pubmed-database

Original：🇺🇸 English

Translated

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

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Sourceaffaan-m/everything-claude-code

Added on2026-05-12

NPX Install

npx skill4agent add affaan-m/everything-claude-code pubmed-database

SKILL.md Content

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PubMed Database

Use this skill when a task needs biomedical literature from PubMed rather than general web search.

When to Use

Searching MEDLINE or life-sciences literature.
Building PubMed queries with MeSH terms, field tags, dates, or article types.
Looking up PMIDs, abstracts, publication metadata, or related citations.
Running systematic-review search passes that need repeatable search strings.
Using NCBI E-utilities directly from Python, shell, or another HTTP client.

Query Construction

Start with the research question, split it into concepts, then combine concepts with Boolean operators.

text

concept_1 AND concept_2 AND filter
synonym_a OR synonym_b
NOT exclusion_term

Useful PubMed field tags:

```
[ti]
```
: title
```
[ab]
```
: abstract
```
[tiab]
```
: title or abstract
```
[au]
```
: author
```
[ta]
```
: journal title abbreviation
```
[mh]
```
: MeSH term
```
[majr]
```
: major MeSH topic
```
[pt]
```
: publication type
```
[dp]
```
: date of publication
```
[la]
```
: language

Examples:

text

diabetes mellitus[mh] AND treatment[tiab] AND systematic review[pt] AND 2023:2026[dp]
(metformin[nm] OR insulin[nm]) AND diabetes mellitus, type 2[mh] AND randomized controlled trial[pt]
smith ja[au] AND cancer[tiab] AND 2026[dp] AND english[la]

MeSH and Subheadings

Prefer MeSH when the concept has a stable controlled-vocabulary term. Combine MeSH with title/abstract terms when the topic is new or terminology varies.

Correct subheading syntax puts the subheading before the field tag:

text

diabetes mellitus, type 2/drug therapy[mh]
cardiovascular diseases/prevention & control[mh]

Use

[majr]

only when the topic must be central to the paper. It can improve precision but may miss relevant work.

Filters

Publication types:

```
clinical trial[pt]
```
```
meta-analysis[pt]
```
```
randomized controlled trial[pt]
```
```
review[pt]
```
```
systematic review[pt]
```
```
guideline[pt]
```

Date filters:

text

2026[dp]
2020:2026[dp]
2026/03/15[dp]

Availability filters:

text

free full text[sb]
hasabstract[text]

E-utilities Workflow

NCBI E-utilities supports repeatable API workflows:

```
esearch.fcgi
```
: search and return PMIDs.
```
esummary.fcgi
```
: return lightweight article metadata.
```
efetch.fcgi
```
: fetch abstracts or full records in XML, MEDLINE, or text.
```
elink.fcgi
```
: find related articles and linked resources.

Use an email and API key for production scripts. Store API keys in environment variables, never in committed files or command history.

python

import os
import time
import requests

BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"


def esearch(query: str, retmax: int = 20) -> list[str]:
    params = {
        "db": "pubmed",
        "term": query,
        "retmode": "json",
        "retmax": retmax,
        "tool": "ecc-pubmed-search",
        "email": os.environ.get("NCBI_EMAIL", ""),
    }
    api_key = os.environ.get("NCBI_API_KEY")
    if api_key:
        params["api_key"] = api_key

    response = requests.get(f"{BASE}/esearch.fcgi", params=params, timeout=30)
    response.raise_for_status()
    time.sleep(0.35)
    return response.json()["esearchresult"]["idlist"]


pmids = esearch("hypertension[mh] AND randomized controlled trial[pt] AND 2024:2026[dp]")
print(pmids)

For batches, prefer NCBI history server parameters (

usehistory=y

,

WebEnv

,

query_key

) instead of passing very long PMID lists through URLs.

Output Discipline

For each search pass, record:

exact search string
database searched
date searched
filters used
result count
export format
any manual exclusions

Example:

markdown

| Database | Date searched | Query | Filters | Results |
| --- | --- | --- | --- | ---: |
| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |

Review Checklist

Are field tags valid PubMed tags?
Are MeSH terms paired with free-text synonyms for newer topics?
Is the date range explicit and appropriate?
Does the search log include enough detail to reproduce the query?
Are API keys loaded from the environment?
Does HTTP code call
```
raise_for_status()
```
or otherwise handle non-200 responses before parsing?
Are rate limits respected?

References

PubMed help
NCBI E-utilities documentation
NCBI API key guidance
NCBI support: eutilities@ncbi.nlm.nih.gov

pubmed-database

NPX Install

Tags

SKILL.md Content

PubMed Database

When to Use

Query Construction

MeSH and Subheadings

Filters

E-utilities Workflow

Output Discipline

Review Checklist

References