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 GPTomics/bioSkills --skill polygenic-riskgit 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/polygenic-risk)<a href="https://agentmods.dev/skills/gptomics/bioskills/polygenic-risk"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/polygenic-risk/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/gptomics/bioskills/polygenic-risk"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/polygenic-risk.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 | $0.00137 | $0.08156 |
| Opus 5 | $0.00068 | $0.04078 |
| Sonnet 5 | $0.00027 | $0.01631 |
| Haiku 4.5 | $0.00014 | $0.00816 |
Grade A, and why
bio-clinical-databases-polygenic-risk 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 7d 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-clinical-databases-polygenic-risk — 100% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 454 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: bigsnpr 1.12+ (LDpred2; Privé 2020), PRSice-2 2.3.5+, PRS-CS 1.0.0+ (Ge 2019), gctb 2.5+ (SBayesR/SBayesS/SBayesRC; Zheng 2024), LDAK 6.0+ (MegaPRS; Zhang 2021), pgsc_calc 2.0+ (nf-core; Lambert 2024), Hail 0.2.130+, numpy 1.26+, pandas 2.2+. No general FDA PRS guidance document exists as of May 2026; the operative regulatory text is the August 2025 Federal Register notice on Cancer Predisposition Risk Assessment Systems (Class II device with special controls).
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_name - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The LDpred2-auto snp_ldpred2_auto() signature changed in bigsnpr 1.11+; pin allow_jump_sign = FALSE and shrink_corr = 0.95 explicitly.
Polygenic Risk Scores; Construction, Calibration, Reporting
'Compute a PRS for my cohort using these GWAS summary statistics' -> Match variants to target genotypes, choose method by data availability + trait architecture, derive LD-aware effect estimates, score, normalize by ancestry, transform to absolute risk.
- CLI (recommended one-stop):
pgsc_calc --target target.vcf --pgs_id PGS000001(nf-core, Lambert 2024) - R (SOTA single-ancestry):
bigsnpr::snp_ldpred2_auto()(Privé 2020) - CLI (multi-ancestry SOTA):
PRS-CSx,PROSPER,MUSSEL,BridgePRS,JointPRS - CLI (SBayesRC with functional annotations):
gctb --sbayes-rc --bfile target --gwas-summary sumstats.ma - CLI (legacy baseline):
PRSice_linux(clumping + thresholding; still cited for some clinical scores)
Method Landscape: 2026 Operational Ranking
| Method | Approach | Best for | Fails when |
|---|---|---|---|
| SBayesRC (Zheng 2024 Nat Genet) | Bayesian + 96 functional annotations | EUR; sparse traits | Sumstats LD-incoherent with reference; chain divergence (run --impute-summary first) |
| MegaPRS (Zhang 2021 Nat Commun) | BLD-LDAK heritability model | EUR; sparser traits | Lacks GCTA-model assumptions; legacy GCTA pipelines |
| LDpred2-auto (Privé 2020) | Bayesian + auto-tuning | Polygenic EUR | LD ref mismatch (s > 0.05); allow_jump_sign default True (must pin FALSE) |
| PRS-CS-auto (Ge 2019 Nat Commun) | Continuous-shrinkage prior | Polygenic EUR | Sparse-trait architecture; HapMap3-restricted variants only |
| lassosum2 (Privé 2022) | Penalized regression | EUR alternative | Highly polygenic (Bayesian methods better); requires tuning data |
| C+T (PRSice-2; Choi 2019) | Clumping + thresholding | Legacy clinical scores (PRS313) | Highly polygenic; Bayesian methods dominate |
| PROSPER (Zhang 2024 Nat Commun) | Ensemble penalized regression | Multi-ancestry, AFR + others | Single-ancestry; tuning set < 1000 |
| MUSSEL (Jin 2024 Cell Genomics) | Spike-slab + super-learner | Multi-ancestry; admixed AFR | Single-ancestry; lacks tuning data |
| JointPRS (Xu L et al 2025 Nat Commun 16:3841) | Data-adaptive Bayesian | Multi-ancestry; sumstats only | Single-ancestry; very small target |
| PRS-CSx (Ruan 2022 Nat Genet) | PRS-CS multi-ancestry extension | Multi-ancestry with EUR + non-EUR sumstats | Low causal-variant overlap across ancestries |
| BridgePRS (Hoggart 2024 Nat Genet) | Ridge-bridge sharing | Low-h^2 AFR / low causal overlap | Standard scenarios (PROSPER/MUSSEL win) |
| PolyPred / PolyPred+ (Weissbrod 2022) | BOLT-LMM + PolyFun-SuSIE | Multi-ancestry; biobank-scale | Small individual-level data; expensive |
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.
- 7d ago First seen · 454 lines · 137 tokens per session scan A 154ed21ff1cc
bio-clinical-databases-polygenic-risk is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 26d ago), licensed MIT. It adds 137 tokens to every session and 8,156 once invoked, about $0.0007 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 2026-09-03.
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