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-fetchgit 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-fetch)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-entrez-fetch"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-entrez-fetch/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-fetch"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-entrez-fetch.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 | $0.04032 |
| Opus 5 | $0.00050 | $0.02016 |
| Sonnet 5 | $0.00020 | $0.00806 |
| Haiku 4.5 | $0.00010 | $0.00403 |
Grade A, and why
bio-entrez-fetch 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
97% identical to bio-entrez-fetch — 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 — 316 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.efetch)to check signatures - CLI:
efetch -versionthenefetch -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 Fetch
"Download a record by accession from NCBI" -> EFetch returns the full record content in a chosen format (FASTA, GenBank, XML, MEDLINE, etc.). ESummary returns a lightweight "docsum" object — much faster when only metadata is needed.
The agent's first decision is always: does this workflow need the full record, or just metadata? ESummary is 5-10x cheaper than EFetch for the equivalent record set. For "tell me the organism, length, and definition line for 10,000 accessions", ESummary wins by an order of magnitude.
- Python:
Entrez.efetch(db=..., id=..., rettype=..., retmode=...)(BioPython) - CLI:
efetch -db nucleotide -id NM_007294 -format gb(Entrez Direct, NBK179288) - R:
entrez_fetch(db=..., id=..., rettype=...)(rentrez)
Required Setup
from Bio import Entrez, SeqIO
Entrez.email = '[email protected]'
Entrez.api_key = 'optional_api_key' # raises rate to 10 req/sec
Decision matrix: rettype + retmode per database
The combinations are not orthogonal — each (db, rettype, retmode) triple is enabled or disabled by NCBI server-side. Wrong combinations return either silent empty responses or HTTP 400. The triples below are the safe, current set.
nucleotide / protein
| rettype | retmode | Returns | Use when |
|---|---|---|---|
fasta |
text |
FASTA | Just need sequence + defline |
gb (nuc) / gp (prot) |
text |
Full flat file | Need annotations, features, references |
gbwithparts |
text |
GB with CONTIG sequences inlined | Whole-genome shotgun assemblies; default gb returns CONTIG records requiring a chase to resolve |
fasta_cds_na |
text |
CDS-only nucleotide | Extract coding regions from annotated GB |
fasta_cds_aa |
text |
CDS-translated AA | Get translated proteins from GB record in one call |
xml (== gb XML) |
xml |
INSDSeq XML | Programmatic parsing; the schema is unversioned and shifts |
acc |
text |
Accession.version per line | Just resolve UID -> accession |
seqid |
text |
Internal seq-id | Rarely needed |
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 · 316 lines · 100 tokens per session scan A 75d9aa9ba532
bio-entrez-fetch 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 4,032 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-entrez-fetch, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.
scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…
cellxgene-census
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
glycobiology
Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.