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 AndyZhuang/Opentest --skill tooluniverse-gwas-trait-to-genegit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/tooluniverse-gwas-trait-to-gene)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-gwas-trait-to-gene"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-gwas-trait-to-gene/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/andyzhuang/opentest/tooluniverse-gwas-trait-to-gene"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-gwas-trait-to-gene.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 | $0.00108 | $0.02338 |
| Opus 5 | $0.00054 | $0.01169 |
| Sonnet 5 | $0.00022 | $0.00468 |
| Haiku 4.5 | $0.00011 | $0.00234 |
Grade A, and why
tooluniverse-gwas-trait-to-gene 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GWAS Trait-to-Gene Discovery
Discover genes associated with diseases and traits using genome-wide association studies (GWAS)
Overview
This skill enables systematic discovery of genes linked to diseases/traits by analyzing GWAS data from two major resources:
- GWAS Catalog (EBI/NHGRI): Curated catalog of published GWAS with >500,000 associations
- Open Targets Genetics: Fine-mapped GWAS signals with locus-to-gene (L2G) predictions
Use Cases
Clinical Research
- "What genes are associated with type 2 diabetes?"
- "Find genetic risk factors for coronary artery disease"
- "Which genes contribute to Alzheimer's disease susceptibility?"
Drug Target Discovery
- Identify genes with strong genetic evidence for disease causation
- Prioritize targets based on L2G scores and replication across studies
- Find genes with genome-wide significant associations (p < 5e-8)
Functional Genomics
- Map disease-associated variants to candidate genes
- Analyze genetic architecture of complex traits
- Understand polygenic disease mechanisms
Workflow
1. Trait Search → Search GWAS Catalog by disease/trait name
↓
2. SNP Aggregation → Collect genome-wide significant SNPs (p < 5e-8)
↓
3. Gene Mapping → Extract mapped genes from associations
↓
4. Evidence Ranking → Score by p-value, replication, fine-mapping
↓
5. Annotation (Optional) → Add L2G predictions from Open Targets
Key Concepts
Genome-wide Significance
- Standard threshold: p < 5×10⁻⁸
- Accounts for multiple testing burden across ~1M common variants
- Higher confidence: p < 5×10⁻¹⁰ or replicated across studies
Gene Mapping Methods
- Positional: Nearest gene to lead SNP
- Fine-mapping: Statistical refinement to credible variants
- Locus-to-Gene (L2G): Integrative score combining multiple evidence types
Evidence Confidence Levels
- High: L2G score > 0.5 OR multiple studies with p < 5e-10
- Medium: 2+ studies with p < 5e-8
- Low: Single study or marginal significance
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.
- 9d ago First seen · 237 lines · 108 tokens per session scan A 901619703d6a
tooluniverse-gwas-trait-to-gene is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 108 tokens to every session and 2,338 once invoked, about $0.0005 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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