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-clinical-databases-acmg-classificationgit 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-clinical-databases-acmg-classification)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clinical-databases-acmg-classification"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-databases-acmg-classification/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-clinical-databases-acmg-classification"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-databases-acmg-classification.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 | $0.00167 | $0.08064 |
| Opus 5 | $0.00084 | $0.04032 |
| Sonnet 5 | $0.00033 | $0.01613 |
| Haiku 4.5 | $0.00017 | $0.00806 |
Grade A, and why
bio-clinical-databases-acmg-classification 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.
r = requests.get(f'https://api.genebe.net/cloud/api-public/v1/variant', This is a copy
97% identical to bio-clinical-databases-acmg-classification — 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 — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+, AutoPVS1 (Xiang 2020), InterVar 2.2+, GeneBe 1.0+ (Stawiński 2024 Clin Genet). ACMG/AMP Bayesian point system is Tavtigian 2018 Genet Med / 2020 Hum Mutat. Pejaver 2022 AJHG PP3/BP4 calibrated thresholds. ClinGen Splicing Subgroup 2023 (Walker AJHG). v3.2 ACMG SF list (Miller 2023). The ACMG 2.0 framework is in development as of May 2026; not yet published.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. VCEP-specific CSpec rules override default ACMG application; the authoritative directory is https://cspec.genome.network/cspec/ui/svi/all.
ACMG/AMP Variant Classification Framework
'Classify this variant per ACMG/AMP' -> Apply 28-criterion framework using Tavtigian point system; gate on ClinGen SVI specifications and VCEP-specific overrides; assign P / LP / VUS / LB / B classification with evidence trail.
- Python (automated): GeneBe API
https://api.genebe.net/cloud/api-public/v1/variant - Python (rule-based): InterVar ->
python InterVar.py -i input.vcf -b hg38 --table_annovar table_annovar.pl - Web tools: VarSome (commercial), Franklin/Genoox (commercial), ClinGen VCI (gold standard for SVI)
- Citation: Richards 2015 Genet Med 17:405 (original framework); Tavtigian 2020 Hum Mutat 41:1734 (point system)
The Tavtigian Bayesian Point System: The Engine Inside All Modern Classifiers
Richards 2015 specified 28 criteria with strength labels (Supporting / Moderate / Strong / Very Strong); combination rules produced P / LP / VUS / LB / B. Tavtigian 2018/2020 demonstrated this framework is mathematically a Bayesian classifier and proposed the naturally-scaled point system that every modern automated classifier implements:
What ships with it
2 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 · 476 lines · 167 tokens per session scan A 1adbdb00bdde
bio-clinical-databases-acmg-classification is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed today), licensed MIT. It adds 167 tokens to every session and 8,064 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 97% identical to bio-clinical-databases-acmg-classification, differing in 12 lines, and is treated as a copy.
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