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-rare-disease-diagnosisgit 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-rare-disease-diagnosis)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-rare-disease-diagnosis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-rare-disease-diagnosis/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-rare-disease-diagnosis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-rare-disease-diagnosis.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 | $0.00072 | $0.02210 |
| Opus 5.5 | $0.00029 | $0.00884 |
| Sonnet 5.5 | $0.00014 | $0.00442 |
| Haiku 4.5 | $0.00007 | $0.00221 |
Grade A, and why
tooluniverse-rare-disease-diagnosis 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rare Disease Diagnosis Advisor
Systematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.
KEY PRINCIPLES:
- Report-first - Create report file FIRST, update progressively
- Phenotype-driven - Convert symptoms to HPO terms before searching
- Multi-database triangulation - Cross-reference Orphanet, OMIM, OpenTargets
- Evidence grading - Grade diagnoses by supporting evidence strength
- English-first queries - Always use English terms in tool calls
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
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.
Clinical Reasoning Framework (BEFORE Tools)
Apply these strategies to form a 3-5 candidate differential, then use tools to confirm/refute:
- Multi-system involvement - Symptoms spanning 2+ organ systems = strongest rare disease signal. Ask: what single pathway explains ALL features?
- Regression question - Losing abilities vs never acquired? Regression = neurodegenerative/metabolic storage. Stable = developmental/structural.
- Trigger question - Episodic/triggered (fasting, illness, exercise) = metabolic disorder (often treatable). Constitutive = structural/degenerative.
- Rarest feature first - Build differential from most specific finding, not most prominent. Check remaining features for consistency.
- Treatable-first - Move treatable conditions to top for urgent workup (enzyme replacement, dietary, chelation, vitamin-responsive).
- Occupational/environmental exposure - Latency up to 50 years. Asbestos/silica/heavy metals/solvents/farming. Always ask about PAST jobs.
- Autoimmune differential - Which joints? Symmetric? Extra-articular? Serologic pattern? Organ under attack?
- Rare syndrome signals - Named triads, common diagnoses failing to explain ALL findings, failed standard treatment, unusual lab findings.
- Tools verify, not generate - Form hypothesis first, then use databases to confirm.
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.
- 10d ago Changed · +7 lines eef1c9e36434
- 1mo ago First seen · 109 lines · 72 tokens per session scan A 0231e5e6f054
tooluniverse-rare-disease-diagnosis is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 72 tokens to every session and 2,210 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.
Other skills, from other repositories
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Draft a LaTeX research paper from all previous stage outputs.
construct-toy-examples
Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal. Use when you are stuck in reasoning and need simpler examples to regain traction, or when you want to see where the assumptions take effect and gain intuition.
obtain-immediate-conclusions
Derive immediate mathematical consequences from a theorem statement or subgoal. Use when starting a new problem, branch, or subgoal, or when cheap progress or a cleaner reformulation is needed before deeper proof search.
clinical-research-analysis-framework
Guided workflow for statistical and ML analysis of clinical data. Use when planning or executing research analyses on MIMIC, eICU, or similar EHR data. Ensures methodological rigor through structured consultation, assumption checking, and stepwise execution with audit trails.
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.