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renepfrenepf/claude-skills/scientific-db-pubmed-database/SKILL.md

pubmed-database

Skill0 starsChanged 4 months agoMITView on GitHub

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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17 other projects use a version of this, 2 of them edited.

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Used with

Projects with it also use ena-database, gwas-database and 9 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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    Also in 7 projects that use this

  • garri333Skill

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    Also in 7 projects that use this

  • +9 more from garri333

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 17 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  • could be automatic: a setting could enforce it
  1. Distinctive:

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

    line 10
  2. Distinctive:

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

    line 23
  3. Distinctive:

    Prefer MeSH when the concept has a stable controlled-vocabulary term.

    line 55
  4. Very specific:

    Use [majr] only when the topic must be central to the paper.

    line 65
  5. Distinctive:

    Use an email and API key for production scripts.

    line 103

“Common” is measured: kept by at least 6 in 10 of the 17 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.

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:

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:

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:

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

Availability filters:

free full text[sb]
hasabstract[text]

E-utilities Workflow

NCBI E-utilities supports repeatable API workflows:

  1. esearch.fcgi: search and return PMIDs.
  2. esummary.fcgi: return lightweight article metadata.
  3. efetch.fcgi: fetch abstracts or full records in XML, MEDLINE, or text.
  4. 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.

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:

| 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

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