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/genetic-correlationnpx skills add GPTomics/bioSkills --skill genetic-correlationgit 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/genetic-correlation)<a href="https://agentmods.dev/skills/gptomics/bioskills/genetic-correlation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/genetic-correlation.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 | $0.00163 | $0.09434 |
| Opus 5 | $0.00081 | $0.04717 |
| Sonnet 5 | $0.00033 | $0.01887 |
| Haiku 4.5 | $0.00016 | $0.00943 |
Grade A, and why
bio-causal-genomics-genetic-correlation 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-genetic-correlation — 98% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: LDSC v1.0.1+ (Python 3; prefer abdenlab/ldsc-python3 v2.0.0 -- belowlab/ldsc v3.0.1 README states the --h2 / --rg / --h2-cts CLI is broken; use Docker jtb114/ldsc:latest for the belowlab fallback; original bulik/ldsc is Python 2.7 unmaintained since 2019), HDL 1.4.0+ (R; GitHub zhenin/HDL), LAVA 0.1.0+ (R; GitHub josefin-werme/LAVA), HESS 0.5.4+ (Python; huwenboshi/hess), Popcorn 1.0+ (Python; brielin/Popcorn), GCTA 1.94+ (GREML-bivariate), baselineLD_v2.2 / eur_w_ld_chr LD-score panels from alkesgroup.broadinstitute.org/LDSCORE, UKB-array SVD eigen reference for HDL.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenpython -c 'import <module>; help(<module>)' - R:
packageVersion('<pkg>')then?function_name - CLI:
<tool> --versionthen<tool> --help
If code throws an LD-score "category not found" error, an HDL reference-panel mismatch, or a LAVA locus-ID lookup failure, introspect the installed LD-score column headers and the supplied partitioning file rather than retrying with default flags.
Genetic Correlation
"Estimate the genetic correlation between two traits from GWAS summary statistics" -> Decompose the bivariate genetic architecture into a single global rg (cross-trait LDSC, HDL), per-locus local rg (LAVA, rho-HESS, HDL-L), or cross-population rg (Popcorn). Genetic correlation is the central cross-trait statistic in causal genomics: it quantifies shared etiology, motivates CHP-aware MR sensitivity when high, gates LCV's gcp partial-causation parameter, and feeds into multi-trait analysis frameworks (MTAG, GenomicSEM). Tool choice is a decision about the regime (sumstats vs individual-level; global vs local; same-ancestry vs trans-ancestry) and the sample-overlap structure between input GWAS.
- CLI (LDSC, robust to overlap):
ldsc.py --rg trait1.sumstats.gz,trait2.sumstats.gz --ref-ld-chr eur_w_ld_chr/ --w-ld-chr eur_w_ld_chr/ --out rg - R (HDL, lower variance, requires independent samples):
HDL.rg(gwas1.df, gwas2.df, LD.path = 'UKB_array_SVD_eigen90_extraction', N0 = 0) - R (LAVA, local rg per locus):
process.input() -> run.univ() -> run.bivar(input, locus_id)over ~2495 LDetect-derived loci - CLI (rho-HESS, locus-level):
hess.py --local-rhog t1.sumstats.gz t2.sumstats.gz --bfile <ref> --partition <part>.bed --chrom <chr> - CLI (Popcorn, trans-ancestry):
popcorn fit -v 1 --cfile cross_pop_scores.txt --sfile1 pop1.txt --sfile2 pop2.txt out
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.
- 6d ago First seen · 487 lines · 163 tokens per session scan A f4e38fbf5e9a
bio-causal-genomics-genetic-correlation is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 21d ago), licensed MIT. It adds 163 tokens to every session and 9,434 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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