skills/43-wentorai-research-plugins/skills/literature/search/pubmed-api/SKILL.md
Search biomedical literature and retrieve records via PubMed E-utilities
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research pubmed-apiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
PubMed is the premier biomedical literature database maintained by the National Center for Biotechnology Information (NCBI) at the US National Library of Medicine. It indexes over 36 million citations and abstracts from MEDLINE, life science journals, and online books. The Entrez Programming Utilities (E-utilities) provide programmatic access to the entire PubMed database and other NCBI databases.
E-utilities consist of a suite of server-side programs that accept URL-based requests and return structured data. These tools are essential for biomedical researchers, systematic reviewers, and developers building health informatics applications. The API supports complex search queries using MeSH (Medical Subject Headings) terms, boolean operators, and field-specific searches.
The API is free and does not require authentication for basic usage. Registering for an NCBI API key raises the rate limit from 3 to 10 requests per second, which is recommended for any automated workflow.
No authentication required for basic usage (3 requests/second). For higher rate limits (10 requests/second), register for a free API key at https://www.ncbi.nlm.nih.gov/account/ and include it in requests:
&api_key=YOUR_API_KEY
Including tool and email parameters in requests helps NCBI contact you if there are issues with your application.
GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgicurl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=CRISPR+AND+cancer[Title]&retmax=10&retmode=json&sort=pub_date"
esearchresult containing count (total hits), idlist (array of PMIDs), and optionally webenv and querykey for history server.GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgicurl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=33116299,34735795&rettype=abstract&retmode=xml"
MedlineCitation with Article (title, abstract, authors, journal), MeSHHeadingList, and PubmedData (DOI, publication status).GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgicurl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=33116299&retmode=json&version=2.0"
uid, title, authors, source (journal), pubdate, doi, and pmcid.Without API key: 3 requests per second. With API key: 10 requests per second. Exceeding limits results in temporary IP blocking. For large-scale data mining, use the NCBI FTP site for bulk downloads. Always include a delay of at least 334ms (or 100ms with API key) between requests. Weekend and evening hours (US Eastern time) are less congested.
Perform a structured search using MeSH terms and field qualifiers:
# Search for clinical trials on diabetes treatment from the last 2 years
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=diabetes[MeSH]+AND+treatment[Title]+AND+clinical+trial[Publication+Type]&mindate=2024/01/01&maxdate=2026/03/09&datetype=pdat&retmax=100&retmode=json"
Use the history server to efficiently search and then retrieve records:
# Step 1: Search and store results
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=machine+learning+AND+radiology&retmax=0&usehistory=y&retmode=json"
# Step 2: Fetch records using WebEnv and query_key from step 1
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&WebEnv=WEBENV_VALUE&query_key=1&retmax=50&rettype=abstract&retmode=xml"
Get JSON summaries for a batch of known PMIDs:
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=33116299,34735795,35363452&retmode=json&version=2.0"
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.