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Literature Review

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.

Claude Code Knowledge Pack7/10/2026

Overview

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 uses the parallel-web skill (parallel-cli search) as the primary web search tool for broad academic literature discovery, supplemented by specialized database access skills (gget, bioservices, datacommons-client). It 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.

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
    • Use the parallel-web skill (parallel-cli search) for initial scoping to quickly gauge the landscape before formal database searches

  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. Always start with parallel-web for broad academic coverage, then supplement with domain-specific databases.

    Web-Based Academic Search (parallel-web skill — START HERE):

    • Use parallel-cli search with academic domain filtering for broad scholarly coverage
    • Run two searches: academic-focused + general to catch all relevant sources
    # Academic-focused search across scholarly sources
parallel-cli search "your research topic" -q "keyword1" -q "keyword2" \\
  --json --max-results 10 --excerpt-max-chars-total 27000 \\
  --include-domains "scholar.google.com,arxiv.org,pubmed.ncbi.nlm.nih.gov,semanticscholar.org,biorxiv.org,medrxiv.org,ncbi.nlm.nih.gov,nature.com,science.org,ieee.org,acm.org,springer.com,wiley.com,cell.com,pnas.org,nih.gov" \\
  -o sources/litreview_<topic>-academic.json

# General search for supplementary sources
parallel-cli search "your research topic" -q "keyword1" -q "keyword2" \\
  --json --max-results 10 --excerpt-max-chars-total 27000 \\
  -o sources/litreview_<topic>-general.json
    • Use parallel-cli extract to fetch full content from specific paper URLs or PDFs found in search results
    parallel-cli extract "https://arxiv.org/abs/XXXX.XXXXX" --json

    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]) 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 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

  1. Create Review Document from template:

    cp assets/review_template.md my_literature_review.md

  2. 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^.

  3. 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

  4. 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.

  1. 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

  2. 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

  3. 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

  1. 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

  2. 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

  3. 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:

  1. Forward citations (papers citing key papers):
    • Use parallel-cli search to find p