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/mendelian-randomizationnpx skills add GPTomics/bioSkills --skill mendelian-randomizationgit 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/mendelian-randomization)<a href="https://agentmods.dev/skills/gptomics/bioskills/mendelian-randomization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/mendelian-randomization.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 | $0.00154 | $0.09374 |
| Opus 5 | $0.00077 | $0.04687 |
| Sonnet 5 | $0.00031 | $0.01875 |
| Haiku 4.5 | $0.00015 | $0.00937 |
Grade A, and why
bio-causal-genomics-mendelian-randomization 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-mendelian-randomization — 100% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 413 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: TwoSampleMR 0.6.0+, MendelianRandomization 0.10+, MR-PRESSO 1.0+, cause 1.2+, MVMR 0.4+, ieugwasr 1.0+, MRlap 0.0.3.2+, coloc 5.2+, mrclust 0.1+, lhcMR 0.0.1+, R 4.4+. Both TwoSampleMR 0.6.0 and ieugwasr 1.0 are the JWT-transition versions; older versions still expect deprecated OAuth.
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters - CLI (plink, GCTA-GSMR):
<tool> --versionthen<tool> --help
If code throws an error referencing a function that has moved (e.g. ieugwasr::ld_clump vs TwoSampleMR::clump_data) or an OAuth token failure, introspect the installed API and adapt the example rather than retrying.
Mendelian Randomization
"Test whether trait X causally affects trait Y from GWAS summary statistics" -> Use genetic variants as instrumental variables (IVs) that satisfy three assumptions (relevance, independence, exclusion restriction) to estimate beta_causal = beta_outcome / beta_exposure under the IV framework (Davey Smith & Ebrahim 2003 IJE 32:1; Burgess & Thompson 2021 Chapman & Hall/CRC, 2nd ed.). Tool choice is a decision about the regime (one-sample vs two-sample, sparse vs polygenic, drug-target vs polygenic exposure) and the pleiotropy model (balanced, directional InSIDE, correlated horizontal). Wrong tool inflates Type-I error or attenuates true effects in a direction predictable from the bias structure.
- R:
TwoSampleMR::mr()orchestrates IVW + Egger + weighted median + weighted mode in one call - R:
MendelianRandomization::mr_ivw / mr_egger / mr_median / mr_mbe / mr_conmixper-method API (S4 objects; MR-RAPS is NOT in this package -- useTwoSampleMR::mr_raps()which wraps the GitHubmr.raps) - R:
MRPRESSO::mr_presso()global / outlier / distortion tests - R:
cause::cause()correlated horizontal pleiotropy mixture - R:
MVMR::strength_mvmr() + MVMR::ivw_mvmr()multivariable conditional-F + IVW
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 · 413 lines · 154 tokens per session scan A 57981de8cf02
bio-causal-genomics-mendelian-randomization is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 22d ago), licensed MIT. It adds 154 tokens to every session and 9,374 once invoked, about $0.0008 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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