Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
npxskills add davila7/claude-code-templates--skill pubmed-databaseLoading…
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.
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