Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4Snpx agentmods add skills/pku-yuangroup/openai4s/bio-causal-genomics-heritability-partitioningWrote 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-heritability-partitioning)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-causal-genomics-heritability-partitioning"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-causal-genomics-heritability-partitioning/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-heritability-partitioning"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-causal-genomics-heritability-partitioning.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 | $0.00186 | $0.09780 |
| Opus 5 | $0.00093 | $0.04890 |
| Sonnet 5 | $0.00037 | $0.01956 |
| Haiku 4.5 | $0.00019 | $0.00978 |
Grade A, and why
bio-causal-genomics-heritability-partitioning scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://raw.githubusercontent.com/dougspeed/LDAK/main/ldak6.3.linux This is a copy
92% identical to bio-causal-genomics-heritability-partitioning — 14 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 — 460 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 fork; prefer abdenlab/ldsc-python3 v2.0.0 which retains the working --h2 / --rg / --h2-cts CLI -- belowlab/ldsc v3.0.1 explicitly broke that CLI per its README and is best run via Docker jtb114/ldsc:latest), LDAK 6.0+, BOLT-LMM 2.4.1+, GCTA 1.94+, HESS 0.5.4+, HDL 1.4.0+ (R; GitHub zhenin/HDL), Popcorn 1.0+ (Python; brielin/Popcorn), baselineLD_v2.2 annotations (alkesgroup.broadinstitute.org/LDSCORE).
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenpython3 -c 'import <module>; help(<module>)' - R:
packageVersion('<pkg>')then?function_name - CLI:
<tool> --versionthen<tool> --help
LDSC's official repository (bulik/ldsc) is Python 2.7 only and unmaintained since 2019; use the Python 3 community forks. If code throws ImportError, AttributeError, or a "category not found" error in the LD score file, introspect the installed binary and the actual LD-score column headers rather than retrying.
Heritability Partitioning
"Estimate SNP heritability and partition it across functional categories, cell types, and loci" -> Decompose h2_SNP from GWAS summary statistics (or individual-level genotypes) into contributions from baseline annotations (coding, conserved, regulatory), tissue-specific chromatin marks, and per-locus components, then reconcile model-dependent enrichment estimates across LDSC and LDAK. Tool choice is a decision about the regime (summary-stat vs individual-level; one-trait vs two-trait genetic correlation; total vs partitioned vs local) and the model assumption about how per-SNP heritability scales with LD, MAF, and functional annotation (GCTA model vs LDAK-Thin vs baseline-LD).
- CLI (h2 from sumstats, EUR):
ldsc.py --h2 trait.sumstats.gz --ref-ld-chr eur_w_ld_chr/ --w-ld-chr eur_w_ld_chr/ --out h2 - CLI (functional partitioning):
ldsc.py --h2 trait.sumstats.gz --ref-ld-chr baselineLD.,<annot>. --frqfile-chr 1000G.EUR.QC. --w-ld-chr weights. --overlap-annot --print-coefficients --out part - CLI (cell-type prioritization, Finucane 2018):
ldsc.py --h2-cts trait.sumstats.gz --ref-ld-chr-cts <cts_file>.ldcts --w-ld-chr weights. --out cts - CLI (cross-trait rg):
ldsc.py --rg t1.sumstats.gz,t2.sumstats.gz --ref-ld-chr eur_w_ld_chr/ --w-ld-chr eur_w_ld_chr/ --out rg - CLI (LDAK alternative):
ldak --sum-hers <out> --summary trait.txt --tagfile ldak.thin.<build>.tagging --check-sums NO - R (HDL):
HDL::HDL.rg(gwas1.df, gwas2.df, LD.path = 'UKB_array_SVD_eigen90_extraction') - CLI (local h2): HESS step1
hess.py --local-hsqg trait.sumstats.gz --chrom <chr> --bfile <ref> --partition <part>.bed --out hess_<chr>
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 · 460 lines · 186 tokens per session scan A ec495372e522
bio-causal-genomics-heritability-partitioning is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 186 tokens to every session and 9,780 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 92% identical to bio-causal-genomics-heritability-partitioning, differing in 14 lines, and is treated as a copy.
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