Agent skill · Code Review & Quality

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.).

majiayu000github.com/majiayu000GitHub ↗
claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill literature-review-k-dense-ai-claude-scientific-wr-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 24 KB
Bundled scripts: none
Allowed tools: ReadWriteEditBash
Path: skills/analysis/literature-review-k-dense-ai-claude-scientific-wr-2/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

Conducts systematic, comprehensive literature reviews following rigorous academic methodology. It searches multiple literature databases, synthesizes findings thematically, verifies citations for accuracy, and generates professional output documents in markdown and PDF formats.

How it works

The skill integrates with tools for database access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation. It outlines a core workflow with phases: Planning and Scoping, Systematic Literature Search (multi-database search, documenting search parameters, exporting results, and aggregating), Screening and Selection (deduplication, title/abstract/full-text screening, PRISMA-like flow), Data Extraction and Quality Assessment, Synthesis and Analysis (thematic synthesis, critical analysis, discussion), Citation Verification (verify DOIs using a script), and Document Generation (generate PDF from markdown with a chosen citation style). It prescribes generating AI-generated schematics using the scientific-schematics skill, described via a command line snippet for creating diagrams. It provides example commands for searching PubMed, bioRxiv, arXiv, and Semantic Scholar, and for post-processing results, deduplication, and generating figures.

When to use it

Use this skill when conducting a systematic literature review for research or publication, synthesizing current knowledge 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, identifying research gaps and future directions, and requiring verified citations and professional formatting.

What it can touch

It references tools for databases and processing: gget (for PubMed, biorxiv, alphafold, etc.), bioservices, datacommons-client, and scripts like scripts/search_databases.py, scripts/verify_citations.py, and scripts/generate_pdf.py. It also mentions a dedicated schematic-generation workflow using scientific-schematics and a Python command:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The workflow includes commands such as:

  • gget search pubmed "search terms"
  • gget search biorxiv "search terms"
  • python search_databases.py combined_results.json --deduplicate --format markdown --output aggregated_results.md
  • 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 --citation-style apa --output my_review.pdf

These are described as part of the stated workflow.

Caveats

The skill requires verification of DOIs before final submission via verify_citations.py, with consistent citation formatting. It mandates inclusion of AI-generated schematics and notes that preprint sources should be handled with caution. It specifies output formats (markdown and PDF) and references standard citation styles (APA, Nature, Vancouver, etc.).

From the SKILL.md

# 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.

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Visual Enhancement with Scientific Schematics
  4. Core Workflow
  5. Phase 1: Planning and Scoping
  6. Phase 2: Systematic Literature Search
  7. Phase 3: Screening and Selection
  8. Phase 4: Data Extraction and Quality Assessment
  9. Phase 5: Synthesis and Analysis
  10. Phase 6: Citation Verification
  11. Phase 7: Document Generation
  12. Database-Specific Search Guidance
  13. PubMed / PubMed Central
  14. bioRxiv / medRxiv
Ships with 1 file
  • metadata.json
Commands it runs
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
More from claude-skill-registry
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About this skill
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 majiayu000/claude-skill-registry --skill literature-review-k-dense-ai-claude-scientific-wr-2 --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 majiayu000/claude-skill-registry, a repository with 534 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.

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