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 silverstein/claude-scientific-skills-desktop --skill gwas-databasegit clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktopWrote 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/silverstein/claude-scientific-skills-desktop/gwas-database)<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/gwas-database"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/gwas-database/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/silverstein/claude-scientific-skills-desktop/gwas-database"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/gwas-database.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 | $0.00050 | $0.04753 |
| Opus 5 | $0.00025 | $0.02377 |
| Sonnet 5 | $0.00010 | $0.00951 |
| Haiku 4.5 | $0.00005 | $0.00475 |
Grade A, and why
gwas-database 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 11d 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.
response = requests.get(url, headers={"Content-Type": "application/json"}) This is a copy
97% identical to gwas-database — 7 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 — 603 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GWAS Catalog Database
Overview
The GWAS Catalog is a comprehensive repository of published genome-wide association studies maintained by the National Human Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EBI). The catalog contains curated SNP-trait associations from thousands of GWAS publications, including genetic variants, associated traits and diseases, p-values, effect sizes, and full summary statistics for many studies.
When to Use This Skill
This skill should be used when queries involve:
- Genetic variant associations: Finding SNPs associated with diseases or traits
- SNP lookups: Retrieving information about specific genetic variants (rs IDs)
- Trait/disease searches: Discovering genetic associations for phenotypes
- Gene associations: Finding variants in or near specific genes
- GWAS summary statistics: Accessing complete genome-wide association data
- Study metadata: Retrieving publication and cohort information
- Population genetics: Exploring ancestry-specific associations
- Polygenic risk scores: Identifying variants for risk prediction models
- Functional genomics: Understanding variant effects and genomic context
- Systematic reviews: Comprehensive literature synthesis of genetic associations
Core Capabilities
1. Understanding GWAS Catalog Data Structure
The GWAS Catalog is organized around four core entities:
- Studies: GWAS publications with metadata (PMID, author, cohort details)
- Associations: SNP-trait associations with statistical evidence (p ≤ 5×10⁻⁸)
- Variants: Genetic markers (SNPs) with genomic coordinates and alleles
- Traits: Phenotypes and diseases (mapped to EFO ontology terms)
Key Identifiers:
- Study accessions:
GCSTIDs (e.g., GCST001234) - Variant IDs:
rsnumbers (e.g., rs7903146) orvariant_idformat - Trait IDs: EFO terms (e.g., EFO_0001360 for type 2 diabetes)
- Gene symbols: HGNC approved names (e.g., TCF7L2)
What ships with it
1 file 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.
- 11d ago First seen · 603 lines · 50 tokens per session scan A 0d914b28f570
gwas-database is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 4,753 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 97% identical to gwas-database, differing in 7 lines, and is treated as a copy.
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