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-diagnostic-test-evaluationgit 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-diagnostic-test-evaluation)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-diagnostic-test-evaluation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-diagnostic-test-evaluation/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-diagnostic-test-evaluation"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-diagnostic-test-evaluation.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 | $0.00130 | $0.01467 |
| Opus 5 | $0.00065 | $0.00733 |
| Sonnet 5 | $0.00026 | $0.00293 |
| Haiku 4.5 | $0.00013 | $0.00147 |
Grade A, and why
tooluniverse-diagnostic-test-evaluation 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 11d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnostic Test / Biomarker Accuracy Evaluation
Judge how well a test or biomarker discriminates disease — at a fixed cutoff (2×2) or across all cutoffs (ROC) — and turn a result into a probability of disease.
Which case are you in?
| You have… | Go to |
|---|---|
| A 2×2 table (TP/FP/TN/FN) at a fixed cutoff | Step 1 (Epidemiology_diagnostic) |
| A continuous biomarker score + true labels | Step 2 (ROC / AUC / Youden, Python) |
| A test's sens/spec + a patient's pre-test probability | Step 3 (Epidemiology_bayesian) |
Step 1 — Fixed-cutoff metrics from a 2×2 table
tu run Epidemiology_diagnostic '{"operation":"diagnostic","tp":90,"fp":10,"tn":180,"fn":20}'
Returns sensitivity, specificity, PPV, NPV, accuracy, LR_pos, LR_neg, and the sample prevalence.
| Metric | Question it answers | Depends on prevalence? |
|---|---|---|
| Sensitivity = TP/(TP+FN) | Of those WITH disease, what fraction test positive? | No |
| Specificity = TN/(TN+FP) | Of those WITHOUT disease, what fraction test negative? | No |
| PPV = TP/(TP+FP) | If positive, what's the chance of disease? | Yes — strongly |
| NPV = TN/(TN+FN) | If negative, what's the chance of being disease-free? | Yes |
| LR+ = sens/(1−spec) | How much a positive raises the odds of disease | No |
| LR− = (1−sens)/spec | How much a negative lowers the odds | No |
The PPV/NPV trap. Sensitivity and specificity are properties of the test; PPV and NPV depend on the disease prevalence in the tested population. A test with great sens/spec has poor PPV in a low-prevalence (screening) setting. Never quote PPV/NPV from a case-control design (its 50/50 prevalence is artificial) — compute them for the real-world prevalence with
Epidemiology_bayesian(Step 3). Report sensitivity, specificity, and likelihood ratios as the prevalence-independent summary.
Step 2 — ROC / AUC / optimal cutoff for a continuous biomarker
When the test is a continuous score, evaluate across all thresholds:
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.
- 11d ago First seen · 96 lines · 130 tokens per session scan A e26ee602e74a
tooluniverse-diagnostic-test-evaluation is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 2d ago), licensed Apache-2.0. It adds 130 tokens to every session and 1,467 once invoked, about $0.0006 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 2026-08-30.
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