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-meta-analysisgit 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-meta-analysis)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-meta-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-meta-analysis/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-meta-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-meta-analysis.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 | $0.00139 | $0.02126 |
| Opus 5.5 | $0.00056 | $0.00850 |
| Sonnet 5.5 | $0.00028 | $0.00425 |
| Haiku 4.5 | $0.00014 | $0.00213 |
Grade A, and why
tooluniverse-meta-analysis 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta-Analysis & Evidence Synthesis
Pool quantitative results from multiple studies into one estimate, and judge how consistent the studies are. This is the statistical half of a systematic review (the literature-collection half is tooluniverse-literature-deep-research).
When to use this
- You have effect sizes from ≥2 studies/cohorts and want a single pooled estimate + CI.
- Synthesizing a systematic review, replicated experiments, multi-cohort GWAS, or multi-dataset associations.
- Deciding whether studies agree (low heterogeneity) or conflict (high heterogeneity).
Do NOT use it to find the studies — use tooluniverse-literature-deep-research / the literature tools for that, then bring the extracted numbers here. Before trusting any input study, consider checking it with Crossref_check_retraction.
The workflow
1. Extract (effect, SE) per study ← THE ERROR-PRONE STEP
2. Pick fixed vs random effects
3. Pool: MetaAnalysis_run
4. Read heterogeneity (I², Q, τ²)
5. Forest plot + interpret
Step 1 — Convert each study to (effect_size, se) ← do this carefully
The pooling step needs an effect size on an additive scale and its standard error. Ratio measures (OR/RR/HR) must be log-transformed first. Most reported numbers give you a CI, not an SE — derive the SE from the CI.
| What the paper reports | effect_size | se |
|---|---|---|
OR / RR / HR with 95% CI [L, U] |
ln(point) |
(ln(U) − ln(L)) / (2 × 1.96) |
| OR / RR / HR with a p-value (no CI) | ln(point) |
` |
GWAS / regression β with SE |
β (as reported) |
the reported SE |
| Two groups, means + SDs + n₁,n₂ | Hedges' g (see script) | SE of g (see script) |
Single proportion p, n |
logit(p)=ln(p/(1−p)) |
sqrt(1/(np) + 1/(n(1−p))) |
Pearson correlation r, n |
Fisher z = atanh(r) |
1 / sqrt(n − 3) |
Critical rules
- Log-transform ratio measures. Pooling raw ORs is wrong; pool
ln(OR)and exponentiate the pooled result back. The script does this for you. - One direction. Make sure every study's effect points the same way (e.g. "exposure increases risk"); flip sign / invert the ratio for studies coded the opposite way.
- Same effect measure. Don't mix OR with HR with mean-difference in one pool.
2 × 1.96assumes a 95% CI; use2 × 1.645for 90%,2 × 2.576for 99%.
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 Changed 8d2987381af5
- 1mo ago First seen · 115 lines · 139 tokens per session scan A 6f8c042fed5d
tooluniverse-meta-analysis is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 139 tokens to every session and 2,126 once invoked, about $0.0006 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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