pubmed-database

Search PubMed and NCBI E-utilities for biomedical literature, MeSH queries, and citations.

Updated May 19, 2026
One-click install
npx skills add https://github.com/azusagasaku/--claude-config --skill pubmed-database-azusagasaku
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pubmed-database
Source: https://github.com/azusagasaku/--claude-config/tree/main/skills/ecc/scientific-db-pubmed-database
Command: npx skills add https://github.com/azusagasaku/--claude-config --skill pubmed-database-azusagasaku

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests.

What problem does it solve? Biomedical research requires precise, reproducible literature searches that general web search cannot deliver. This Skill provides structured PubMed query construction, MeSH term usage, and direct NCBI E-utilities API workflows so searches are accurate and repeatable. ## Core Features & Use Cases - Query Construction: Build PubMed search strings using field tags ([ti], [ab], [mh], [pt], [dp]), Boolean operators, MeSH terms, and subheadings. - E-utilities API Workflow: Run esearch, esummary, efetch, and elink programmatically from Python with rate limiting, history server support, and API key handling. - Reproducible Search Logging: Record exact search strings, dates, filters, and result counts for systematic reviews. - Use Case: A researcher conducting a systematic review on type 2 diabetes treatments can build a MeSH-based query, retrieve PMIDs via esearch, fetch abstracts via efetch, and log every search pass for the review protocol. ## Quick Start Use the pubmed-database skill to find randomized controlled trials on hypertension published from 2024 to 2026 and return the PMIDs.

Frequently Asked Questions about pubmed-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I search PubMed with MeSH terms?▼

Use the [mh] field tag with the controlled vocabulary term, such as diabetes mellitus, type 2[mh]. Add subheadings before the tag, like diabetes mellitus, type 2/drug therapy[mh], and combine with free-text [tiab] synonyms for newer topics.

How to use NCBI E-utilities API from Python?▼

Call esearch.fcgi with db=pubmed, your query term, and retmode=json to get PMIDs, then use esummary or efetch for metadata and abstracts. Pass an email parameter, load the API key from environment variables, and add delays to respect rate limits.

What PubMed field tags filter by publication type and date?▼

Use [pt] for publication types like randomized controlled trial[pt] or systematic review[pt], and [dp] for dates such as 2020:2026[dp]. Other useful tags include [la] for language and free full text[sb] for availability.

Do I need an API key for NCBI E-utilities?▼

An API key is optional but recommended for production scripts since it raises rate limits. Store it in an environment variable like NCBI_API_KEY and never commit it to files or command history.

How do I handle large batches of PubMed results?▼

Use the NCBI history server by passing usehistory=y in esearch, then reference the returned WebEnv and query_key in subsequent efetch or esummary calls. This avoids passing very long PMID lists through URLs.

When should I use [majr] versus [mh] in PubMed queries?▼

Use [majr] only when the topic must be central to the paper, as it improves precision but can miss relevant work. For broader recall, use [mh] combined with title/abstract free-text terms.