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.
- Reads credentials
17 other projects use a version of this, 2 of them edited.
Adopted by
Most recently taken up by
- BrightGold70/skillstheir own version, first seen Nov 8, 2026
- 1stANUNNAKI/majlistheir own version, first seen Nov 8, 2026
- MarieLynneBlock/arcanum-artifextheir own version, first seen Nov 1, 2026
- davidanthonyn/claude-skillstheir own version, first seen Oct 20, 2026
- Osmosy/vector-genetheir own version, first seen Oct 13, 2026
- garri333/Skillstheir own version, first seen Oct 11, 2026
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.
garri333Skillena-database
Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.
Also in 7 projects that use this
garri333Skillgwas-database
Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.
Also in 7 projects that use this
- +9 more from garri333
Other versions17 projects use a version of this.
Word for word · 0
None found.
Edited · 2
- Fmarzochi/EGC★ 6199% same
- SamuelNDCE/claude-super-skill-library★ 199% same
Wrote their own version · 15
- google-deepmind/science-skills★ 3.4ksame name
- wu-yc/LabClaw★ 1.1ksame name
- LeonChaoX/qinyan-academic-skills★ 910same name
- jaechang-hits/SciAgent-Skills★ 386same name
- qinyan-ai/qinyan-academic-skills★ 80same name
- mikkelkrogsholm/skills★ 74same name
- MarieLynneBlock/arcanum-artifex★ 4same name
- 1stANUNNAKI/majlis★ 2same name
- BrightGold70/skills★ 1same name
- garri333/Skills★ 1same name
5 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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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
- Distinctive: line 10
Use this skill when a task needs biomedical literature from PubMed rather than general web search.
- Distinctive: line 23
Start with the research question, split it into concepts, then combine concepts with Boolean operators.
- Distinctive: line 55
Prefer MeSH when the concept has a stable controlled-vocabulary term.
- Very specific: line 65
Use [majr] only when the topic must be central to the paper.
- Distinctive: line 103
Use an email and API key for production scripts.
“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.
Files
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_termUseful 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
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