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-geo-datagit 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-geo-data)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-database-access-geo-data"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-geo-data/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-geo-data"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-database-access-geo-data.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 | $0.00141 | $0.04776 |
| Opus 5 | $0.00071 | $0.02388 |
| Sonnet 5 | $0.00028 | $0.00955 |
| Haiku 4.5 | $0.00014 | $0.00478 |
Grade A, and why
bio-geo-data scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- CLI: `wget` from `ftp.ncbi.nlm.nih.gov/geo/series/...` This is a copy
98% identical to bio-geo-data — 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 — 381 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+, GEOparse 2.0+, R Bioconductor GEOquery 2.70+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show biopython geoparsethen introspect signatures - R:
packageVersion('GEOquery')
If the GSE structure doesn't match expectations (missing fields, malformed series matrix), re-fetch from FTP directly and inspect the SOFT or MINiML file as source of truth.
GEO Data
"Pull expression data from GEO accession GSE..." -> GEO stores Series (GSE), Samples (GSM), Platforms (GPL), and curated DataSets (GDS, frozen 2018). The single most consequential decision is processed (series matrix) vs raw (supplementary files / linked SRA) — the answer turns on how much trust the submitter's normalization deserves.
The single most-missed gotcha: SuperSeries. A GSE may be a meta-container (!Series_relation = SuperSeries of: GSExxxxx) holding multiple sub-studies on different platforms. Naively pulling samples from a SuperSeries gives mixed Affymetrix + Illumina + RNA-seq, mis-batched.
- Python:
Entrez.esearch(db='gds'), GEOparse for full series download - R:
GEOquery::getGEO()(Bioconductor; more mature than GEOparse) - CLI:
wgetfromftp.ncbi.nlm.nih.gov/geo/series/...
Required Setup
pip install biopython GEOparse pandas
# OR for R-side:
# R: BiocManager::install('GEOquery')
from Bio import Entrez
Entrez.email = '[email protected]'
Entrez.api_key = 'optional'
GEO record taxonomy
| Prefix | Type | Granularity | What's in it |
|---|---|---|---|
| GSE | Series | One study | Title, summary, design, links to GSMs, supplementary files |
| GSM | Sample | One biological/technical sample | Submitter metadata, per-sample processed data, link to raw SRA |
| GPL | Platform | One array / sequencer | Probe annotations or sequencer model |
| GDS | DataSet | Curated, normalized subset of one GSE | Re-normalized expression matrix (frozen 2018; new GDS no longer created) |
| GSEXXX SuperSeries | Series meta-container | Wraps multiple SubSeries | !Series_relation = SuperSeries of: ... |
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.
- 8d ago First seen · 381 lines · 141 tokens per session scan A 8cf6edf58ee4
bio-geo-data is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 141 tokens to every session and 4,776 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 98% identical to bio-geo-data, differing in 12 lines, and is treated as a copy.
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