Pubmed database

Skill ranbot-ai/awesome-skills/skills/pubmed-database

Awesome Claude Skills, Tools for Customizing Claude AI workflows

Install

npx -y skills add ranbot-ai/awesome-skills --skill pubmed-database

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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/R

SKILL.md

5.3 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

PubMed Database

Overview

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.

When to Use This Skill

This skill should be used when:

Searching for biomedical or life sciences research articles
Constructing complex search queries with Boolean operators, field tags, or MeSH terms
Conducting systematic literature reviews or meta-analyses
Accessing PubMed data programmatically via the E-utilities API
Finding articles by specific criteria (author, journal, publication date, article type)
Retrieving citation information, abstracts, or full-text articles
Working with PMIDs (PubMed IDs) or DOIs
Creating automated workflows for literature monitoring or data extraction

Core Capabilities

1. Advanced Search Query Construction

Construct sophisticated PubMed queries using Boolean operators, field tags, and specialized syntax.

Basic Search Strategies:

Combine concepts with Boolean operators (AND, OR, NOT)
Use field tags to limit searches to specific record parts
Employ phrase searching with double quotes for exact matches
Apply wildcards for term variations
Use proximity searching for terms within specified distances

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:

Need comprehensive list of available field tags
Require detailed explanation of search operators
Constructing complex proximity searches
Understanding automatic term mapping behavior
Need specific syntax for date ranges, wildcards, or special characters

Grep pattern for field tags: \[au\]|\[ti\]|\[ab\]|\[mh\]|\[pt\]|\[dp\]

2. MeSH Terms and Controlled Vocabulary

Use Medical Subject Headings (MeSH) for precise, consistent searching across the biomedical literature.

MeSH Searching:

[mh] tag searches MeSH terms with automatic inclusion of narrower terms
[majr] tag limits to articles where the topic is the main focus
Combine MeSH terms with subheadings for specificity (e.g., diabetes mellitus/therapy[mh])

Common MeSH Subheadings:

/diagnosis - Diagnostic methods
/drug therapy - Pharmaceutical treatment
/epidemiology - Disease patterns and prevalence
/etiology - Disease causes
/prevention & control - Preventive measures
/therapy - Treatment approaches

Example:

# Diabetes therapy with specific focus
diabetes mellitus, type 2[mh]/drug therapy AND cardiovascular diseases[mh]/prevention & control

3. Article Type and Publication Filtering

Filter results by publication type, date, text availability, and other attributes.

Publication Types (use [pt] field tag):

Clinical Trial
Meta-Analysis
Randomized Controlled Trial
Review
Systematic Review
Case Reports
Guideline

Date Filtering:

Single year: 2024[dp]
Date range: 2020:2024[dp]
Specific date: 2024/03/15[dp]

Text Availability:

Free full text: Add AND free full text[sb] to query
Has abstract: Add AND hasabstract[text] to query

Example:

# Recent free full-text RCTs on hypertension
hypertension[mh] AND randomized controlled trial[pt] AND 2023:2024[dp] AND free full text[sb]

4. Programmatic Access via E-utilities API

Access PubMed data programmatically using the NCBI E-utilities REST API for automation and bulk operations.

Core API Endpoints:

ESearch - Search database and retrieve PMIDs
EFetch - Download full records in various formats
ESummary - Get document summaries
EPost - Upload UIDs for batch processing
ELink - Find related articles and linked data

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:

Without API key: 3 requests/second
With API key: 10 requests/second
A