113 件 ・ 関連度順
概要と使いどころ
Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary). Use when downloading sequences, fetching GenBank/GenPept records, getting document summaries, parsing nested XML, navigating GI deprecation, choosing between rettype+retmode combinations, and parsing into Biopython SeqRecord/SwissProt objects. Covers nucleotide, protein, gene, pubmed, sra, gds, taxonomy, snp, clinvar.
日本語の概要は準備中です。原文の説明を表示しています。
Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.
日本語の概要は準備中です。原文の説明を表示しています。
Produce an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill. Do not use for a literature review over published papers. This skill collects evidence from the open web. A request for the literature on a biomedical, clinical, life-science, or other scientific topic — papers, studies, trials, preprints — belongs to firecrawl-research-papers, which queries Firecrawl's paper index (PubMed, bioRxiv, medRxiv, arXiv) instead of searching websites.
日本語の概要は準備中です。原文の説明を表示しています。
Deterministic deep research via RivalSearchMCP. 9 tools: 5-engine web search (DuckDuckGo/Bing/Yahoo/Mojeek/Wikipedia), 9-platform social search (Reddit/HN/StackOverflow/Dev.to/Medium/ProductHunt/Bluesky/Lobste.rs/Lemmy), 5-source news (Google/Bing/Guardian/GDELT/DDG), 5 academic DBs (OpenAlex/CrossRef/arXiv/PubMed/EuropePMC), GitHub search, website mapping, content extraction with OCR, and research topic synthesis. No API keys required. Use when the user needs web research, competitive analysis, content discovery, or academic paper search.
日本語の概要は準備中です。原文の説明を表示しています。
Produce an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill. Do not use for a literature review over published papers. This skill collects evidence from the open web. A request for the literature on a biomedical, clinical, life-science, or other scientific topic — papers, studies, trials, preprints — belongs to firecrawl-research-papers, which queries Firecrawl's paper index (PubMed, bioRxiv, medRxiv, arXiv) instead of searching websites.
日本語の概要は準備中です。原文の説明を表示しています。
Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.
日本語の概要は準備中です。原文の説明を表示しています。
Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Use whenever the user wants to obtain, download, or fetch a paper's PDF — given a DOI, an arXiv id, a paper title, a citation, or a list of DOIs. Trigger on phrases like "download this paper", "find the PDF for [DOI]", "grab me the [Nature/bioRxiv/arXiv] paper on X", "get the open-access version", "I need this article", or any bulk/batch paper download request, even when the user doesn't explicitly say "PDF" or "DOI". Resolves via Unpaywall → Semantic Scholar → arXiv → PubMed Central → bioRxiv/medRxiv → publisher direct (institutional opt-in) → Sci-Hub mirrors as last-resort fallback.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive citation management for academic research. Search Google Scholar and PubMed 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.
日本語の概要は準備中です。原文の説明を表示しています。
Search academic papers across arXiv, PubMed, Semantic Scholar, bioRxiv, medRxiv, Google Scholar, and more. Get BibTeX citations, download PDFs, analyze citation networks. Use for literature reviews, finding papers, and academic research.
日本語の概要は準備中です。原文の説明を表示しています。
Search CrossRef, OpenAlex, PubMed, Semantic Scholar, and optional Scopus; deduplicate results, download open-access PDFs by DOI, classify papers, monitor new results, and analyze coverage. Use when asked to search literature, monitor a topic, download an open-access paper, classify papers, or analyze literature gaps.
日本語の概要は準備中です。原文の説明を表示しています。
Extracts structured citation data from academic repositories using BeautifulSoup4 with lxml parser. Parses DOI metadata, author affiliations, and reference lists from PubMed, arXiv, and Semantic Scholar HTML.
日本語の概要は準備中です。原文の説明を表示しています。
Assess the scientific integrity and trustworthiness of publications before relying on their findings. Use this skill whenever evaluating a paper for a workflow, citing a study, building an analysis on published methods, or when a user asks about the reliability of a study. Checks for formal retractions, corrections, expressions of concern, and — critically — informal contradictions where subsequent studies failed to reproduce key findings. Integrates with PubMed, bioRxiv, and Consensus to provide a trust assessment. Use this skill for ANY publication evaluation, retraction checking, author reliability assessment, or when a user says "can I trust this paper", "is this study reliable", "has this been refuted", or "check this publication".
日本語の概要は準備中です。原文の説明を表示しています。
Cross-reference ENCODE data with PubMed, bioRxiv, ClinicalTrials.gov, Open Targets, GTEx, ClinVar, GWAS Catalog, gnomAD, Ensembl, and other scientific databases. Use when the user wants to find publications, preprints, or clinical trials related to ENCODE experiments, chain ENCODE data with other scientific MCP servers, or build translational pipelines from genomic data to clinical application.
日本語の概要は準備中です。原文の説明を表示しています。
Use ENCODE functional genomics data for disease mechanism research. Use when the user wants to connect GWAS variants to regulatory elements, annotate disease-associated loci with functional data, identify therapeutic targets from epigenomic data, build disease regulatory models, cross-reference with clinical trials and drug databases, or conduct any disease-focused, pathology-driven, or clinical variant interpretation workflow. Covers the full pipeline from disease-tissue mapping through GWAS variant annotation, heritability enrichment, cancer epigenomics, drug target identification, and clinical trial cross-referencing. Integrates ENCODE with Open Targets, PubMed, ClinicalTrials.gov, and bioRxiv.
日本語の概要は準備中です。原文の説明を表示しています。
多源深度调研与专业报告生成。适用场景广泛——只要用户的问题涉及需要深度分析的专业话题，就应使用此技能。包括但不限于：(1) 用户明确要求调研/research/综述/报告/发现；(2) 用户提出一个技术或科学话题，话题复杂度需要多源深度分析；(3) 用户要求对比多种技术方案的优劣；(4) 涉及生物医药、蛋白质、基因、药物靶点等需要专业数据库支撑的问题。核心能力：根据问题性质自动组合 arXiv 论文检索 + ToolUniverse 科学工具（UniProt/OpenTargets/PubMed/FAERS 等 1000+ 工具）+ Web 搜索，多源采集数据，交叉分析，生成专业 PDF/DOCX 研究报告。
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to "find papers on", "search research about", "download academic articles", "get citations for", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery. Requires OpenClawCLI installation from clawhub.ai.
日本語の概要は準備中です。原文の説明を表示しています。
Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary). Use when downloading sequences, fetching GenBank/GenPept records, getting document summaries, parsing nested XML, navigating GI deprecation, choosing between rettype+retmode combinations, and parsing into Biopython SeqRecord/SwissProt objects. Covers nucleotide, protein, gene, pubmed, sra, gds, taxonomy, snp, clinvar.
日本語の概要は準備中です。原文の説明を表示しています。
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
日本語の概要は準備中です。原文の説明を表示しています。
Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.
日本語の概要は準備中です。原文の説明を表示しています。
Search scientific papers via the BGPT MCP server and retrieve structured experimental data — methods, results, conclusions, quality scores, and 25+ metadata fields per paper.
日本語の概要は準備中です。原文の説明を表示しています。
Given an article DOI or PubMed ID, discover and download the genomics data files deposited by the authors (VCF, FASTA, H5AD, CSV, JSON, BAM, etc.) from public repositories such as GEO, ENA, Zenodo, Figshare, Dryad, and OSF.
日本語の概要は準備中です。原文の説明を表示しています。
医学证据等级排序与编排层 skill。当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / evidence grading / quality of evidence / 系统评价排序 / 临床证据排序 / 证据金字塔"时触发。本 skill 不直接调用 NCBI/Europe PMC/ClinicalTrials API,而是编排 6 个底层原子 skill(pubmed-eutils / europepmc-search / clinical-trials-v2 / aact-bulk-trials / bioc-fulltext-fetch / pubtator-entity-search)的召回结果,按 ChatGPT 推荐的证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE 自动评级,自动剔除 editorial / letter / comment。优先使用 BioMCP MCP server 做 fan-out(genomoncology/biomcp),无 BioMCP 时走自建 slow-path 兜底。
日本語の概要は準備中です。原文の説明を表示しています。