literature-review
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.).
npx skills add LeonChaoX/qinyan-academic-skills --skill literature-review --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Conducts systematic, comprehensive literature reviews using multiple databases, synthesizes findings thematically, verifies citations, and generates professional outputs in markdown and PDF formats.
How it works
- Integrates with databases and tools for access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation.
- Enforces visual enhancement: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill generated before finalizing any document.
- Guides users through a Core Workflow with phases: Planning, Systematic Search, Screening, Data Extraction and Quality Assessment, Synthesis, Citation Verification, and Document Generation.
- For search, instructs documenting search parameters for each database, exporting results as JSON, and aggregating into a single file; provides example commands for combining results and deduplication.
- For screening, prescribes deduplication via a Python script, title/abstract/full-text screening, and PRISMA-like flow diagram creation.
- For data extraction and synthesis, instructs extracting study metadata, assessing quality with standard tools, organizing by themes, and performing thematic synthesis rather than study-by-study summaries.
- For citation verification, requires running a verification script to check DOIs, review the verification report, and re-run until all citations pass.
- For document generation, includes commands to create a final PDF with selectable citation styles (apa, nature, chicago, vancouver, ieee) and ensures all sections, figures, and table of contents are correct.
When to use it
Use this skill when conducting systematic literature reviews, meta-analyses, research syntheses, or comprehensive searches across biomedical, scientific, and technical domains, and when you need verified citations and professional formatting.
What it can touch
- Databases and tools via allowed commands: gget (pubmed, biorxiv, alphafold, etc.), bioservices (ChEMBL, KEGG, UniProt, COSMIC), datacommons-client, and scripts for search and verification.
- Output formats: Markdown and PDF generation workflows with citation style options (apa, nature, chicago, vancouver, ieee).
Caveats
- Must include AI-generated schematics before finalizing any document.
- Preprint sources (e.g., bioRxiv) may require caution regarding peer review; verify with CrossRef and subsequent publications.
- Citations must be verified for accuracy prior to final submission using the provided verification workflow.
# 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.
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Core Workflow
- Phase 1: Planning and Scoping
- Phase 2: Systematic Literature Search
- Phase 3: Screening and Selection
- Phase 4: Data Extraction and Quality Assessment
- Phase 5: Synthesis and Analysis
- Phase 6: Citation Verification
- Phase 7: Document Generation
- Database-Specific Search Guidance
- PubMed / PubMed Central
- bioRxiv / medRxiv
python scripts/generate_schematic.py "your diagram description" -o figures/output.png python search_databases.py combined_results.json \ python search_databases.py results.json --deduplicate --output unique_results.json cp assets/review_template.md my_literature_review.md python scripts/verify_citations.py my_literature_review.md python scripts/generate_pdf.py my_literature_review.md \ Search PubMed gget search pubmed "CRISPR gene editing" -l 100 Search with filters Use PubMed Advanced Search Builder to construct complex queries
What does the literature-review skill do?
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.).
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill literature-review --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.