Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PKU-YuanGroup/OpenAI4S --skill bio-database-access-entrez-searchgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-entrez-search)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-entrez-search"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-entrez-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-entrez-search"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-entrez-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00100 | $0.04385 |
| Opus 5 | $0.00050 | $0.02193 |
| Sonnet 5 | $0.00020 | $0.00877 |
| Haiku 4.5 | $0.00010 | $0.00439 |
Grade A, and why
bio-entrez-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
98% identical to bio-entrez-search — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: BioPython 1.83+, Entrez Direct 21.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show biopythonthenhelp(Bio.Entrez.esearch)to check signatures - CLI:
esearch -versionthenesearch -helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Entrez Search
"Find NCBI records matching a query" -> ESearch returns matching record UIDs (not full records) from one NCBI database; EGQuery returns counts across all databases; EInfo describes a database's searchable fields and update timestamp.
The single most important fact: ESearch returns UIDs (PMIDs, GI numbers, gene IDs, etc.), not records. To get content the agent must call EFetch or ESummary. Forgetting this is the most common Entrez mistake.
- Python:
Entrez.esearch(db=..., term=...)(BioPython) - CLI:
esearch -db pubmed -query 'CRISPR[Title]'(Entrez Direct, NBK179288) - R:
entrez_search(db=..., term=...)(rentrez)
Required Setup
from Bio import Entrez
import time
Entrez.email = '[email protected]' # NCBI requires; sets User-Agent
Entrez.api_key = 'YOUR_KEY' # 3 -> 10 req/sec; get at ncbi.nlm.nih.gov/account/settings/
Entrez.tool = 'project-name' # appears in NCBI usage logs; helps if rate-throttled
What ESearch actually does
ESearch sends the query string through the Entrez Query Translator (EQT), which rewrites unqualified terms into the canonical term[field] form, then runs the rewritten query against the per-database index. The result is a list of UIDs plus a QueryTranslation string showing exactly what was searched. Reproducible work always inspects QueryTranslation and builds queries that are translation-stable from the start.
handle = Entrez.esearch(db='nucleotide', term='human BRCA1')
record = Entrez.read(handle)
handle.close()
print(record['QueryTranslation'])
# '("homo sapiens"[Organism] OR human[All Fields]) AND (BRCA1[Gene Name] OR BRCA1[All Fields])'
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 309 lines · 100 tokens per session scan A adaf66eb78b7
bio-entrez-search is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 100 tokens to every session and 4,385 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to bio-entrez-search, differing in 12 lines, and is treated as a copy.
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