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-dataset-discoverygit 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-dataset-discovery)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-dataset-discovery"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-dataset-discovery/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-dataset-discovery"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-dataset-discovery.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 | $0.00108 | $0.01968 |
| Opus 5 | $0.00054 | $0.00984 |
| Sonnet 5 | $0.00022 | $0.00394 |
| Haiku 4.5 | $0.00011 | $0.00197 |
Grade A, and why
tooluniverse-dataset-discovery scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(url, timeout=120) How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dataset Discovery
When to Use
- User asks "find me data about X" or "where can I get data on Y"
- User wants to analyze a relationship between variables
- User needs specific study designs (longitudinal, cross-sectional, experimental)
- User asks about specific surveys or cohorts
Step 1: Understand What the Research Question Requires
Before searching, determine the minimum data requirements:
Study design needed:
- "Does X predict CHANGES in Y over time?" → longitudinal (same people measured repeatedly). Cross-sectional data CANNOT answer this — don't settle for it.
- "Is X associated with Y?" → cross-sectional is sufficient (one-time measurement)
- "Does intervention X cause outcome Y?" → experimental (clinical trial with controls)
- "What genes/proteins are involved in X?" → omics (sequencing, expression, proteomics)
Variables needed:
- List the specific exposure, outcome, and confounder variables
- For each variable, note the measurement type (continuous, categorical, biomarker vs self-report)
- Identify minimum confounders needed (age, sex are almost always required; domain-specific confounders depend on the question)
Population needed:
- Age range, geography, clinical status, sample size requirements
- Power analysis: to detect a small effect (r=0.1), you need ~800 subjects at 80% power
Step 2: Search Strategy
Search from broadest to most specific. Use find_tools to discover available dataset search tools — don't rely on memorized tool names.
Layer 1 — Cross-repository search (cast wide net): Search tools that index datasets across thousands of repositories. These find datasets you didn't know existed.
- Search by: research topic keywords, variable names, population descriptors
- Look for: DOI-registered datasets, repository listings, government data portals
Layer 2 — Domain-specific repositories: Search repositories specialized for your data type.
- Health surveys: CDC, NHANES (search by variable name, not topic keywords)
- Genomics: SRA, ENA, ArrayExpress, GEO
- Proteomics: PRIDE, MassIVE
- Metabolomics: MetaboLights, Metabolomics Workbench
- Clinical: ClinicalTrials.gov (for trial data with results)
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.
- 11d ago First seen · 184 lines · 108 tokens per session scan A 893a68e98bbf
tooluniverse-dataset-discovery is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed yesterday), licensed Apache-2.0. It adds 108 tokens to every session and 1,968 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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