scientific-db-pubmed-database
Helps your agent search for scientific articles in the biomedical field. It's useful for research needing specific medical information.
What it does
Searches the PubMed database for scientific articles, specializing in biomedicine. It helps find research, abstracts, and publication information for medical studies.
When to use it
- ✓Looking for studies on diseases and treatments.
- ✓Building complex searches with specific terms.
- ✓Finding articles published within certain dates or languages.
Example of what to ask your AI
“Find articles about the treatment of type 2 diabetes published in the last 5 years.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
You tell me what you're looking for, using medical terms and filters like dates or study types. I use these terms to search PubMed and bring back the most relevant results for you.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill scientific-db-pubmed-databaseOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/scientific-db-pubmed-database/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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:
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.
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
FAQ
What are MeSH terms?
MeSH are terms from a controlled vocabulary used to index articles in PubMed, helping to find research more accurately.
How do I search by author?
You can use the code `[au]` followed by the author's name, for example: `smith ja[au]`.
Can I search for articles in Portuguese?
You can specify the language using `AND english[la]` for English, or omit it to search in other available languages.
Details
Original description by the author
Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring. Use when a task needs biomedical literature from PubMed rather than general web search.
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #274
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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