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-mendelian-randomizationgit 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-mendelian-randomization)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-mendelian-randomization"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-mendelian-randomization/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-mendelian-randomization"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-mendelian-randomization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00244 | $0.02672 |
| Opus 5.5 | $0.00098 | $0.01069 |
| Sonnet 5.5 | $0.00049 | $0.00534 |
| Haiku 4.5 | $0.00024 | $0.00267 |
Grade A, and why
tooluniverse-mendelian-randomization 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 10d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mendelian Randomization (Causal Inference from Genetic Instruments)
MR estimates the CAUSAL effect of an exposure on an outcome using genetic variants as instrumental variables. Because alleles are randomized at conception, MR is largely robust to the confounding and reverse causation that bias observational associations. It is not a free lunch: the causal claim rests on three assumptions, and violating them (especially horizontal pleiotropy) silently biases the estimate.
LOOK UP, DON'T GUESS: never assert a causal MR estimate from memory. Genetic-instrument results are updated as new GWAS are published — always retrieve current evidence with EpiGraphDB_get_mendelian_randomization. Do not invent beta/p-values.
Correlation ≠ causation, and genetic correlation ≠ causation. A high genetic correlation (rg) means two traits share heritability — it does NOT establish a causal direction. Only MR (with valid instruments) speaks to causality. Report them as different kinds of evidence.
The three instrumental-variable assumptions
| Assumption | Statement | How it fails | Check |
|---|---|---|---|
| Relevance | Instrument is robustly associated with the exposure | Weak instruments (low F-stat) → bias toward the confounded observational estimate | MOE score; instruments selected at GWAS significance |
| Independence | Instrument shares no common cause with the outcome | Population stratification, assortative mating | Ancestry-matched GWAS; report population |
| Exclusion restriction | Instrument affects the outcome ONLY through the exposure | Horizontal pleiotropy — the variant influences the outcome via another path | MR-Egger intercept ≈ 0; agreement across methods |
If you cannot speak to these, your causal claim is provisional. Say so.
When to use
- "Does [exposure] causally affect [outcome/disease]?" — the core MR question.
- Triangulating an observational/epidemiological association ("BMI correlates with depression — is it causal?").
- Reverse-causation checks (bidirectional MR: does the outcome cause the exposure instead?).
- Prioritising drug targets / risk factors with genetic causal support.
- Distinguishing a causal driver from a shared-etiology bystander (MR vs genetic correlation).
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.
- 10d ago Changed · +1 lines 54982d3be8c3
- 1mo ago First seen · 121 lines · 244 tokens per session scan A 10684285d2b9
tooluniverse-mendelian-randomization is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 244 tokens to every session and 2,672 once invoked, about $0.0010 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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construct-toy-examples
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obtain-immediate-conclusions
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clinical-research-analysis-framework
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apache-iv-score
Calculate APACHE IV (Acute Physiology and Chronic Health Evaluation IV) score for ICU mortality prediction. Use for severity assessment, hospital mortality prediction, ICU benchmarking, or case-mix adjustment. eICU has pre-computed scores; MIMIC-IV requires custom implementation with diagnosis mapping challenges.