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-causal-genomics-genomic-semgit 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-causal-genomics-genomic-sem)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-causal-genomics-genomic-sem"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-causal-genomics-genomic-sem/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-causal-genomics-genomic-sem"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-causal-genomics-genomic-sem.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 | $0.00162 | $0.08747 |
| Opus 5 | $0.00081 | $0.04373 |
| Sonnet 5 | $0.00032 | $0.01749 |
| Haiku 4.5 | $0.00016 | $0.00875 |
Grade A, and why
bio-causal-genomics-genomic-sem 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 13d 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-causal-genomics-genomic-sem — 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 — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: GenomicSEM 0.0.5+ (GitHub GenomicSEM/GenomicSEM), lavaan 0.6-17+, LDSC v1.0.1+ (Python 3; prefer abdenlab/ldsc-python3 v2.0.0 -- belowlab/ldsc v3.0.1 README states the CLI is broken; Docker jtb114/ldsc:latest is the belowlab fallback), baselineLD_v2.2 annotations (alkesgroup.broadinstitute.org/LDSCORE), MTAG 1.0.8+ (Python; JonJala/mtag), R 4.4+.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('GenomicSEM')then?ldsc,?commonfactor,?usermodel,?commonfactorGWAS - Python (LDSC, MTAG):
<tool>.py -hand inspect the source underldsc/ormtag/
GenomicSEM is GitHub-only (never on CRAN). If ldsc() or usermodel() throws an error about lavaan syntax or non-positive-definite covariance, introspect the installed API (getMethod('ldsc')) and adapt rather than retrying.
Genomic SEM
"Model the latent genetic architecture across several correlated GWAS" -> Treat each GWAS as a measured indicator of one or more latent genetic factors and fit a structural equation model to the LDSC-derived genetic covariance matrix S and its sampling covariance V (Grotzinger 2019 Nat Hum Behav 3:513). The framework extends naturally to a multivariate GWAS in which a SNP is regressed on a latent factor (common-factor GWAS), with Q_SNP testing whether the SNP effect is homogeneous across factor loadings. Sample overlap between input GWAS is absorbed by the off-diagonals of V; ignoring V inflates Type-I.
- R:
GenomicSEM::ldsc()produces the (S, V) covariance pair from munged sumstats - R:
GenomicSEM::commonfactor()fits a single-factor CFA across all traits in S - R:
GenomicSEM::usermodel()fits an arbitrary lavaan-syntax model - R:
GenomicSEM::commonfactorGWAS()runs SNP -> factor multivariate GWAS with Q_SNP - R:
GenomicSEM::userGWAS()runs arbitrary multivariate SNP regression with per-path Q_SNP - Python (alternative):
mtag.py --sumstats t1,t2,t3 --out mtag_out(multi-trait power boost on individual traits)
What ships with it
3 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.
- 13d ago First seen · 500 lines · 162 tokens per session scan A 05bad8d5580e
bio-causal-genomics-genomic-sem is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 8,747 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-causal-genomics-genomic-sem, differing in 12 lines, and is treated as a copy.
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