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-epidemiological-analysisgit 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-epidemiological-analysis)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-epidemiological-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-epidemiological-analysis/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-epidemiological-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-epidemiological-analysis.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 | $0.00085 | $0.03065 |
| Opus 5 | $0.00043 | $0.01533 |
| Sonnet 5 | $0.00017 | $0.00613 |
| Haiku 4.5 | $0.00009 | $0.00307 |
Grade A, and why
tooluniverse-epidemiological-analysis 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 12d 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.
r = requests.get(url, timeout=120) How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Epidemiological Data Analysis
Complete workflow for observational epidemiology — from research question to publication-ready report. Write and run Python code for every step. Never describe what you "would do" — do it.
Step 1: Formulate the Research Question (PECO Framework)
Define Population, Exposure, Comparator, Outcome before touching data.
- Population: Who? (e.g., adults aged 20-79, cancer patients stage III+, ICU admissions)
- Exposure: What factor? (e.g., nutrient intake, drug treatment, gene mutation, environmental pollutant)
- Comparator: Vs. what? (e.g., lowest tertile, unexposed, wild-type, placebo)
- Outcome: What health event? (e.g., disease incidence, survival time, biomarker level, mortality)
Study design check: Does the question require temporality?
- Cross-sectional: prevalence, associations at one time point
- Longitudinal/cohort: incidence, causal inference, temporal relationships
- Case-control: rare outcomes, odds ratios (nested within cohort)
- Clinical trial: intervention effects with randomized controls
If the question implies causation ("does X cause Y?") but only cross-sectional data is available, state the limitation explicitly and proceed with association language.
Step 2: Find and Evaluate Data
Use ToolUniverse to discover datasets and find what prior studies used:
# Search for relevant datasets — use find_tools to discover what's available
find_tools("dataset search")
find_tools("your domain keywords") # e.g., "cancer genomics", "clinical trial", "survey health"
# Search literature for study precedents — papers cite their data sources
execute_tool("PubMed_search_articles", {"query": "[exposure] [outcome] [study design]", "max_results": 5})
execute_tool("EuropePMC_search_articles", {"query": "[exposure] [outcome] cohort", "limit": 5})
Evaluate dataset fitness: Does it have the exposure variable? The outcome? Key confounders (age, sex, plus domain-specific)? Adequate sample size?
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.
- 12d ago First seen · 269 lines · 85 tokens per session scan A edec91532f47
tooluniverse-epidemiological-analysis is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 2d ago), licensed Apache-2.0. It adds 85 tokens to every session and 3,065 once invoked, about $0.0004 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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