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bgpt-paper-search

Busca artículos científicos y recupera datos experimentales estructurados extraídos de estudios de texto completo a través del servidor BGPT MCP. Devuelve más de 25 campos por artículo, incluyendo métodos, resultados, tamaños de muestra, puntuaciones de calidad y conclusiones. Útil para revisiones bibliográficas, síntesis de evidencia y obtención de detalles experimentales que no están disponibles solo en los resúmenes.

¿Qué es bgpt-paper-search?

Esta skill permite a agentes de IA (como Claude Code, Cursor o agentes que soporten el protocolo MCP) buscar y obtener datos estructurados de artículos científicos a partir de su texto completo, no solo de resúmenes o metadatos superficiales. A diferencia de bases de datos tradicionales como PubMed o Semantic Scholar, BGPT extrae y devuelve más de 25 campos por artículo, incluyendo métodos experimentales detallados, resultados cuantitativos, tamaños de muestra, evaluaciones de calidad y conclusiones de los autores. Esto es especialmente valioso para revisiones sistemáticas, meta-análisis, síntesis de evidencia y cualquier tarea de investigación que requiera información precisa y reproducible de los estudios primarios. La skill se conecta a un servidor MCP remoto (bgpt.pro), no requiere instalación local y ofrece un tier gratuito de 50 búsquedas. Los resultados se entregan en formato JSON estructurado, listos para ser procesados, comparados o incorporados en tablas de evidencia.

Compatible con✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/bgpt-paper-search
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Documentación

Búsqueda de Artículos BGPT

Descripción General

BGPT es un servidor MCP remoto que busca en una base de datos curada de artículos científicos construida a partir de datos experimentales extraídos de estudios de texto completo. A diferencia de las bases de datos bibliográficas tradicionales que devuelven títulos y resúmenes, BGPT devuelve datos estructurados del contenido real del artículo — métodos, resultados cuantitativos, tamaños de muestra, evaluaciones de calidad y más de 25 campos de metadatos por artículo.

Cuándo Usar Esta Skill

Usa esta skill cuando:

  • Busques artículos científicos con detalles experimentales específicos
  • Realices revisiones bibliográficas sistemáticas o exploratorias
  • Necesites encontrar resultados cuantitativos, tamaños de muestra o tamaños del efecto entre estudios
  • Quieras comparar metodologías utilizadas en diferentes estudios
  • Busques artículos con puntuaciones de calidad o clasificación de evidencia
  • Necesites datos estructurados de artículos de texto completo (no solo resúmenes)
  • Construyas tablas de evidencia para meta-análisis o guías clínicas

Configuración

BGPT es un servidor MCP remoto — no requiere instalación local. Debes configurarlo en los ajustes MCP de tu agente antes de usarlo; esta skill instruye al agente para que llame a la herramienta MCP search_papers y no habilita el acceso MCP por sí sola.

Claude Desktop / Claude Code

Añade a tu configuración MCP:

{
  "mcpServers": {
    "bgpt": {
      "command": "npx",
      "args": ["mcp-remote", "https://bgpt.pro/mcp/sse"]
    }
  }
}

npm (alternativa)

npx bgpt-mcp

Uso

Una vez configurado el servidor MCP BGPT, llama a su herramienta search_papers a través de la interfaz MCP del agente (no mediante Bash):

Buscar artículos sobre: "eficiencia de la edición genética CRISPR en células humanas"

El servidor devuelve resultados estructurados que incluyen:

  • Título, autores, revista, año, DOI
  • Métodos: Técnicas experimentales, modelos, protocolos
  • Resultados: Hallazgos clave con datos cuantitativos
  • Tamaños de muestra: Número de sujetos/muestras
  • Puntuaciones de calidad: Evaluaciones de calidad del estudio
  • Conclusiones: Conclusiones de los autores e implicaciones

Precios

  • Nivel gratuito: 50 búsquedas por red, no se requiere clave API
  • Nivel de pago: $0.01 por resultado con una clave API de bgpt.pro/mcp

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

adaptyv

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

analytical-method-validation

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include

anndata

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

arbor

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g.

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

autoskill

Observe the user

benchling-integration

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

bids

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biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

bulk-rnaseq

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g.

cellxgene-census

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

clinical-decision-support

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

cobrapy

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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