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bgpt-paper-search

Pesquise artigos científicos e recupere dados experimentais estruturados extraídos de estudos completos por meio do servidor MCP BGPT. Retorna mais de 25 campos por artigo, incluindo métodos, resultados, tamanhos amostrais, pontuações de qualidade e conclusões. Útil para revisões de literatura, síntese de evidências e obtenção de detalhes experimentais não disponíveis apenas em resumos.

O que é bgpt-paper-search?

O BGPT Paper Search é uma skill que integra um servidor MCP remoto, permitindo que agentes de IA (como Claude Desktop, Claude Code, Cursor e Codex) pesquisem em um banco de dados curado de artigos científicos a partir de dados experimentais completos extraídos de textos integrais. Diferente de bases tradicionais que retornam apenas títulos e resumos, o BGPT retorna dados estruturados como métodos, resultados quantitativos, tamanhos amostrais, avaliações de qualidade e mais de 25 metadados por artigo. Essa skill é ideal para revisões sistemáticas, síntese de evidências, comparação de metodologias entre estudos e construção de tabelas de evidências para meta-análises. A configuração é simples: basta adicionar o servidor MCP remoto nas configurações do agente (via JSON para Claude Desktop/Claude Code ou npm) e, em seguida, usar a ferramenta `search_papers` por meio da interface MCP. O serviço oferece um nível gratuito de 50 pesquisas por rede, sem necessidade de chave de API, e um plano pago de $0,01 por resultado com chave de API.

Funciona com✓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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Documentação

BGPT Paper Search

Visão Geral

BGPT é um servidor MCP remoto que pesquisa um banco de dados curado de artigos científicos construído a partir de dados experimentais brutos extraídos de textos completos de estudos. Diferente de bases de dados tradicionais que retornam títulos e resumos, o BGPT retorna dados estruturados do conteúdo real dos artigos — métodos, resultados quantitativos, tamanhos amostrais, avaliações de qualidade e mais de 25 metadados por artigo.

Quando Usar Esta Skill

Use esta skill quando:

  • Pesquisar artigos científicos com detalhes experimentais específicos
  • Conduzir revisões sistemáticas ou de escopo da literatura
  • Encontrar resultados quantitativos, tamanhos amostrais ou tamanhos de efeito entre estudos
  • Comparar metodologias utilizadas em diferentes estudos
  • Buscar artigos com pontuações de qualidade ou classificação de evidências
  • Precisar de dados estruturados de artigos completos (não apenas resumos)
  • Construir tabelas de evidências para meta-análises ou diretrizes clínicas

Configuração

BGPT é um servidor MCP remoto — nenhuma instalação local é necessária. Configure-o nas configurações MCP do seu agente antes do uso; esta skill instrui o agente a chamar a ferramenta MCP search_papers e não habilita acesso MCP por si só.

Claude Desktop / Claude Code

Adicione à sua configuração MCP:

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

npm (alternativa)

npx bgpt-mcp

Uso

Uma vez configurado o servidor MCP BGPT, chame sua ferramenta search_papers por meio da interface MCP do agente (não via Bash):

Pesquise artigos sobre: "eficiência de edição genética CRISPR em células humanas"

O servidor retorna resultados estruturados incluindo:

  • Título, autores, periódico, ano, DOI
  • Métodos: Técnicas experimentais, modelos, protocolos
  • Resultados: Principais descobertas com dados quantitativos
  • Tamanhos amostrais: Número de sujeitos/amostras
  • Pontuações de qualidade: Avaliações de qualidade do estudo
  • Conclusões: Conclusões dos autores e implicações

Preços

  • Nível gratuito: 50 pesquisas por rede, sem necessidade de chave de API
  • Pago: $0,01 por resultado com uma chave de 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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