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-fine-mappinggit 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-fine-mapping)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-causal-genomics-fine-mapping"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-causal-genomics-fine-mapping/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-fine-mapping"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-causal-genomics-fine-mapping.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 | $0.00161 | $0.09641 |
| Opus 5 | $0.00081 | $0.04820 |
| Sonnet 5 | $0.00032 | $0.01928 |
| Haiku 4.5 | $0.00016 | $0.00964 |
Grade A, and why
bio-causal-genomics-fine-mapping 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
100% identical to bio-causal-genomics-fine-mapping — 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 — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: susieR 0.12.27+, coloc 5.2.3+, FINEMAP 1.4.2+, PolyFun (head of omerwe/polyfun 2024), PAINTOR V3.0, SuSiEx (head of getian107/SuSiEx), DAP-G (head of xqwen/dap), pyfocus 0.8+, R 4.3+, PLINK 1.9 / 2.0.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('susieR')then?susie_rssto confirm argument names (e.g.,prior_weightsvsprior_variancesemantics) - CLI:
finemap --help,SuSiEx --help,PAINTOR --help,dap-g --helpto confirm flags - Python:
polyfun.py --help
If a call throws an error about an argument that no longer exists, introspect the installed function and adapt rather than retrying.
Fine-Mapping
"Narrow my GWAS locus to the variants likely to be causal" -> Fit a sparse Bayesian regression that propagates LD into posterior inclusion probabilities (PIPs) and credible sets, then validate that credible sets correspond to physically reasonable haplotypes given the LD reference.
- R (summary statistics + LD):
susieR::susie_rss(z, R, n, L=10)+estimate_s_rssLD diagnostic - R (individual-level genotypes):
susieR::susie(X, y, L=10) - CLI (shotgun stochastic search):
finemap --sss --in-files master.z --n-causal-snps 5 --prob-tol 0.001 - CLI (cross-ancestry joint):
SuSiEx --sst_file=eur.sst,eas.sst --n_gwas=N1,N2 --ref_file=eur.bim,eas.bim --ld_file=eur_ld,eas_ld --chr_col=1,1 --snp_col=2,2 --bp_col=3,3 --a1_col=4,4 --a2_col=5,5 --eff_col=6,6 --se_col=7,7 --pval_col=8,8 --chr=<chr> --bp=<start,end> --out_dir=<dir> --out_name=<name>(column-number flags and--ld_fileare required; populations are assigned by the ORDER of the comma-separated--sst_file/--n_gwas/--ref_file/--ld_filelists, not a--popflag; seeSuSiEx --help) - Python (functional priors):
polyfun.py --compute-h2-L2-> per-SNP priors -> susie_rss withprior_weights= - Python (TWAS fine-mapping):
focus finemapon gene-level Z-scores
What ships with it
6 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 · 444 lines · 161 tokens per session scan A 1551a62969ba
bio-causal-genomics-fine-mapping is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 161 tokens to every session and 9,641 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bio-causal-genomics-fine-mapping, differing in 12 lines, and is treated as a copy.
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