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-nih-funding-landscapegit 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-nih-funding-landscape)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-nih-funding-landscape"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-nih-funding-landscape/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-nih-funding-landscape"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-nih-funding-landscape.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 | $0.00133 | $0.05378 |
| Opus 5 | $0.00067 | $0.02689 |
| Sonnet 5 | $0.00027 | $0.01076 |
| Haiku 4.5 | $0.00013 | $0.00538 |
Grade A, and why
tooluniverse-nih-funding-landscape 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 8d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NIH Funding Landscape
Build source-traceable NIH funding analyses with explicit scopes, count units, data coverage, and interpretation limits. Use OpenNIH_* for funding facts and other ToolUniverse sources only for downstream evidence they actually cover.
Always read references/tool-reference.md before choosing tools or comparing totals. Read references/verified-cases.md when testing the skill, debugging an unexpected response, or adapting one of the verified case patterns. Read references/public-value-cases.md for a patient, family, advocate, journalist, trainee, applicant, taxpayer, policy, or other public-facing request.
Public Value Routing
Start from the reader's decision, not from the available endpoints:
| Reader | Optimize the answer for | Never imply |
|---|---|---|
| Patient, family, advocate | A topic-specific research map, recent activity, inspectable projects, and next contacts | Clinical expertise, quality of care, treatment advice, or patient benefit from funding alone |
| Journalist, taxpayer, policy analyst | A reproducible headline number, its definition, its largest drivers, and its sensitivity to alternate queries | That the largest number is the truest, a partial year is final, or spending caused outcomes |
| Researcher, trainee, applicant | Funded precedents, active mechanisms, institutions, and project language | Application odds, reviewer preferences, mentorship quality, or K99-to-R00 conversion |
| Institution or translational team | Resolved peer portfolios and exact identifiers for output follow-up | Raw-name totals as one entity or grant-output chronology as causality |
| Local reporter or community | A location-qualified portfolio joined through resolved institutions | That an institution-name substring is a city/state geography query or that award location equals beneficiary location |
| Entrepreneur | Topic-specific R41/R42/R43/R44 awards, companies, and phase-labeled project activity | Commercial success, current company status, addressable market, or Phase I-to-II conversion from annual award rows |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 196 lines · 133 tokens per session scan A a5d41170d094
tooluniverse-nih-funding-landscape is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 3d ago), licensed Apache-2.0. It adds 133 tokens to every session and 5,378 once invoked, about $0.0007 per session on Opus 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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