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-neurosynth-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-neurosynth-meta-analysis)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-neurosynth-meta-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-neurosynth-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-neurosynth-meta-analysis"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-neurosynth-meta-analysis.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 | $0.00179 | $0.01785 |
| Opus 5.5 | $0.00072 | $0.00714 |
| Sonnet 5.5 | $0.00036 | $0.00357 |
| Haiku 4.5 | $0.00018 | $0.00178 |
Grade A, and why
tooluniverse-neurosynth-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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neurosynth Meta-Analytic Coordinate Decoding
Statistically decode human fMRI brain coordinates against ~14,000 published neuroimaging studies, or look up which studies/coordinates are associated with a cognitive term — using the real, freely-downloadable Neurosynth coordinate database (no API key, no login).
Honesty contract (read first)
This skill downloads a real coordinate database and runs real statistics on it. It must never fabricate a term association, a study result, or a p-value.
- Preflight before anything. Run
scripts/download_data.pyfirst. If any file fails to download, STOP and report the failure — do not proceed with partial data or describe hypothetical decoding results. - Never guess a term.
search_by_term.pyrequires an exact (case-sensitive) vocabulary match. If the term isn't found, it returns suggestions (substring matches, or close spellings when there are none, e.g.hipocampus→hippocampus) — use those, don't invent a plausible-sounding term. - Report the multiple-testing caveat every time.
decode_coordinate.pytests ~3,228 terms simultaneously per query. Uncorrected p-values will produce false positives at this scale — always surface the FDR-correctedfdr_q_value, not just the rawp_value, when characterizing how confident a result is. - This is a simplified reverse-inference test, not a full meta-analysis pipeline. It does binary term-presence comparison (near-coordinate studies vs. rest of corpus) via a two-proportion z-test — a real, defensible statistical test, but not the same as running NiMARE's full activation-likelihood-estimation (ALE) or multilevel kernel density analysis (MKDA) methods. Say so if precision matters (e.g., the user is drafting something for publication).
- Never overwrite the cached data files —
download_data.pywrites to a dedicated cache directory and is idempotent (skips re-download if already cached, unless--force).
When to Use This Skill
Apply when users:
- Give an MNI coordinate (or a named landmark you can translate to one, e.g. Broca's area ≈ (-50, 20, 10)) and ask what cognitive function it's associated with
- Ask to "decode" an fMRI activation peak from their own study against the published literature
- Want to know which studies report activation for a specific cognitive term, or where those studies' peaks are located
- Ask for a quick meta-analytic sanity check before writing a discussion section ("is this activation consistent with what's typically reported at this location?")
What ships with it
5 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.
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 · 78 lines · 179 tokens per session scan A 4de753eb655b
tooluniverse-neurosynth-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 179 tokens to every session and 1,785 once invoked, about $0.0007 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.
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