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:
Generate at minimum ONE schematic or diagram (e.g., PRISMA flow diagram for systematic reviews) 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.
Core Workflow
Literature reviews follow a structured, multi-phase workflow:
Phase 1: Planning and Scoping
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)?"
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)
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
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
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
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.
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
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
Title Screening:
Review all titles against inclusion/exclusion criteria Exclude obviously irrelevant studies Document number excluded at this stage
Abstract Screening:
Read abstracts of remaining studies Apply inclusion/exclusion criteria rigorously Document reasons for exclusion
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
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
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
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
Organize by Themes:
Identify 3-5 major themes across studies Group studies by theme (studies may appear in multiple themes) Note patterns, consensus, and controversies Phase 5: Synthesis and Analysis
Create Review Document from template:
cp assets/review_template.md my_literature_review.md
Write Thematic Synthesis (NOT study-by-study summaries):
Organize Results section by themes or research questions Synthesize findings across multiple studies within each theme Compare and contrast different approaches and results Identify consensus areas and points of controversy Highlight the strongest evidence
Example structure:
3.3.1 Theme: CRISPR Delivery Methods
Multiple delivery approaches have been investigated for therapeutic gene editing. Viral vectors (AAV) were used in 15 studies^1-15^ and showed high transduction efficiency (65-85%) but raised immunogenicity concerns^3,7,12^. In contrast, lipid nanoparticles demonstrated lower efficiency (40-60%) but improved safety profiles^16-23^.
Critical Analysis:
Evaluate methodological strengths and limitations across studies Assess quality and consistency of evidence Identify knowledge gaps and methodological gaps Note areas requiring future research
Write Discussion:
Interpret findings in broader context Discuss clinical, practical, or research implications Acknowledge limitations of the review itself Compare with previous reviews if applicable Propose specific future research directions Phase 6: Citation Verification
CRITICAL: All citations must be verified for accuracy before final submission.
Verify All DOIs:
python scripts/verify_citations.py my_literature_review.md
This script:
Extracts all DOIs from the document Verifies each DOI resolves correctly Retrieves metadata from CrossRef Generates verification report Outputs properly formatted citations
Review Verification Report:
Check for any failed DOIs Verify author names, titles, and publication details match Correct any errors in the original document Re-run verification until all citations pass
Format Citations Consistently:
Choose one citation style and use throughout (see references/citation_styles.md) Common styles: APA, Nature, Vancouver, Chicago, IEEE Use verification script output to format citations correctly Ensure in-text citations match reference list format Phase 7: Document Generation
Generate PDF:
python scripts/generate_pdf.py my_literature_review.md \ --citation-style apa \ --output my_review.pdf
Options:
--citation-style: apa, nature, chicago, vancouver, ieee --no-toc: Disable table of contents --no-numbers: Disable section numbering --check-deps: Check if pandoc/xelatex are installed
Review Final Output:
Check PDF formatting and layout Verify all sections are present Ensure citations render correctly Check that figures/tables appear properly Verify table of contents is accurate
Quality Checklist:
All DOIs verified with verify_citations.py Citations formatted consistently PRISMA flow diagram included (for systematic reviews) Search methodology fully documented Inclusion/exclusion criteria clearly stated Results organized thematically (not study-by-study) Quality assessment completed Limitations acknowledged References complete and accurate PDF generates without errors Database-Specific Search Guidance PubMed / PubMed Central
Access via gget skill:
Search PubMed
gget search pubmed "CRISPR gene editing" -l 100
Search with filters
Use PubMed Advanced Search Builder to construct complex queries
Then execute via gget or direct Entrez API
Search tips:
Use MeSH terms: "sickle cell disease"[MeSH] Field tags: [Title], [Title/Abstract], [Author] Date filters: 2020:2024[Publication Date] Boolean operators: AND, OR, NOT See MeSH browser: https://meshb.nlm.nih.gov/search bioRxiv / medRxiv
Access via gget skill:
gget search biorxiv "CRISPR sickle cell" -l 50
Important considerations:
Preprints are not peer-reviewed Verify findings with caution Check if preprint has been published (CrossRef) Note preprint version and date arXiv
Access via direct API or WebFetch:
Example search categories:
q-bio.QM (Quantitative Methods)
q-bio.GN (Genomics)
q-bio.MN (Molecular Networks)
cs.LG (Machine Learning)
stat.ML (Machine Learning Statistics)
Search format: category AND terms
search_query = "cat:q-bio.QM AND ti:\"single cell sequencing\""
Semantic Scholar
Access via direct API (requires API key, or use free tier):
200M+ papers across all fields Excellent for cross-disciplinary searches Provides citation graphs and paper recommendations Use for finding highly influential papers Specialized Biomedical Databases
Use appropriate skills:
ChEMBL: bioservices skill for chemical bioactivity UniProt: gget or bioservices skill for protein information KEGG: bioservices skill for pathways and genes COSMIC: gget skill for cancer mutations AlphaFold: gget alphafold for protein structures PDB: gget or direct API for experimental structures Citation Chaining
Expand search via citation networks:
Forward citations (papers citing key papers):
Use Google Scholar "Cited by" Use Semantic Scholar or OpenAlex APIs Identifies newer research building on seminal work
Backward citations (references from key papers):
Extract references from included papers Identify highly cited foundational work Find papers cited by multiple included studies Citation Style Guide
Detailed formatting guidelines are in references/citation_styles.md. Quick reference:
APA (7th Edition) In-text: (Smith et al., 2023) Reference: Smith, J. D., Johnson, M. L., & Williams, K. R. (2023). Title. Journal, 22(4), 301-318. https://doi.org/10.xxx/yyy Nature In-text: Superscript numbers^1,2^ Reference: Smith, J. D., Johnson, M. L. & Williams, K. R. Title. Nat. Rev. Drug Discov. 22, 301-318 (2023). Vancouver In-text: Superscript numbers^1,2^ Reference: Smith JD, Johnson ML, Williams KR. Title. Nat Rev Drug Discov. 2023;22(4):301-18.
Always verify citations with verify_citations.py before finalizing.
Best Practices Search Strategy Use multiple databases (minimum 3): Ensures comprehensive coverage Include preprint servers: Captures latest unpublished findings Document everything: Search strings, dates, result counts for reproducibility Test and refine: Run pilot searches, review results, adjust search terms Screening and Selection Use clear criteria: Document inclusion/exclusion criteria before screening Screen systematically: Title → Abstract → Full text Document exclusions: Record reasons for excluding studies Consider dual screening: For systematic reviews, have two reviewers screen independently Synthesis Organize thematically: Group by themes, NOT by individual studies Synthesize across studies: Compare, contrast, identify patterns Be critical: Evaluate quality and consistency of evidence Identify gaps: Note what's missing or understudied Quality and Reproducibility Assess study quality: Use appropriate quality assessment tools Verify all citations: Run verify_citations.py script Document methodology: Provide enough detail for others to reproduce Follow guidelines: Use PRISMA for systematic reviews Writing Be objective: Present evidence fairly, acknowledge limitations Be systematic: Follow structured template Be specific: Include numbers, statistics, effect sizes where available Be clear: Use clear headings, logical flow, thematic organization Common Pitfalls to Avoid Single database search: Misses relevant papers; always search multiple databases No search documentation: Makes review irreproducible; document all searches Study-by-study summary: Lacks synthesis; organize thematically instead Unverified citations: Leads to errors; always run verify_citations.py Too broad search: Yields thousands of irrelevant results; refine with specific terms Too narrow search: Misses relevant papers; include synonyms and related terms Ignoring preprints: Misses latest findings; include bioRxiv, medRxiv, arXiv No quality assessment: Treats all evidence equally; assess and report quality Publication bias: Only positive results published; note potential bias Outdated search: Field evolves rapidly; clearly state search date Example Workflow
Complete workflow for a biomedical literature review:
1. Create review document from template
cp assets/review_template.md crispr_sickle_cell_review.md
2. Search multiple databases using appropriate skills
- Use gget skill for PubMed, bioRxiv
- Use direct API access for arXiv, Semantic Scholar
- Export results in JSON format
3. Aggregate and process results
python scripts/search_databases.py combined_results.json \ --deduplicate \ --rank citations \ --year-start 2015 \ --year-end 2024 \ --format markdown \ --output search_results.md \ --summary
4. Screen results and extract data
- Manually screen titles, abstracts, full texts
- Extract key data into the review document
- Organize by themes
5. Write the review following template structure
- Introduction with clear objectives
- Detailed methodology section
- Results organized thematically
- Critical discussion
- Clear conclusions
6. Verify all citations
python scripts/verify_citations.py crispr_sickle_cell_review.md
Review the citation report
cat crispr_sickle_cell_review_citation_report.json
Fix any failed citations and re-verify
python scripts/verify_citations.py crispr_sickle_cell_review.md
7. Generate professional PDF
python scripts/generate_pdf.py crispr_sickle_cell_review.md \ --citation-style nature \ --output crispr_sickle_cell_review.pdf
8. Review final PDF and markdown outputs
Integration with Other Skills
This skill works seamlessly with other scientific skills:
Database Access Skills gget: PubMed, bioRxiv, COSMIC, AlphaFold, Ensembl, UniProt bioservices: ChEMBL, KEGG, Reactome, UniProt, PubChem datacommons-client: Demographics, economics, health statistics Analysis Skills pydeseq2: RNA-seq differential expression (for methods sections) scanpy: Single-cell analysis (for methods sections) anndata: Single-cell data (for methods sections) biopython: Sequence analysis (for background sections) Visualization Skills matplotlib: Generate figures and plots for review seaborn: Statistical visualizations Writing Skills brand-guidelines: Apply institutional branding to PDF internal-comms: Adapt review for different audiences Resources Bundled Resources
Scripts:
scripts/verify_citations.py: Verify DOIs and generate formatted citations scripts/generate_pdf.py: Convert markdown to professional PDF scripts/search_databases.py: Process, deduplicate, and format search results
References:
references/citation_styles.md: Detailed citation formatting guide (APA, Nature, Vancouver, Chicago, IEEE) references/database_strategies.md: Comprehensive database search strategies
Assets:
assets/review_template.md: Complete literature review template with all sections External Resources
Guidelines:
PRISMA (Systematic Reviews): http://www.prisma-statement.org/ Cochrane Handbook: https://training.cochrane.org/handbook AMSTAR 2 (Review Quality): https://amstar.ca/
Tools:
MeSH Browser: https://meshb.nlm.nih.gov/search PubMed Advanced Search: https://pubmed.ncbi.nlm.nih.gov/advanced/ Boolean Search Guide: https://www.ncbi.nlm.nih.gov/books/NBK3827/
Citation Styles:
APA Style: https://apastyle.apa.org/ Nature Portfolio: https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards NLM/Vancouver: https://www.nlm.nih.gov/bsd/uniform_requirements.html Dependencies Required Python Packages pip install requests # For citation verification
Required System Tools
For PDF generation
brew install pandoc # macOS apt-get install pandoc # Linux
For LaTeX (PDF generation)
brew install --cask mactex # macOS apt-get install texlive-xetex # Linux
Check dependencies:
python scripts/generate_pdf.py --check-deps
Summary
This literature-review skill provides:
Systematic methodology following academic best practices Multi-database integration via existing scientific skills Citation verification ensuring accuracy and credibility Professional output in markdown and PDF formats Comprehensive guidance covering the entire review process Quality assurance with verification and validation tools Reproducibility through detailed documentation requirements
Conduct thorough, rigorous literature reviews that meet academic standards and provide comprehensive synthesis of current knowledge in any domain.