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-multiomic-disease-characterizationgit 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-multiomic-disease-characterization)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-multiomic-disease-characterization"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-multiomic-disease-characterization/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-multiomic-disease-characterization"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-multiomic-disease-characterization.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 | $0.00073 | $0.02011 |
| Opus 5.5 | $0.00029 | $0.00804 |
| Sonnet 5.5 | $0.00015 | $0.00402 |
| Haiku 4.5 | $0.00007 | $0.00201 |
Grade A, and why
tooluniverse-multiomic-disease-characterization 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Omics Disease Characterization Pipeline
Characterize diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Disease disambiguation FIRST - Resolve all identifiers before omics analysis
- Layer-by-layer analysis - Systematically cover all omics layers
- Cross-layer integration - Identify genes/targets appearing in multiple layers
- Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)
- Tissue context - Emphasize disease-relevant tissues/organs
- Quantitative scoring - Multi-Omics Confidence Score (0-100)
- Druggable focus - Prioritize targets with therapeutic potential
- Biomarker identification - Highlight diagnostic/prognostic markers
- Mechanistic synthesis - Generate testable hypotheses
- Source references - Every statement must cite tool/database
- Completeness checklist - Mandatory section showing analysis coverage
- English-first queries - Always use English terms in tool calls. Respond in user's language
Multi-omics disease characterization asks: what molecular layers are dysregulated? Genomic mutations → transcriptomic changes → proteomic effects → metabolomic consequences. Concordance across layers strengthens the finding. Discordance reveals regulatory complexity.
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.
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 ships with it
6 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 · 154 lines · 73 tokens per session scan A f8860fc44134
tooluniverse-multiomic-disease-characterization is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 73 tokens to every session and 2,011 once invoked, about $0.0003 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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