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-drug-discoverygit 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-drug-discovery)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-gwas-drug-discovery"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-gwas-drug-discovery/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-drug-discovery"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-gwas-drug-discovery.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 | $0.00079 | $0.04762 |
| Opus 5 | $0.00039 | $0.02381 |
| Sonnet 5 | $0.00016 | $0.00952 |
| Haiku 4.5 | $0.00008 | $0.00476 |
Grade A, and why
tooluniverse-gwas-drug-discovery 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 8d 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 — 577 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GWAS-to-Drug Target Discovery
Transform genome-wide association studies (GWAS) into actionable drug targets and repurposing opportunities.
Overview
This skill bridges genetic discoveries from GWAS with drug development by:
- Identifying genetic risk factors - Finding genes associated with diseases
- Assessing druggability - Evaluating which genes can be targeted by drugs
- Prioritizing targets - Ranking candidates by genetic evidence strength
- Finding existing drugs - Discovering approved/investigational compounds
- Identifying repurposing opportunities - Matching drugs to new indications
Why This Matters
From Genetics to Therapeutics: GWAS has identified thousands of disease-associated variants, but most haven't been translated into therapies. This skill accelerates that translation.
Success Stories:
- PCSK9 (cholesterol) → Alirocumab, Evolocumab (approved 2015)
- IL-6R (rheumatoid arthritis) → Tocilizumab (approved 2010)
- CTLA4 (autoimmunity) → Abatacept (approved 2005)
- CFTR (cystic fibrosis) → Ivacaftor (approved 2012)
Genetic Evidence Doubles Success Rate: Targets with genetic support have 2x higher probability of clinical approval (Nelson et al., Nature Genetics 2015).
Core Concepts
1. GWAS Evidence Strength
Not all genetic associations are equal. Consider:
- P-value - Statistical significance (genome-wide: p < 5×10⁻⁸)
- Effect size (beta/OR) - Magnitude of genetic effect
- Replication - Confirmed in multiple studies
- Sample size - Larger studies = more reliable
- Population diversity - Validated across ancestries
2. Druggability Criteria
A good drug target must be:
- Accessible - Protein location allows drug binding (extracellular > intracellular)
- Modality match - Target class fits drug type (GPCR → small molecule, receptor → antibody)
- Tractable - Binding pocket suitable for drug design
- Safe - Minimal off-target effects, not essential in all tissues
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.
- 8d ago First seen · 577 lines · 79 tokens per session scan A b95408fb7eb1
tooluniverse-gwas-drug-discovery is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 79 tokens to every session and 4,762 once invoked, about $0.0004 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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