**:

• Obtain full texts of remaining studies
• Conduct detailed review against all criteria
• Document specific reasons for exclusion
• Record final number of included studies

5. **Create PRISMA Flow Diagram**:

   Initial search: n = X
   ├─ After deduplication: n = Y
   ├─ After title screening: n = Z
   ├─ After abstract screening: n = A
   └─ Included in review: n = B

Phase 4: Data Extraction and Quality Assessment

1. **Extract Key Data** from each included study:

• Study metadata (authors, year, journal, DOI)
• Study design and methods
• Sample size and population characteristics
• Key findings and results
• Limitations noted by authors
• Funding sources and conflicts of interest

2. **Assess Study Quality**:

• **For RCTs**: Use Cochrane Risk of Bias tool
• **For observational studies**: Use Newcastle-Ottawa Scale
• **For systematic reviews**: Use AMSTAR 2
• Rate each study: High, Moderate, Low, or Very Low quality
• Consider excluding very low-quality studies

3. **Organize