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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.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Orchestrate building a brand-new feature end to end — research, plan, TDD implementation, review, and gated commit — by delegating each phase to the matching ECC agent. Use when adding a capability that does not exist yet.
Orchestrate bootstrapping a working MVP from a design or spec document — ingest the doc, plan thin vertical slices, scaffold the first end-to-end slice, then TDD-implement, review, and gated commit. Use to turn an SDD/PRD into a running starting point.
Orchestrate altering an existing, working feature to new desired behavior — update its tests to the new spec, change the implementation to match, review, and gated commit. Use when behavior is not broken but should be different.
Orchestrate fixing a bug — reproduce it as a failing regression test, fix to green, review, and gated commit — by delegating each phase to the matching ECC agent. Use when existing behavior is broken or wrong.
Shared orchestration engine for the orch-* skill family. Defines the gated Research-Plan-TDD-Review-Commit pipeline, the size classifier, the agent map, and the two human gates that the orch-* operation skills delegate to. Not usually invoked directly.
| 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. |
| 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 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.
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?