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-scientific-agent-ski-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: 27 KB
Bundled scripts: none
Version: 1.0
Allowed tools: ReadWriteEditBash
Path: skills/analysis/literature-review-k-dense-ai-scientific-agent-ski-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

This skill instructs the agent to conduct systematic literature reviews across multiple databases (e.g., PubMed, arXiv, bioRxiv, Semantic Scholar), using parallel-web as the primary search tool. It includes supplementary domain-specific tools (gget, bioservices, datacommons-client) for targeted sources, and emphasizes producing outputs in markdown and PDF with verified citations in styles like APA, Nature, and Vancouver.

How it works

  • It defines a multi-phase workflow: planning/scoping, systematic search across databases, deduplication and screening (title, abstract, full-text), data extraction, thematic synthesis, and citation verification.
  • It uses the parallel-web skill via commands such as parallel-cli search for broad discovery and parallel-cli extract to fetch full contents.
  • It recommends specialized tools: gget for PubMed/BioRxiv, bioservices for ChEMBL/KEGG/UniProt, and datacommons-client for demographic data.
  • It requires generating at least 1-2 AI-generated schematics using the scientific-schematics skill, via a described process and a provided Python command snippet to create diagrams.
  • It includes a documented export-and-aggregate workflow using scripts/search_databases.py and a citation verification step via scripts/verify_citations.py before PDF generation.
  • It prescribes final document generation with python scripts/generate_pdf.py my_literature_review.md --citation-style apa and optional flags for formatting.

When to use it

Use this skill for:

  • 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 paper or thesis
  • Investigating state of the art in a domain
  • Identifying gaps and future directions
  • Requiring verified citations and professional formatting

What it can touch

  • Databases and sources via tools listed in the workflow: parallel-cli search, parallel-cli extract, gget, bioservices, datacommons-client.
  • Output artifacts: my_literature_review.md, my_review.pdf, and auxiliary files generated by scripts like search_databases.py, verify_citations.py, and generate_pdf.py.
  • It specifies citation styles: APA, Nature, Vancouver, Chicago, IEEE through the --citation-style option.

Caveats

  • The skill mandates mandatory AI-generated schematics using the scientific-schematics tool and describes how to generate them.
  • It requires documenting search strategies for each database and ensuring the PRISMA-like flow is represented.
  • It emphasizes verification of DOIs and citations prior to final submission via verify_citations.py.
  • It relies on external tools and external database access which may have access or licensing considerations not stated beyond MIT license in the skill metadata.
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 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 r

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
parallel-cli search "your research topic" -q "keyword1" -q "keyword2" \
parallel-cli extract "https://arxiv.org/abs/XXXX.XXXXX" --json
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
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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-scientific-agent-ski-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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