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 setup-scanvi-remote-toolgit 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/setup-scanvi-remote-tool)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/setup-scanvi-remote-tool"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/setup-scanvi-remote-tool/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/setup-scanvi-remote-tool"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/setup-scanvi-remote-tool.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low MCP Rug Pull · line 117 pip install without ==version installs the latest release, which could include malicious changes.Fix: Pin the version: pip install package==1.2.3
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 | $0.00042 | $0.01880 |
| Opus 5 | $0.00021 | $0.00940 |
| Sonnet 5 | $0.00008 | $0.00376 |
| Haiku 4.5 | $0.00004 | $0.00188 |
Grade A, and why
setup-scanvi-remote-tool 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 7d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set up scANVI as a remote tool
Validation status (2026-08-16): scANVI dependencies, GB10 execution, loopback discovery, and a bounded labeled/unlabeled annotation call passed with aligned predictions. Public publication, cross-user isolation, representative accuracy, broad concurrency, and recovery remain incomplete. Authenticated private Platform import and owner testing passed on 2026-08-16; public publication and independent-caller authorization/isolation remain untested.
Prerequisites
- Run from the ToolUniverse repository root on Linux with Python 3.12.3.
- GPU recommended for training; CPU only for tiny tests.
- Keep provider data, weights, caches, and credentials outside Git.
- Bind to loopback. A non-loopback bind requires TOOLUNIVERSE_API_TOKEN; never put it in arguments or results.
Run the standard-library contract check before downloading large dependencies:
python scripts/remote_validation/setup_skill_preflight.py --implementation scanvi
After exporting provider resources, add --check-provider-env. After the
server starts, add --live to verify the exact MCP tool set without running
the model. Before sharing, add --check-connect-prereqs; this reports only
whether a key is set and never prints its value.
Create an isolated environment
python3 -m venv .venvs/scanvi
. .venvs/scanvi/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .
python -m pip install -r src/tooluniverse/remote/scanvi/requirements.txt
Package/network-dependent commands must be rerun in a clean environment before marking this skill complete.
Obtain credentials, data, and model weights
- Set TOOLUNIVERSE_REMOTE_DATA_ROOT. labels_key/unlabeled_category must match obs; verify data rights.
Authorize once, then share with one short command
After installing dependencies, exporting the provider resources above, and installing the pinned relay SDK described under Connect below, run from the repository root. Log in only once per machine (and again after key rotation):
What ships with it
1 file 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.
- 7d ago First seen · 174 lines · 42 tokens per session scan A a47e20cb8382
setup-scanvi-remote-tool is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 1,880 once invoked, about $0.0002 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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