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-clinical-data-integrationgit 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-clinical-data-integration)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-clinical-data-integration"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-clinical-data-integration/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-clinical-data-integration"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-clinical-data-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00078 | $0.03467 |
| Opus 5 | $0.00039 | $0.01733 |
| Sonnet 5 | $0.00016 | $0.00693 |
| Haiku 4.5 | $0.00008 | $0.00347 |
Grade A, and why
tooluniverse-clinical-data-integration 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 12d 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical Data Integration for Drug Safety
End-to-end drug safety review pipeline that integrates FDA label information, FAERS spontaneous reports, disproportionality signal detection, pharmacogenomic biomarkers, clinical trial data, and published literature. Designed for regulatory assessments, pharmacovigilance, and clinical decision support.
Guiding principles:
- Label is ground truth -- FDA-approved labeling is the authoritative starting point for known safety information
- Signals need context -- a FAERS signal without label or literature corroboration is hypothesis-generating, not confirmatory
- Disproportionality is not causation -- PRR/ROR measure reporting patterns, not causal relationships
- Pharmacogenomics narrows risk -- PGx biomarkers can identify which patients face elevated risk
- Progressive reporting -- create the report file early; update section by section
- English-first queries -- use English drug names in all tool calls; respond in the user's language
Clinical data integration starts with data harmonization. Different hospitals code the same diagnosis differently (ICD-10 vs SNOMED). Before merging datasets, verify the coding system. Missing data is informative — a missing lab value may mean the test wasn't ordered (patient was stable) not that the result was normal.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
Differentiation: This skill emphasizes regulatory-grade data integration across the full drug lifecycle. For focused FAERS signal detection with quantitative scoring, see tooluniverse-adverse-event-detection. For general pharmacovigilance workflows, see tooluniverse-pharmacovigilance.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
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.
- 12d ago First seen · 291 lines · 78 tokens per session scan A f884d55c4e4a
tooluniverse-clinical-data-integration is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 2d ago), licensed Apache-2.0. It adds 78 tokens to every session and 3,467 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 2026-08-30.
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