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Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/phoroth/AGENTIC --skill pubmed-database命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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| name | pubmed-database |
| description | Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring. |
| metadata | {"origin":"community"} |
Use this skill when a task needs biomedical literature from PubMed rather than general web search.
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]: languageExamples:
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]
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.
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]
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 enprojectnment 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.enprojectn.get("NCBI_EMAIL", ""),
}
api_key = os.enprojectn.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.
For each search pass, record:
Example:
| Database | Date searched | Query | Filters | Results |
| --- | --- | --- | --- | ---: |
| PubMed | 2026-05-11 | `sickle cell disease[mh] AND CRISPR[tiab]` | 2020:2026[dp], English | 42 |
raise_for_status() or otherwise handle non-200
responses before parsing?