Literature Review

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

A literature review runs in seven phases, documented in full with commands and templates in references/core_workflow.md:

1. Planning and scoping — the question, inclusion and exclusion criteria, and scope.
2. Systematic literature search — multi-database searching with recorded queries.
3. Screening and selection — title/abstract then full-text screening with counts kept for the PRISMA flow.
4. Data extraction and quality assessment — structured extraction and risk-of-bias or quality appraisal.
5. Synthesis and analysis — thematic or quantitative synthesis across studies.
6. Citation verification — every citation checked against the actual source.
7. Document generation — assembling the review with a complete bibliography.

Record every search string and date as you go: a review that cannot reproduce its own search is not systematic. Per-database search guidance and citation styles are in references/search_and_citation.md, and a full worked review is in references/example_workflow.md.

Best Practices

Search Strategy

1. Start with parallel-web: Use parallel-cli search with academic domains for initial broad coverage before querying specialized databases
2. Use multiple databases (minimum 3): Ensures comprehensive coverage — parallel-web counts as one source
3. Include preprint servers: Captures latest unpublished findings
4. Document everything: Search strings, dates, result counts for reproducibility — save all parallel-cli output to sources/
5. Test and refine: Run pilot searches, review results, adjust search terms
6. Sort by citations: When available, sort search results by citation count to surface influential work first
7. Use parallel-cli extract: Fetch full content from promising URLs found during search to verify relevance before full-text screening

Screening and Selection

1. Use multiple databases (minimum 3): Ensures comprehensive coverage
2. Include preprint servers: Captures latest unpublished findings
3. Document everything: Search strings, dates, result counts for reproducibility
4. Test and refine: Run pilot searches, review results, adjust search terms

Screening and Selection

1. Use clear criteria: Document inclusion/exclusion criteria before screening
2. Screen systematically: Title → Abstract → Full text
3. Document exclusions: Record reasons for excluding studies
4. Consider dual screening: For systematic reviews, have two reviewers screen independently

Synthesis

1. Organize thematically: Group by themes, NOT by individual studies
2. Synthesize across studies: Compare, contrast, identify patterns
3. Be critical: Evaluate quality and consistency of evidence
4. Identify gaps: Note what's missing or understudied

Quality and Reproducibility

1. Assess study quality: Use appropriate quality assessment tools
2. Verify all citations: Run verify_citations.py script
3. Document methodology: Provide enough detail for others to reproduce
4. Follow guidelines: Use PRISMA for systematic reviews

Writing

1. Be objective: Present evidence fairly, acknowledge limitations
2. Be systematic: Follow structured template
3. Be specific: Include numbers, statistics, effect sizes where available
4. Be clear: Use clear headings, logical flow, thematic organization

Common Pitfalls to Avoid

1. Single database search: Misses relevant papers; always search multiple databases
2. No search documentation: Makes review irreproducible; document all searches
3. Study-by-study summary: Lacks synthesis; organize thematically instead
4. Unverified citations: Leads to errors; always run verify_citations.py
5. Too broad search: Yields thousands of irrelevant results; refine with specific terms
6. Too narrow search: Misses relevant papers; include synonyms and related terms
7. Ignoring preprints: Misses latest findings; include bioRxiv, medRxiv, arXiv
8. No quality assessment: Treats all evidence equally; assess and report quality
9. Publication bias: Only positive results published; note potential bias
10. Outdated search: Field evolves rapidly; clearly state search date

Integration with Other Skills

This skill works seamlessly with other scientific skills:

Web Search & Extraction (parallel-web skill — PRIMARY)

• parallel-cli search: Broad academic and general web search with domain filtering — use for initial scoping, finding papers, citation chaining, and supplementary searches
• parallel-cli extract: Fetch full content from paper URLs, journal websites, and preprint servers — use for reading abstracts, extracting reference lists, and verifying paper details
• parallel-cli search --include-domains: Academic-focused search across scholarly domains (arxiv.org, pubmed, nature.com, etc.)

Database Access Skills

• gget: PubMed, bioRxiv, COSMIC, AlphaFold, Ensembl, UniProt
• bioservices: ChEMBL, KEGG, Reactome, UniProt, PubChem
• datacommons-client: Demographics, economics, health statistics

Analysis Skills