ToolUniverse is a collection of tools, interfaces, and supporting components for building AI systems that perform scientific work. It is for developers creating AI scientist agents that use APIs, databases, machine-learning tools, and domain-specific utilities. The catalogue includes skills, commands, an MCP server, an agent, and a hook for working with the ecosystem.
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 mims-harvard/ToolUniverse --skill tooluniverse-gwas-snp-interpretationgit clone --depth 1 https://github.com/mims-harvard/ToolUniverseWrote 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/mims-harvard/tooluniverse/tooluniverse-gwas-snp-interpretation)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-gwas-snp-interpretation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/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/mims-harvard/tooluniverse/tooluniverse-gwas-snp-interpretation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-gwas-snp-interpretation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00092 | $0.02135 |
| Opus 5.5 | $0.00037 | $0.00854 |
| Sonnet 5.5 | $0.00018 | $0.00427 |
| Haiku 4.5 | $0.00009 | $0.00214 |
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 1mo 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GWAS SNP Interpretation Skill
SNP interpretation: a GWAS hit is a REGION, not a single causal variant. The lead SNP may not be causal — it may be in LD with the causal variant. Always check LD structure and functional annotation before concluding a specific SNP is mechanistically responsible. Use LDlink_get_proxies(variant="rs...", population="EUR") to retrieve the high-R² LD proxies (needs a free LDLINK_TOKEN) — a proxy in a coding/regulatory region is a better mechanistic candidate than the lead SNP itself. Fine-mapping (SuSiE, FINEMAP credible sets) narrows the causal set but rarely identifies a single variant with certainty. L2G scores integrate eQTL, chromatin interaction, and distance data to predict the causal gene — a lead SNP mapping to gene A may actually regulate gene B 500 kb away via a distal enhancer.
LOOK UP DON'T GUESS: never assume a SNP's functional consequence, mapped gene, or population frequency — always call gwas_get_snp_by_id and OpenTargets_get_variant_info to retrieve current annotations.
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
What ships with it
4 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.
- 1mo ago First seen · 229 lines · 92 tokens per session scan A 77731a712ce3
tooluniverse-gwas-snp-interpretation is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 92 tokens to every session and 2,135 once invoked, about $0.0004 per session on Opus 5.5. 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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