K-Dense-AI/scientific-agent-skills

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.).

90CollectingNetwork accessRuns scriptsWrites files
See how to use itView GitHub source
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill "skills/literature-review"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn literature-review's source instructions into a guide you can follow

According to the pinned SKILL.md from K-Dense-AI/scientific-agent-skills: Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc. ).

npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill "skills/literature-review"
Check the pinned source

Best fit

  • 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

Bring this context

  • A concrete task that matches the documented purpose of literature-review.
  • The files, examples, or context the task depends on.
  • Your constraints, target environment, and definition of done.

Expected outputs

  • A result that follows the pinned literature-review instructions.
  • A concise record of assumptions, inputs used, and unresolved questions.
  • A final check against the source workflow and relevant permission signals.

Key source sections

Read literature-review through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

Core Workflow

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

SKILL.md · Core Workflow
Planning and scoping — the question, inclusion and exclusion criteria, and scope.Systematic literature search — multi-database searching with recorded queries.Screening and selection — title/abstract then full-text screening with counts kept
02

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…

SKILL.md · When to Use This Skill
Conducting a systematic literature review for research or publicationSynthesizing current knowledge on a specific topic across multiple sourcesPerforming meta-analysis or scoping reviews
03

Visual Enhancement with Scientific Schematics

⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.

SKILL.md · Visual Enhancement with Scientific Schematics
Generate at minimum ONE schematic or diagram (e.g., PRISMA flow diagram for systematic reviews)Prefer 2-3 figures for comprehensive reviews (search strategy flowchart, thematic synthesis diagram, conceptual framework)Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
04

Best Practices

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: Captu…

SKILL.md · Best Practices
Start with parallel-web: Use parallel-cli search with academic domains for initial broad coverage before querying specialized databasesUse multiple databases (minimum 3): Ensures comprehensive coverage — parallel-web counts as one sourceInclude preprint servers: Captures latest unpublished findings
05

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: Captu…

SKILL.md · Search Strategy
Start with parallel-web: Use parallel-cli search with academic domains for initial broad coverage before querying specialized databasesUse multiple databases (minimum 3): Ensures comprehensive coverage — parallel-web counts as one sourceInclude preprint servers: Captures latest unpublished findings

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use literature-review to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned literature-review source to [task]. Pay particular attention to these source sections: “Core Workflow”, “When to Use This Skill”, “Visual Enhancement with Scientific Schematics”, “Best Practices”, “Search Strategy”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current literature-review result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “Core Workflow” has been checked.

The source section “When to Use This Skill” has been checked.

The source section “Visual Enhancement with Scientific Schematics” has been checked.

The source section “Best Practices” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

When another Skill is the better fit

FAQ

What does literature-review do?

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc. ).

How do I start using literature-review?

The catalog detected this source-specific install command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill "skills/literature-review". Inspect the command and pinned source before running it.

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
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Repository forks
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Quality
90/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

90/100
Documentation30/30
Specificity23/25
Maintenance20/20
Trust signals17/25

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Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 7 min

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:

Citation Styles:

Dependencies

Required CLI Tools

# parallel-cli (PRIMARY — for web search and URL extraction)
curl -fsSL https://parallel.ai/install.sh | bash
# Or: uv tool install "parallel-web-tools[cli]"
# Authenticate: parallel-cli auth

Required Python Packages

pip install requests  # For citation verification

Required System Tools

# For PDF generation
brew install pandoc  # macOS
apt-get install pandoc  # Linux

# For LaTeX (PDF generation)
brew install --cask mactex  # macOS
apt-get install texlive-xetex  # Linux

Check dependencies:

python scripts/generate_pdf.py --check-deps

Summary

This literature-review skill provides:

  1. Systematic methodology following academic best practices
  2. Parallel-web powered search using parallel-cli search for fast, broad academic literature discovery with scholarly domain filtering
  3. Multi-database integration via existing scientific skills (gget, bioservices, datacommons-client)
  4. Citation verification ensuring accuracy and credibility
  5. Professional output in markdown and PDF formats
  6. Comprehensive guidance covering the entire review process
  7. Quality assurance with verification and validation tools
  8. Reproducibility through detailed documentation requirements

Conduct thorough, rigorous literature reviews that meet academic standards and provide comprehensive synthesis of current knowledge in any domain.

Skill path
skills/literature-review/SKILL.md
Commit SHA
e7ac42510774
Repository license
MIT
Data collected