Skills Agentes

Literature Review

Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).

Solicitaread write edit bash
Estrellas
34.8k

en todo el repo

Actividad
70

0–100, la ruta de este skill

Actualizado
hace 15 días

último commit aquí

Commits
12

últimos 90 días

Contexto
3.2k tok

120 tok en reposo

Paquete
12 archivos

130 KB

Instalar

Funciona con cualquier agente que lea SKILL.md

npx -y skills add K-Dense-AI/scientific-agent-skills --skill literature-review --agent claude-code

Se instala solo en este repositorio.

Este skill makes network requests, needs API credentials.

Qué hace

  • Busca en múltiples bases de datos académicas (PubMed, arXiv, bioRxiv, Semantic Scholar) con parallel-cli search
  • Ejecuta las siete fases de una revisión sistemática: planificación, búsqueda, cribado, extracción, síntesis, verificación y redacción
  • Verifica todas las citas contra la fuente real con verify_citations.py
  • Genera documentos markdown y PDF profesionales con bibliografía en APA, Nature, Vancouver, etc.
  • Genera al menos un diagrama (p. ej. flujo PRISMA) con la skill scientific-schematics

Úsalo cuando

  • Hacer una revisión bibliográfica sistemática para publicación o tesis
  • Sintetizar el conocimiento actual sobre un tema en varias fuentes
  • Hacer un meta-análisis o scoping review
  • Escribir la sección de revisión de literatura de un paper

No lo uses cuando

    Qué lo activa

    Di cualquiera de estas frases y el agente debería cargar este skill.

    • Haz una revisión sistemática de literatura sobre este tema
    • Sintetiza los estudios publicados sobre esta terapia
    • Genera el diagrama PRISMA de mi revisión
    • Verifica las citas de mi documento de revisión

    SKILL.md

    En inglés

    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

    uv 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.

    Reproducido de K-Dense-AI/scientific-agent-skills bajo licencia MIT license. Leer esta página en markdown.

    Archivos

    12 archivos en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.

    Antes de instalar

    Necesita parallel-cli (con `parallel-cli auth`) para la búsqueda web, y pandoc/LaTeX (mactex o texlive-xetex) para generar el PDF.

    Necesita en el PATH:curlpython

    Variables de entorno:OPENROUTER_API_KEY

    Detalles

    Creador
    K-Dense-AI
    Categoría
    Investigación
    Licencia
    MIT license
    Recursos incluidos
    scripts en python + referencias
    Código fuente
    Ver SKILL.md

    Etiquetas

    Más de K-Dense-AI/scientific-agent-skills

    Este repo incluye 163 skills. Si instalas uno, normalmente ya tienes los demás.

    Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.

    Costo de contexto al activarse
    3.7k tok
    Tamaño del paquete
    21 archivos
    Última actualización
    hace 28 días
    investigacion

    Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.

    Costo de contexto al activarse
    5.1k tok
    Tamaño del paquete
    24 archivos
    Última actualización
    hace 15 días
    documentos

    Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.

    Costo de contexto al activarse
    2.7k tok
    Tamaño del paquete
    8 archivos
    Última actualización
    hace 15 días
    diseno ui

    Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

    Costo de contexto al activarse
    3.9k tok
    Tamaño del paquete
    17 archivos
    Última actualización
    hace 15 días
    documentos

    Crea diagramas científicos de calidad de publicación con la IA Nano Banana 2 y refinamiento iterativo inteligente. Gemini 3.6 Flash revisa la calidad y solo regenera si está por debajo del umbral de tu tipo de documento.

    Costo de contexto al activarse
    4.1k tok
    Tamaño del paquete
    6 archivos
    Última actualización
    hace 15 días
    diseno ui

    Consulta y descarga datos públicos de imágenes oncológicas del NCI Imaging Data Commons (IDC): colecciones, acceso DICOM, radiología (CT, MR, PET), patología, metadatos, visualización y licencias. No requiere autenticación.

    Costo de contexto al activarse
    7.6k tok
    Tamaño del paquete
    15 archivos
    Última actualización
    hace 18 días
    datos analitica

    Skills relacionados

    Astropy

    34.8k

    Librería Python central para astronomía y astrofísica: unidades/cantidades, coordenadas, E/S de FITS, tablas, sistemas de tiempo, WCS y cosmología, para implementar o depurar código con Astropy.

    Costo de contexto al activarse
    3.6k tok
    Tamaño del paquete
    8 archivos
    Última actualización
    el mes pasado
    investigacion

    Integración con el SDK de Python de Benchling y su API REST para entidades del registro, inventario, entradas del cuaderno electrónico (ELN), workflows, Benchling Apps y consultas al Data Warehouse.

    Costo de contexto al activarse
    1.9k tok
    Tamaño del paquete
    6 archivos
    Última actualización
    el mes pasado
    investigacion

    Busca papers científicos y obtiene datos experimentales estructurados extraídos de estudios a texto completo vía el servidor MCP de BGPT: más de 25 campos por paper (métodos, resultados, muestras, calidad, conclusiones).

    Costo de contexto al activarse
    713 tok
    Tamaño del paquete
    1 archivo
    Última actualización
    el mes pasado
    investigacion