Scientific db pubmed database

Skill mturac/everything-openai-codex/skills/scientific-db-pubmed-database

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

Install

npx -y skills add mturac/everything-openai-codex --skill scientific-db-pubmed-database

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

4.6 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most databases sql skills give in ~1.2k tokens

Counted across 609 of the 712 authors here whose files we hold, read 2026-09-06

Index all foreign key columnsin 26 of 609
Use cursor pagination instead of offsetin 25 of 609, across 20 files
Use timestamptz for timestampsin 21 of 609
Specify columns instead of using select starin 20 of 609, across 10 files
Use parameterized queries for all database interactionsin 20 of 609, across 19 files
Use Enum for categorical datain 17 of 609, across 7 files
Order by frequently filtered columnsin 17 of 609, across 7 files
Batch data insertsin 17 of 609, across 7 files
Use expand-contract pattern for schema changesin 17 of 609
Use materialized views for real-time aggregationsin 16 of 609, across 6 files
Partition tables by timein 16 of 609, across 6 files
Use smallest appropriate data typesin 16 of 609, across 6 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.