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-metabolomics-pathwaygit 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-metabolomics-pathway)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-metabolomics-pathway"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-metabolomics-pathway/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-metabolomics-pathway"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-metabolomics-pathway.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 | $0.01954 |
| Opus 5.5 | $0.00031 | $0.00782 |
| Sonnet 5.5 | $0.00016 | $0.00391 |
| Haiku 4.5 | $0.00008 | $0.00195 |
Grade A, and why
tooluniverse-metabolomics-pathway 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metabolomics Pathway Analysis
Identify metabolites, map to metabolic pathways, find disease associations, and connect to enzymes/genes.
Domain Reasoning
Metabolite-to-pathway mapping requires correct, database-specific identifiers. HMDB IDs link to KEGG/Reactome but must be converted via BridgeDb; PubChem CIDs need explicit cross-referencing. Always verify metabolite identity first: the same common name can refer to structurally distinct isomers, and PubChem names frequently differ from CTD/KEGG names.
LOOK UP DON'T GUESS
- Pathway membership: call
MetaCyc_get_compound,KEGG_get_compound, orReactomeContent_search - Cross-database IDs: use
BridgeDb_xrefs - Enzyme-metabolite relationships: use
CTD_get_chemical_gene_interactionsorKEGG_get_compound - Disease associations: query
Metabolite_get_diseasesorCTD_get_chemical_diseases
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.
Workflow
Phase 0: Identify & Resolve → Phase 1: Characterize → Phase 2: Pathway Map →
Phase 3: Enzyme/Gene Linkage → Phase 4: Disease Associations → Phase 5: Cross-DB Enrichment → Report
Phase 0: Metabolite Identification & Resolution
By Name
Metabolite_search: query (REQUIRED), search_type ("name"/"formula"). Returns PubChem matches with CID, name, formula, MW, SMILES.
MetabolomicsWorkbench_search_compound_by_name: name (REQUIRED). Cross-reference with RefMet.
By Mass/Formula
MetabolomicsWorkbench_search_by_mz: mz (REQUIRED), adduct (e.g., "M+H"), tolerance. Uses moverz/REFMET/{mz}/{adduct}/{tolerance}.
MetabolomicsWorkbench_search_by_exact_mass: exact_mass (REQUIRED), tolerance. Uses moverz/REFMET/{mass}/M/{tolerance}.
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 · 155 lines · 78 tokens per session scan A c492863045dd
tooluniverse-metabolomics-pathway is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 78 tokens to every session and 1,954 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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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
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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.