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-snp-interpretationgit 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-snp-interpretation)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-gwas-snp-interpretation"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-gwas-snp-interpretation/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-snp-interpretation"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-gwas-snp-interpretation.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 | $0.00110 | $0.01918 |
| Opus 5 | $0.00055 | $0.00959 |
| Sonnet 5 | $0.00022 | $0.00384 |
| Haiku 4.5 | $0.00011 | $0.00192 |
Grade A, and why
tooluniverse-gwas-snp-interpretation 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GWAS SNP Interpretation Skill
Overview
Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context.
Use Cases:
- "Interpret rs7903146" (TCF7L2 diabetes variant)
- "What diseases is rs429358 associated with?" (APOE Alzheimer's variant)
- "Clinical significance of rs1801133" (MTHFR variant)
- "Is rs12913832 in any fine-mapped loci?" (Eye color variant)
What It Does
The skill provides a comprehensive interpretation of SNPs by:
- SNP Annotation: Retrieves basic variant information including genomic coordinates, alleles, functional consequence, and mapped genes
- Association Discovery: Finds all GWAS trait/disease associations with statistical significance
- Fine-Mapping Evidence: Identifies credible sets the variant belongs to (fine-mapped causal loci)
- Gene Mapping: Uses Locus-to-Gene (L2G) predictions to identify likely causal genes
- Clinical Summary: Aggregates evidence into actionable clinical significance
Workflow
User Input: rs7903146
↓
[1] SNP Lookup
→ Get location, consequence, MAF
→ gwas_get_snp_by_id
↓
[2] Association Search
→ Find all trait/disease associations
→ gwas_get_associations_for_snp
↓
[3] Fine-Mapping (Optional)
→ Get credible set membership
→ OpenTargets_get_variant_credible_sets
↓
[4] Gene Predictions
→ Extract L2G scores for causal genes
→ (embedded in credible sets)
↓
[5] Clinical Summary
→ Aggregate evidence
→ Identify key traits and genes
↓
Output: Comprehensive Interpretation Report
Data Sources
GWAS Catalog (EMBL-EBI)
- SNP annotations: Functional consequences, mapped genes, population frequencies
- Associations: P-values, effect sizes, study metadata
- Coverage: 350,000+ publications, 670,000+ associations
Open Targets Genetics
- Fine-mapping: Statistical credible sets from SuSiE, FINEMAP methods
- L2G predictions: Machine learning-based gene prioritization
- Colocalization: QTL evidence for causal genes
- Coverage: UK Biobank, FinnGen, and other large cohorts
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 · 224 lines · 110 tokens per session scan A 29e6795301d2
tooluniverse-gwas-snp-interpretation is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 110 tokens to every session and 1,918 once invoked, about $0.0006 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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