Source profileQuality 84/100

affaan-m/ECC/skills/scientific-db-pubmed-database/SKILL.md

pubmed-database

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

Source repository stars
234,327
Declared platforms
0
Static risk flags
1
Last source update
2026-07-27
Source checked
2026-07-28

Decision brief

What it does—and where it fits

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

Best for

  • 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.

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/affaan-m/ECC --skill "skills/scientific-db-pubmed-database"
Safe inspection promptEditorial

Inspect the Agent Skill "pubmed-database" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/skills/scientific-db-pubmed-database/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    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.
  2. 02

    Review Checklist

    Are field tags valid PubMed tags?

    Are field tags valid PubMed tags?Are MeSH terms paired with free-text synonyms for newer topics?Is the date range explicit and appropriate?
  3. 03

    When to Use

    Searching MEDLINE or life-sciences literature.

    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.
  4. 04

    Query Construction

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

    [ti]: title[ab]: abstract[tiab]: title or abstract
  5. 05

    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.

    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:Use [majr] only when the topic must be central to the paper. It can improve precision but may miss relevant work.

Permission review

Static risk signals and limitations

Network access

medium · line 104

The documentation includes network, browsing, or remote request actions.

BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

Network access

medium · line 120

The documentation includes network, browsing, or remote request actions.

response = requests.get(f"{BASE}/esearch.fcgi", params=params, timeout=30)

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score84/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars234,327SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
affaan-m/ECC
Skill path
skills/scientific-db-pubmed-database/SKILL.md
Commit
4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38
License
MIT
Collected
2026-07-28
Default branch
main
View the original 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

Alternatives

Compare before choosing