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-singler-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-singler-remote-tool)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/setup-singler-remote-tool"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/setup-singler-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-singler-remote-tool"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/setup-singler-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 118 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 | $0.00040 | $0.01924 |
| Opus 5 | $0.00020 | $0.00962 |
| Sonnet 5 | $0.00008 | $0.00385 |
| Haiku 4.5 | $0.00004 | $0.00192 |
Grade A, and why
setup-singler-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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set up SingleR as a remote tool
Validation status (2026-08-16): R 4.4.3/SingleR 2.8.0, loopback discovery, and a deterministic query/reference call passed with 240 aligned labels. Public publication, cross-user isolation, representative reference/accuracy validation, 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.
- CPU only; size RAM for test/reference matrices.
- 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 singler
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/singler
. .venvs/singler/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .
python -m pip install -r src/tooluniverse/remote/singler/requirements.txt
Rscript -e 'if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager"); BiocManager::install(c("SingleR","celldex","Matrix","jsonlite"))'
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 and optionally RSCRIPT_BIN. Use only approved built-in/relative references; verify licenses.
Authorize once, then share with one short command
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 · 175 lines · 40 tokens per session scan A f3d845fc59fd
setup-singler-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 40 tokens to every session and 1,924 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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