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-statistical-modelinggit 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-statistical-modeling)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-statistical-modeling"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-statistical-modeling/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-statistical-modeling"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-statistical-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00090 | $0.09677 |
| Opus 5.5 | $0.00036 | $0.03871 |
| Sonnet 5.5 | $0.00018 | $0.01935 |
| Haiku 4.5 | $0.00009 | $0.00968 |
Grade A, and why
tooluniverse-statistical-modeling 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 — 687 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Modeling for Biomedical Data Analysis
RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
*_executed.ipynb→ read withtu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'and cite its cell outputs as the authoritative answer- Pre-computed result files (CSV/TSV with names like
*results*,*deseq*,*enrich*,*stats*,*_simplified.csv) → read directly and report the requested value - Canonical analysis scripts (
analysis.R,run_*.py,find_*.R,*.Rmd) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
PRIMARY SCRIPTS — use these FIRST
These scripts encode the question-specific gotchas in scripts/ and emit
labelled, parseable output. Prefer them over ad-hoc statsmodels / scipy code.
| Script | When to use it |
|---|---|
r_natural_spline_regression.py |
ANY question that mentions R syntax lm(y ~ ns(x, df = K)), "natural spline", or asks for spline R²/F/peak prediction CIs. Always shells out to Rscript so splines::ns() matches. |
spline_model_compare.py |
"Best-fitting model among quadratic, cubic and natural spline" / "max colony area at the optimal x". Fits all three in R, ranks by adj-R²/AIC/BIC, and reports the BEST model's peak (x*, y*) with 95% CI. |
logistic_regression_or.py |
Binary or ordinal logistic regression where the answer is an OR (or OR + 95% CI). Handles label encoding, explicit Placebo=0/BCG=1 maps, AND interaction terms (--interaction A:B -> creates A_B = A*B). Prints OR + CI for every coefficient and a SCALARS block for the requested --coef-name. |
power_analysis.py |
"Minimum sample size per group", "TTestIndPower", "given Cohen's d, what N for power=0.8". Computes pooled-SD Cohen's d from a CSV (or accepts --effect-size), then TTestIndPower.solve_power. |
expression_anova.py |
Per-gene ANOVA / median LFC across cell types or sample groups (NOT pooled across genes — see warnings below). |
prepare_ae_cohort.py |
Clinical-trial AE severity tests (chi-square / ordinal) on SDTM DM/AE files (encoding='latin1', max(AESEV) per subject across ALL AEs — no AEPT filter). |
stat_tests.py |
Stdlib-only chi-square goodness-of-fit, Fisher's exact, simple OLS. Use when scipy/statsmodels aren't available. |
What ships with it
20 files 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.
- anova_and_tests.md 4.3 KB
- EXAMPLES.md 9.0 KB
- QUICK_START.md 5.6 KB
- references/common_patterns.md 14 KB
- references/cox_regression.md 14 KB
- references/linear_models.md 11 KB
- references/logistic_regression.md 9.6 KB
- references/ordinal_logistic.md 13 KB
- references/troubleshooting.md 12 KB
- scripts/expression_anova.py 5.7 KB runs code
- scripts/format_statistical_output.py 11 KB runs code
- scripts/logistic_regression_or.py 11 KB runs code
- scripts/model_diagnostics.py 15 KB runs code
- scripts/power_analysis.py 5.8 KB runs code
- scripts/prepare_ae_cohort.py 5.0 KB runs code
- scripts/r_natural_spline_regression.py 11 KB runs code
- scripts/sdtm_ordinal_logistic.py 9.0 KB runs code
- scripts/spline_model_compare.py 11 KB runs code
- scripts/stat_tests.py 22 KB runs code
- TOOLS_REFERENCE.md 5.9 KB
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 Changed · +27 lines c9844766824e
- 1mo ago First seen · 660 lines · 90 tokens per session scan A 8f77fa55cd78
tooluniverse-statistical-modeling is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 90 tokens to every session and 9,677 once invoked, about $0.0004 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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