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 agentmods add commands/mims-harvard/tooluniverse/cross-validategit 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/commands/mims-harvard/tooluniverse/cross-validate)<a href="https://agentmods.dev/commands/mims-harvard/tooluniverse/cross-validate"><img src="https://agentmods.dev/badge/commands/mims-harvard/tooluniverse/cross-validate.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.01209 |
| Opus 5 | $0.00030 | $0.00605 |
| Sonnet 5 | $0.00012 | $0.00242 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
cross-validate 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 5d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-validate this claim against independent databases: $ARGUMENTS
A single source for a high-stakes fact is fragile. Force 3+ sources from DIFFERENT databases (not 3 tools that all wrap the same upstream API).
Process
1. Decompose the claim into testable assertions
A claim like "BRAF V600E is FDA-approved indication for vemurafenib in melanoma" has three independent assertions:
- (a) BRAF V600E exists as an annotated cancer variant
- (b) Vemurafenib is FDA-approved
- (c) The approved indication includes BRAF-V600E-positive melanoma
Each assertion can be confirmed/refuted separately. Write them out before calling tools — this is the anchor for what you'll verify.
For a numeric claim ("X% of breast cancers carry TP53 mutations"), the assertion is the value itself; cross-validate by getting the same number from independent cohorts.
2. Pick 3 independent databases per assertion
"Independent" means different upstream maintainers, not different tools wrapping the same API. Examples:
| Domain | Independent sources |
|---|---|
| Drug approvals | OpenFDA, ChEMBL, DrugBank — each maintains its own approval table |
| Variant pathogenicity | ClinVar (NIH), CIViC (academic crowd-sourced), OncoKB (MSKCC), gnomAD frequency (Broad) |
| Gene-disease association | OpenTargets (composite), DisGeNET, OMIM, GenCC |
| Mutation frequency in cancer | TCGA (GDC), COSMIC, IntOGen — each samples different cohorts |
| Clinical trial existence | ClinicalTrials.gov, EU CTR, ChiCTR |
| Protein function | UniProt, Reactome, KEGG |
| Pharmacology | DrugBank, ChEMBL, PharmGKB, FDA Label |
Pick 3. If the claim's domain only has 2 truly-independent sources, note that in the report — don't fabricate a third.
3. Run each source independently
Use tu run <tool> per source. Don't shortcut to a compound tool that aggregates
multiple sources — that defeats the cross-validation point. Track each source's
verdict separately:
Source 1 (ClinVar): CONFIRMS — V600E listed as Pathogenic, condition: Melanoma
Source 2 (CIViC): CONFIRMS — V600E linked to vemurafenib sensitivity in melanoma
Source 3 (OncoKB): CONFIRMS — V600 variant Level 1 evidence for vemurafenib in melanoma
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.
- 5d ago First seen · 112 lines · 59 tokens per session scan A a12ee3b9772a
cross-validate is a command published in the GitHub repository mims-harvard/ToolUniverse (1,672 stars, last pushed today), licensed Apache-2.0. It adds 59 tokens to every session and 1,209 once invoked, about $0.0003 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 2026-08-30.
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