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SKILL.md
npx skills add davila7/claude-code-templates --skill pubmed-database
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.
npx skills add davila7/claude-code-templates --skill pubmed-database
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:
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:
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:
Access PubMed data programmatically using the NCBI E-utilities REST API for automation and bulk operations.
Core API Endpoints:
Basic Workflow:
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:
Citation Matching (via ECitMatch API): Use journal name, year, volume, page, and author to find PMIDs:
By Author and Metadata:
Conduct comprehensive literature searches for systematic reviews and meta-analyses.
PICO Framework (Population, Intervention, Comparison, Outcome): Structure clinical research questions systematically:
Comprehensive Search Strategy:
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:
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:
Citation Links:
Export search results in various formats for citation management and further analysis.
Export Formats:
Clipboard and Collections:
Batch Export via API:
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.
# Recent free full-text RCTs on hypertension
hypertension[mh] AND randomized controlled trial[pt] AND 2023:2024[dp] AND free full text[sb]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# By PMID
12345678[pmid]
# By DOI
10.1056/NEJMoa123456[doi]
# By PMC ID
PMC123456[pmc]Format: journal|year|volume|page|author|key|
Example: Science|2008|320|5880|1185|key1|# 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]# 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]# 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]#1: diabetes mellitus[mh]
#2: cardiovascular diseases[mh]
#3: #1 AND #2 AND risk factors[tiab]# Find related articles programmatically
elink.fcgi?dbfrom=pubmed&db=pubmed&id=PMID&cmd=neighbor# Export citations in MEDLINE format
efetch.fcgi?db=pubmed&id=PMID1,PMID2&rettype=medline&retmode=text