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 agentmods add skills/gptomics/bioskills/mediation-analysisnpx skills add GPTomics/bioSkills --skill mediation-analysisgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/mediation-analysis)<a href="https://agentmods.dev/skills/gptomics/bioskills/mediation-analysis"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/mediation-analysis.svg" alt="Measured on agentmods" 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 | $0.00116 | $0.08947 |
| Opus 5 | $0.00058 | $0.04473 |
| Sonnet 5 | $0.00023 | $0.01789 |
| Haiku 4.5 | $0.00012 | $0.00895 |
Grade A, and why
bio-causal-genomics-mediation-analysis 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-causal-genomics-mediation-analysis — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 469 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: R 4.3+, mediation 4.5.0+, CMAverse 0.1.0+ (GitHub BS1125/CMAverse), HIMA >= 2.3.0 (GitHub YinanZheng/HIMA; archived from CRAN 2026-07), bama 1.3+, causalweight 1.0.5+ (medDML), MVMR 0.4+, TwoSampleMR 0.6+, EValue 4.1+, gesttools 1.3+, ipw 1.0.11+.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters - HIMA must be pinned at
>= 2.3.0for the code patterns below; the formula interfacehima(formula, data.pheno, data.M, mediator.type, penalty, ...)was introduced in 2.3.0. On HIMA 2.2.x the classic engine is the top-levelhima(X, Y, M, COV.XM=, COV.MY=, Y.family=, penalty=)(positional order X, Y, M) with no formula interface; HIMA 2.2.x is NOT API-compatible with the examples here. - In 2.3+ the classic engine is
hima_classic(X, M, Y, COV.XM=, COV.MY=, Y.type=)(positional order X, M, Y; outcome type viaY.type, notY.family); use it only to reproduce the original 2016-2021 SIS+penalty pipelines.
If code throws an error, introspect the installed package (?hima, args(cmest)) and adapt the example to match the actual API rather than retrying.
Mediation Analysis
"Does expression of GENE_X mediate the SNP-to-disease effect?" -> Decompose the total effect of a treatment (genotype, exposure) on an outcome into direct and indirect paths through one or more mediators, with explicit handling of exposure-mediator interaction, sensitivity to unmeasured confounding, and high-dimensional mediator screening.
- R (single-mediator, observational, sequential-ignorability assumed):
mediation::mediate(med_model, out_model, treat='X', mediator='M', boot=TRUE, sims=5000) - R (4-way decomposition with exposure-mediator interaction):
CMAverse::cmest(...EMint=TRUE, estimation='paramfunc', inference='bootstrap', nboot=1000) - R (high-dimensional / EWAS mediators):
HIMA::hima(Y ~ X + covariates, data.pheno, data.M, mediator.type='gaussian', penalty='DBlasso')(modern v2.3+ formula interface) - R (MR-based mediation): two-step
TwoSampleMRwith independent instruments ORMVMR::ivw_mvmrfor joint direct effect - R (doubly-robust double-ML):
causalweight::medDML(y, d, m, x)
What ships with it
5 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.
- 6d ago First seen · 469 lines · 116 tokens per session scan A 7ed1477cd331
bio-causal-genomics-mediation-analysis is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 21d ago), licensed MIT. It adds 116 tokens to every session and 8,947 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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