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.
git 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/commands/mims-harvard/tooluniverse/self-review)<a href="https://agentmods.dev/commands/mims-harvard/tooluniverse/self-review"><img src="https://agentmods.dev/badge/commands/mims-harvard/tooluniverse/self-review.svg" alt="Measured on agentmods" 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 | $0.00047 | $0.00324 |
| Opus 5 | $0.00023 | $0.00162 |
| Sonnet 5 | $0.00009 | $0.00065 |
| Haiku 4.5 | $0.00005 | $0.00032 |
Grade A, and why
self-review 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 8d 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.
What it actually says
Apply the tooluniverse-self-review skill to this request: $ARGUMENTS
Interpret the request
- Treat
$ARGUMENTSas the evaluation instruction and optional target, not automatically as the task being evaluated. - If
$ARGUMENTSis empty or refers to "current work", "this", or "what we have", recover the original goal and current work from the conversation and available artifacts. - If an artifact, answer, file, diff, or section is supplied explicitly, review that target against its stated or preceding goal.
- Plain
eval,evaluate,review,assess, orcheckrequests are qualitative. Do not generate points, grades, weighted criteria, or numeric totals unless$ARGUMENTSexplicitly asks for them. - If the request is to create an eval suite, test, grader, or benchmark, treat it as an engineering task rather than running self-review.
Produce
By default, give a concise evidence-backed assessment with findings ordered by impact, meaningful strengths, prioritized fixes, and a plain-language completion verdict. Do not print RET scenarios, perspectives, criteria tables, or scoring machinery unless explicitly requested.
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.
- 8d ago First seen · 29 lines · 47 tokens per session scan A 58c512667621
self-review is a command published in the GitHub repository mims-harvard/ToolUniverse (1,676 stars, last pushed today), licensed Apache-2.0. It adds 47 tokens to every session and 324 once invoked, about $0.0002 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.
Other commands, from other repositories
template
Manage issue templates for streamlined issue creation.
discover
Search the Everruns(Dev) API catalog for available operations.
doctor
Badi configuration validation. Checks all Badi components and produces a diagnostic report.
swarm-status
Command documentation for swarm-status in category swarm.
archive-ledger
../../../shared/commands/archive-ledger.md.
research
Document codebase as-is with thoughts directory for historical context.