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-gene-liabilitygit 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-gene-liability)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-gene-liability"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-gene-liability/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-gene-liability"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-gene-liability.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 | $0.00069 | $0.01815 |
| Opus 5.5 | $0.00028 | $0.00726 |
| Sonnet 5.5 | $0.00014 | $0.00363 |
| Haiku 4.5 | $0.00007 | $0.00181 |
Grade A, and why
tooluniverse-gene-liability 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gene Liability Evaluation
Assess whether reducing a human gene's function is likely to be unsafe. Resolve the gene, gather independent human and model-system evidence, calculate a transparent 0–100 liability score, and recommend an appropriate modulation strategy.
Higher scores mean greater predicted liability from the stated intervention. This is a safety score, not a target-efficacy or druggability score.
Required Input
Accept a gene symbol, name, Ensembl ID, or UniProt accession. Also capture:
- intervention modality: knockout, knockdown, degrader, irreversible inhibitor, reversible inhibitor, antibody, or unknown;
- intended tissue and indication, when supplied;
- desired inhibition depth and duration, when supplied.
If modality is absent, assess complete systemic loss of function and label that assumption prominently. Do not silently generalize a knockout result to partial or tissue-restricted pharmacology.
Evidence Rules
- Look up the gene before reasoning. Do not score an ambiguous identifier.
- Use human causal evidence before animal models, screens, expression, or prediction.
- Treat absent data as unknown, never as evidence of safety.
- Cite every score-changing observation with the source and identifier.
- Separate germline loss of function, somatic loss, acute pharmacology, and chronic pharmacology.
- Treat DepMap as cancer-cell essentiality, not normal-tissue essentiality.
- Report contradictory evidence instead of averaging it away.
Grade evidence as:
- T1: human clinical outcome or replicated causal human genetics;
- T2: curated human evidence, mammalian knockout phenotype, or established drug class safety;
- T3: functional screen, tissue expression, or single experimental study;
- T4: computational prediction or catalog annotation.
Workflow
1. Resolve the gene
Use MyGene_query_genes with species="human" and request symbol, name, Ensembl,
UniProt, and Entrez identifiers. Require an exact symbol or identifier match. If the
query maps to multiple loci, stop and ask for clarification.
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 · 180 lines · 69 tokens per session scan A 48874430fea2
tooluniverse-gene-liability is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 69 tokens to every session and 1,815 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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nanoresearch-writing
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.