LI

literature-review

Automates the synthesis and organization of academic research from multiple sources.

Install

mkdir -p .claude/skills/literature-review && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/89" && unzip -o skill.zip -d .claude/skills/literature-review && rm skill.zip

Installs to .claude/skills/literature-review

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

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.).
480 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • Conduct systematic literature searches across multiple academic databases
  • Synthesize research findings thematically for meta-analyses or scoping reviews
  • Verify citations for accuracy and format them in styles like APA, Nature, and Vancouver
  • Generate publication-quality scientific schematics and diagrams
  • Produce professional review documents in markdown and PDF formats

How it works

The skill utilizes parallel-cli for broad web-based academic discovery combined with domain-specific database tools like gget and bioservices. It follows a structured workflow of scoping, systematic searching, deduplication, and synthesis, culminating in document generation with mandatory AI-generated schematics.

Inputs & outputs

You give it
Research question defined by PICO framework and search parameters
You get back
Formatted markdown or PDF literature review document with verified citations and scientific figures

When to use literature-review

  • Synthesizing research papers
  • Creating systematic literature reviews
  • Finding sources for tech research
  • Summarizing academic findings
  • Meta-analyzing research data

About this skill

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

  • 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
  • venue-templates: Access venue-specific writing style guides when preparing reviews for publication

Venue-Specific Writing Styles

When preparing a literature review for a specific journal, consult the venue-templates skill for writing style guidance:

  • venue_writing_styles.md: Master style comparison across venues
  • nature_science_style.md: Nature/Science flowing abstract style, story-driven structure
  • cell_press_style.md: Cell Press graphical abstracts, Highlights format
  • medical_journal_styles.md: NEJM/Lancet/JAMA structured abstracts, PRISMA compliance

These guides help adapt your review's tone, abstract format, and structure to match the target venue's expectations.

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:

Tools:


Content truncated.

When not to use it

  • Performing informal or non-academic literature summaries
  • Searching databases without defining a PICO framework or inclusion criteria

Prerequisites

OPENROUTER_API_KEYparallel-clipandocLaTeX (texlive-xetex)

Limitations

  • Requires mandatory inclusion of 1-2 AI-generated figures to be considered complete
  • Dependent on external database availability and API access for specific scientific domains

How it compares

Unlike manual searching, this skill enforces a rigorous, reproducible methodology including automated citation verification, systematic deduplication, and mandatory visual synthesis through integrated schematic generation.

Compared to similar skills

literature-review side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
literature-review (this skill)5592moReviewAdvanced
deep-research02moNo flagsAdvanced
openalex-database487moReviewIntermediate
scientific-critical-thinking187moReviewAdvanced

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Example prompts that trigger this skill in your AI assistant.

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