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-model-organism-geneticsgit 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-model-organism-genetics)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-model-organism-genetics"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-model-organism-genetics/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-model-organism-genetics"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-model-organism-genetics.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 | $0.00156 | $0.08987 |
| Opus 5.5 | $0.00062 | $0.03595 |
| Sonnet 5.5 | $0.00031 | $0.01797 |
| Haiku 4.5 | $0.00016 | $0.00899 |
Grade A, and why
tooluniverse-model-organism-genetics 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
Model Organism Genetics Pipeline
Map human genes to model organism orthologs and retrieve phenotype, expression, and functional data across six species. Synthesize cross-species evidence to assess gene function conservation and identify the best animal models for studying human genes and diseases.
Not for: human variant interpretation (tooluniverse-variant-analysis), drug target validation (tooluniverse-drug-target-validation), human disease characterization (tooluniverse-multiomic-disease-characterization), pathogen outbreak/drug-repurposing intelligence once a VEuPathDB target gene is identified (tooluniverse-infectious-disease).
LOOK UP, DON'T GUESS: When asked about a species' taxonomy, ecology, or biology, search GBIF/NCBI Taxonomy first. For GBIF: use GBIF_search_species(query="species name"), then use the nubKey (not key) from the result to call GBIF_get_species(speciesKey=nubKey) for full taxonomy (kingdom, phylum, class, order, family). The nubKey is the GBIF backbone key; the key is dataset-specific and often lacks higher taxonomy.
Reasoning Principles
Ortholog Reasoning
Sequence conservation across species implies functional conservation — but not always. A highly conserved gene in mouse and human likely has the same function. But regulatory differences (when/where a gene is expressed) can cause different phenotypes even from the same gene. Always check: is the protein domain conserved, or just raw sequence? Are there known regulatory differences? A 40% identity ortholog with a conserved catalytic domain can be more functionally equivalent than a 90% identity paralog in the same species.
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 · +86 lines · +72 tokens per session 12863e527075
- 1mo ago First seen · 264 lines · 84 tokens per session scan A 16584755a20d
tooluniverse-model-organism-genetics is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 156 tokens to every session and 8,987 once invoked, about $0.0006 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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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.