pubmed-database by davila7/claude-code-templates
npx skills add https://github.com/davila7/claude-code-templates --skill pubmed-databasePubMed 是美国国家医学图书馆提供的综合性数据库,可免费访问 MEDLINE 和生命科学文献。使用布尔运算符、MeSH 术语和字段标签构建高级查询,通过 E-utilities API 以编程方式访问数据,用于系统综述和文献分析。
在以下情况下应使用此技能:
使用布尔运算符、字段标签和专用语法构建复杂的 PubMed 查询。
基本搜索策略:
示例查询:
# 关于糖尿病治疗的最新系统综述
diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2024[dp]
# 比较两种药物的临床试验
(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
# 特定作者的研究
smith ja[au] AND cancer[tiab] AND 2023[dp] AND english[la]
何时查阅 search_syntax.md:
广告位招租
在这里展示您的产品或服务
触达数万 AI 开发者,精准高效
字段标签的 Grep 模式:\[au\]|\[ti\]|\[ab\]|\[mh\]|\[pt\]|\[dp\]
使用医学主题词(MeSH)在生物医学文献中进行精确、一致的搜索。
MeSH 搜索:
diabetes mellitus/therapy[mh])常见 MeSH 副标题:
示例:
# 具有特定重点的糖尿病治疗
diabetes mellitus, type 2[mh]/drug therapy AND cardiovascular diseases[mh]/prevention & control
按出版物类型、日期、文本可用性等属性筛选结果。
出版物类型(使用 [pt] 字段标签):
日期筛选:
2024[dp]2020:2024[dp]2024/03/15[dp]文本可用性:
AND free full text[sb]AND hasabstract[text]示例:
# 关于高血压的最新免费全文 RCT
hypertension[mh] AND randomized controlled trial[pt] AND 2023:2024[dp] AND free full text[sb]
使用 NCBI E-utilities REST API 以编程方式访问 PubMed 数据,用于自动化和批量操作。
核心 API 端点:
基本工作流程:
import requests
# 步骤 1:搜索文章
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/"
search_url = f"{base_url}esearch.fcgi"
params = {
"db": "pubmed",
"term": "diabetes[tiab] AND 2024[dp]",
"retmax": 100,
"retmode": "json",
"api_key": "YOUR_API_KEY" # 可选但推荐
}
response = requests.get(search_url, params=params)
pmids = response.json()["esearchresult"]["idlist"]
# 步骤 2:获取文章详情
fetch_url = f"{base_url}efetch.fcgi"
params = {
"db": "pubmed",
"id": ",".join(pmids),
"rettype": "abstract",
"retmode": "text",
"api_key": "YOUR_API_KEY"
}
response = requests.get(fetch_url, params=params)
abstracts = response.text
速率限制:
最佳实践:
何时查阅 api_reference.md:
API 端点的 Grep 模式:esearch|efetch|esummary|epost|elink|einfo
使用部分引文信息或特定标识符查找文章。
按标识符:
# 按 PMID
12345678[pmid]
# 按 DOI
10.1056/NEJMoa123456[doi]
# 按 PMC ID
PMC123456[pmc]
引文匹配(通过 ECitMatch API):使用期刊名称、年份、卷、页码和作者查找 PMID:
格式:journal|year|volume|page|author|key|
示例:Science|2008|320|5880|1185|key1|
按作者和元数据:
# 第一作者、年份和主题
smith ja[1au] AND 2023[dp] AND cancer[tiab]
# 期刊、卷和页码
nature[ta] AND 2024[dp] AND 456[vi] AND 123-130[pg]
为系统综述和荟萃分析进行全面的文献检索。
PICO 框架(人群、干预、对照、结局):系统地构建临床研究问题:
# 示例:糖尿病治疗效果
# P: diabetes mellitus, type 2[mh]
# I: metformin[nm]
# C: lifestyle modification[tiab]
# O: glycemic control[tiab]
diabetes mellitus, type 2[mh] AND
(metformin[nm] OR lifestyle modification[tiab]) AND
glycemic control[tiab] AND
randomized controlled trial[pt]
全面搜索策略:
# 包含多个同义词和 MeSH 术语
(disease name[tiab] OR disease name[mh] OR synonym[tiab]) AND
(treatment[tiab] OR therapy[tiab] OR intervention[tiab]) AND
(systematic review[pt] OR meta-analysis[pt] OR randomized controlled trial[pt]) AND
2020:2024[dp] AND
english[la]
搜索优化:
何时查阅 common_queries.md:
查询示例的 Grep 模式:diabetes|cancer|cardiovascular|clinical trial|systematic review
使用 PubMed 的搜索历史和 My NCBI 功能来实现高效的研究工作流程。
搜索历史(通过高级搜索):
示例:
#1: diabetes mellitus[mh]
#2: cardiovascular diseases[mh]
#3: #1 AND #2 AND risk factors[tiab]
My NCBI 功能:
RSS 订阅源:为任何搜索创建 RSS 订阅源,以监控您感兴趣领域的新出版物。
查找相关研究并探索引文网络。
相似文章功能:每篇 PubMed 文章都包含基于以下内容预计算的相关文章:
用于相关数据的 ELink:
# 以编程方式查找相关文章
elink.fcgi?dbfrom=pubmed&db=pubmed&id=PMID&cmd=neighbor
引文链接:
以各种格式导出搜索结果,用于引文管理和进一步分析。
导出格式:
剪贴板和集合:
通过 API 批量导出:
# 以 MEDLINE 格式导出引文
efetch.fcgi?db=pubmed&id=PMID1,PMID2&rettype=medline&retmode=text
此技能在 references/ 目录中包含三个全面的参考文件:
完整的 E-utilities API 文档,包括所有九个端点、参数、响应格式和最佳实践。在以下情况下查阅:
PubMed 搜索语法的详细指南,包括字段标签、布尔运算符、通配符和特殊字符。在以下情况下查阅:
针对各种研究场景、疾病类型和方法的示例查询的广泛集合。在以下情况下查阅:
参考文件加载策略:根据具体任务需要,将参考文件加载到上下文中。对于简单的查询或基本搜索,此 SKILL.md 中的信息可能就足够了。对于复杂的操作,请查阅相应的参考文件。
每周安装次数
213
代码仓库
GitHub 星标数
22.6K
首次出现
2026年1月21日
安全审计
安装于
opencode175
gemini-cli162
claude-code156
codex153
cursor152
github-copilot141
PubMed is the U.S. National Library of Medicine's comprehensive database providing free access to MEDLINE and life sciences literature. Construct advanced queries with Boolean operators, MeSH terms, and field tags, access data programmatically via E-utilities API for systematic reviews and literature analysis.
This skill should be used when:
Construct sophisticated PubMed queries using Boolean operators, field tags, and specialized syntax.
Basic Search Strategies :
Example Queries :
# Recent systematic reviews on diabetes treatment
diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2024[dp]
# Clinical trials comparing two drugs
(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
# Author-specific research
smith ja[au] AND cancer[tiab] AND 2023[dp] AND english[la]
When to consult search_syntax.md :
Grep pattern for field tags: \[au\]|\[ti\]|\[ab\]|\[mh\]|\[pt\]|\[dp\]
Use Medical Subject Headings (MeSH) for precise, consistent searching across the biomedical literature.
MeSH Searching :
Common MeSH Subheadings :
Example :
# Diabetes therapy with specific focus
diabetes mellitus, type 2[mh]/drug therapy AND cardiovascular diseases[mh]/prevention & control
Filter results by publication type, date, text availability, and other attributes.
Publication Types (use [pt] field tag):
Date Filtering :
2024[dp]2020:2024[dp]2024/03/15[dp]Text Availability :
AND free full text[sb] to queryAND hasabstract[text] to queryExample :
# Recent free full-text RCTs on hypertension
hypertension[mh] AND randomized controlled trial[pt] AND 2023:2024[dp] AND free full text[sb]
Access PubMed data programmatically using the NCBI E-utilities REST API for automation and bulk operations.
Core API Endpoints :
Basic Workflow :
import requests
# Step 1: Search for articles
base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/"
search_url = f"{base_url}esearch.fcgi"
params = {
"db": "pubmed",
"term": "diabetes[tiab] AND 2024[dp]",
"retmax": 100,
"retmode": "json",
"api_key": "YOUR_API_KEY" # Optional but recommended
}
response = requests.get(search_url, params=params)
pmids = response.json()["esearchresult"]["idlist"]
# Step 2: Fetch article details
fetch_url = f"{base_url}efetch.fcgi"
params = {
"db": "pubmed",
"id": ",".join(pmids),
"rettype": "abstract",
"retmode": "text",
"api_key": "YOUR_API_KEY"
}
response = requests.get(fetch_url, params=params)
abstracts = response.text
Rate Limits :
Best Practices :
When to consult api_reference.md :
Grep pattern for API endpoints: esearch|efetch|esummary|epost|elink|einfo
Find articles using partial citation information or specific identifiers.
By Identifier :
# By PMID
12345678[pmid]
# By DOI
10.1056/NEJMoa123456[doi]
# By PMC ID
PMC123456[pmc]
Citation Matching (via ECitMatch API): Use journal name, year, volume, page, and author to find PMIDs:
Format: journal|year|volume|page|author|key|
Example: Science|2008|320|5880|1185|key1|
By Author and Metadata :
# First author with year and topic
smith ja[1au] AND 2023[dp] AND cancer[tiab]
# Journal, volume, and page
nature[ta] AND 2024[dp] AND 456[vi] AND 123-130[pg]
Conduct comprehensive literature searches for systematic reviews and meta-analyses.
PICO Framework (Population, Intervention, Comparison, Outcome): Structure clinical research questions systematically:
# Example: Diabetes treatment effectiveness
# P: diabetes mellitus, type 2[mh]
# I: metformin[nm]
# C: lifestyle modification[tiab]
# O: glycemic control[tiab]
diabetes mellitus, type 2[mh] AND
(metformin[nm] OR lifestyle modification[tiab]) AND
glycemic control[tiab] AND
randomized controlled trial[pt]
Comprehensive Search Strategy :
# Include multiple synonyms and MeSH terms
(disease name[tiab] OR disease name[mh] OR synonym[tiab]) AND
(treatment[tiab] OR therapy[tiab] OR intervention[tiab]) AND
(systematic review[pt] OR meta-analysis[pt] OR randomized controlled trial[pt]) AND
2020:2024[dp] AND
english[la]
Search Refinement :
When to consult common_queries.md :
Grep pattern for query examples: diabetes|cancer|cardiovascular|clinical trial|systematic review
Use PubMed's search history and My NCBI features for efficient research workflows.
Search History (via Advanced Search):
Example :
#1: diabetes mellitus[mh]
#2: cardiovascular diseases[mh]
#3: #1 AND #2 AND risk factors[tiab]
My NCBI Features :
RSS Feeds : Create RSS feeds for any search to monitor new publications in your area of interest.
Find related research and explore citation networks.
Similar Articles Feature : Every PubMed article includes pre-calculated related articles based on:
ELink for Related Data :
# Find related articles programmatically
elink.fcgi?dbfrom=pubmed&db=pubmed&id=PMID&cmd=neighbor
Citation Links :
Export search results in various formats for citation management and further analysis.
Export Formats :
Clipboard and Collections :
Batch Export via API :
# Export citations in MEDLINE format
efetch.fcgi?db=pubmed&id=PMID1,PMID2&rettype=medline&retmode=text
This skill includes three comprehensive reference files in the references/ directory:
Complete E-utilities API documentation including all nine endpoints, parameters, response formats, and best practices. Consult when:
Detailed guide to PubMed search syntax including field tags, Boolean operators, wildcards, and special characters. Consult when:
Extensive collection of example queries for various research scenarios, disease types, and methodologies. Consult when:
Reference Loading Strategy : Load reference files into context as needed based on the specific task. For brief queries or basic searches, the information in this SKILL.md may be sufficient. For complex operations, consult the appropriate reference file.
Weekly Installs
213
Repository
GitHub Stars
22.6K
First Seen
Jan 21, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
opencode175
gemini-cli162
claude-code156
codex153
cursor152
github-copilot141
Python PDF处理教程:合并拆分、提取文本表格、创建PDF文件
55,400 周安装