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-pharmacokineticsgit 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-pharmacokinetics)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-pharmacokinetics"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-pharmacokinetics/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-pharmacokinetics"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-pharmacokinetics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.01693 |
| Opus 5.5 | $0.00052 | $0.00677 |
| Sonnet 5.5 | $0.00026 | $0.00339 |
| Haiku 4.5 | $0.00013 | $0.00169 |
Grade A, and why
tooluniverse-pharmacokinetics 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 1mo 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pharmacokinetic (PK) Analysis — Non-Compartmental Analysis
Turn a concentration-vs-time profile after a dose into the standard PK parameters, and compute bioavailability from IV + oral data. Non-compartmental analysis (NCA) is the model-independent workhorse used for most PK reporting.
When to use this
- You have measured plasma/serum (or other matrix) drug concentrations at known times after a dose.
- You need Cmax/Tmax/AUC/half-life/clearance/Vd, or absolute bioavailability F.
- Comparing exposure (AUC, Cmax) between formulations, doses, or routes.
This is measured-data PK. For predicting ADMET properties from a chemical structure, use tooluniverse-admet-prediction.
Step 1 — Prepare the concentration-time data
| Issue | What to do |
|---|---|
| Units — be consistent | One time unit (h), one concentration unit (mg/L or ng/mL), one dose unit (mg). Pass them as time_unit/conc_unit/dose_unit. CL and Vd come back in derived units (e.g. L/h, L). |
| Route matters | Set route to iv or po/oral. CL and Vd are only directly interpretable for IV data; from oral data they are apparent (CL/F, Vd/F) because absorption is incomplete. |
| Include t=0 | For IV bolus include the t=0 (back-extrapolated) point; for oral the pre-dose value is usually 0. |
| BLQ (below limit of quantification) | Leading BLQs before the first measurable → treat as 0; BLQs in the terminal tail → drop them (don't set to 0, it corrupts the terminal slope). |
| Sampling design | You need enough late points to define the terminal phase (≥3 points clearly in the log-linear decline) or the half-life and AUC0-∞ are unreliable. |
| Single vs multiple dose | NCA here assumes a single dose. For steady-state, analyze one dosing interval (AUC0-τ) and say so. |
Step 2 — Run NCA
tu run NCA_compute_parameters '{
"times":[0,0.5,1,2,4,8,12,24],
"concentrations":[0,2.5,4.8,6.1,4.2,2.1,1.0,0.2],
"dose":100, "route":"iv",
"dose_unit":"mg", "conc_unit":"mg/L", "time_unit":"h"}'
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.
- 1mo ago First seen · 88 lines · 130 tokens per session scan A c8d688acc796
tooluniverse-pharmacokinetics is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 130 tokens to every session and 1,693 once invoked, about $0.0005 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 2026-09-03.
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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.