scientific-db-pubmed-database
Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring. Use when a task needs biomedical literature from PubMed rather than general web search.
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原作者说明节选
从固定版本的 SKILL.md 提取,保留原文语言。以下内容属于作者说明,本站尚未验证安装、兼容或运行结果。
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原文涵盖的主题
- PubMed Database
- When to Use
- Query Construction
- MeSH and Subheadings
- Filters
- E-utilities Workflow
- Output Discipline
- Review Checklist
Use this skill when a task needs biomedical literature from PubMed rather than general web search. Searching MEDLINE or life-sciences literature. Building PubMed queries with MeSH terms, field tags, dates, or article types. Looking up PMIDs, abstracts, publication metadata, or related citations. Running systematic-review search passes that need repeatable search strings. Using NCBI E-utilities directly from Python, shell, or another HTTP client. Start with the research question, split it into concepts, then combine concepts with Boolean operators. Useful PubMed field tags: [ti]: title [ab]: abstract [tiab]: title or abstract [au]: author [ta]: journal title abbreviation [mh]: MeSH term [majr]: major MeSH topic [pt]: publication type [dp]: date of publication [la]: language Examples: Prefer MeSH when the concept has a stable controlled-vocabulary term. Combine MeSH with title/abstract terms when the topic is new or terminology varies. Correct subheading syntax puts the subheading before the field tag: Use [majr] only when the topic must be central to the paper. It can improve precision but may miss relevant work. Publication types: clinical trial[pt] meta-analysis[pt] randomized con
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- 来源仓库
- affaan-m/ECC
- 源文件
- skills/scientific-db-pubmed-database/SKILL.md
- 固定版本
- d2ef2680594f9a17526a6e1a2e18b3d242c5d17b
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本条为自动发现的元数据,尚未完成人工文档核查;适用性与运行结果待核查。
原作者:affaan-m/ECC · 原作者仓库元数据。文档核查:未核查。最近来源检查:2026-11-09T16:01:00.494Z。
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