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-medical-imaging-radiologygit 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-medical-imaging-radiology)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-medical-imaging-radiology"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-medical-imaging-radiology/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-medical-imaging-radiology"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-medical-imaging-radiology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00285 | $0.01848 |
| Opus 5.5 | $0.00114 | $0.00739 |
| Sonnet 5.5 | $0.00057 | $0.00370 |
| Haiku 4.5 | $0.00028 | $0.00185 |
Grade A, and why
tooluniverse-medical-imaging-radiology 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 10d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cancer Radiology Imaging Cohort Discovery (TCIA)
Discover and characterize public cancer imaging cohorts from The Cancer Imaging Archive (TCIA, National Cancer Institute) — collection, patient, study, and DICOM series-level metadata for building or scoping a radiology research cohort.
Scope: what this skill can and cannot do
Can: enumerate collections, patients, studies, and series; filter by modality/body part/manufacturer; pull DICOM series-level metadata (scanner, acquisition, license); estimate series download size; list individual DICOM instance (slice) UIDs.
Cannot: download or view actual pixel data, run segmentation/detection
models, or interpret images. Every TCIA tool here returns metadata about
what imaging exists — not the images themselves. If the user wants the
actual pixels, tell them the DICOM download UI/API is a separate TCIA
service (https://www.cancerimagingarchive.net/) outside this tool set.
TCIA data is public, de-identified cancer imaging. Study/series dates are
frequently shifted to a placeholder (e.g. 2000-01-01) as part of
de-identification — do not interpret these as real acquisition dates.
When to Use This Skill
- Find a public radiology imaging collection for a given cancer type or organ (e.g. lung nodule CT, glioblastoma MRI, breast MRI)
- Count patients, studies, or series in a collection, optionally filtered by modality or body part
- Check which modalities (CT/MR/PT/CR/DX/US/NM), body parts, or scanner manufacturers are represented in a collection before committing to it
- Get DICOM series-level metadata (equipment, acquisition parameters,
license) for a specific
SeriesInstanceUID - Estimate total download size (bytes, image count) for a series before retrieving it
- Distinguish real patients from phantom/QA test objects in a collection
NOT for (route elsewhere):
- Microscopy/cell imaging quantification (ImageJ/CellProfiler/QuPath
measurements, colony counts, fluorescence intensity) ->
tooluniverse-image-analysis - Actual pixel-level image analysis, segmentation, or diagnostic interpretation of DICOM images -> not covered by any ToolUniverse skill; these tools are metadata-only
- Tumor genomic/transcriptomic/mutation data (even for the same TCGA
collections that TCIA also has imaging for) ->
tooluniverse-cancer-genomics-tcga
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.
- 10d ago First seen · 119 lines · 285 tokens per session scan A 700ffed1df28
tooluniverse-medical-imaging-radiology is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 285 tokens to every session and 1,848 once invoked, about $0.0011 per session on Opus 5.5. 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 2026-10-07.
Other skills, from other repositories
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nanoresearch-writing
Draft a LaTeX research paper from all previous stage outputs.
construct-toy-examples
Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal. Use when you are stuck in reasoning and need simpler examples to regain traction, or when you want to see where the assumptions take effect and gain intuition.
obtain-immediate-conclusions
Derive immediate mathematical consequences from a theorem statement or subgoal. Use when starting a new problem, branch, or subgoal, or when cheap progress or a cleaner reformulation is needed before deeper proof search.
clinical-research-analysis-framework
Guided workflow for statistical and ML analysis of clinical data. Use when planning or executing research analyses on MIMIC, eICU, or similar EHR data. Ensures methodological rigor through structured consultation, assumption checking, and stepwise execution with audit trails.
apache-iv-score
Calculate APACHE IV (Acute Physiology and Chronic Health Evaluation IV) score for ICU mortality prediction. Use for severity assessment, hospital mortality prediction, ICU benchmarking, or case-mix adjustment. eICU has pre-computed scores; MIMIC-IV requires custom implementation with diagnosis mapping challenges.