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-protein-interactionsgit 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-protein-interactions)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-protein-interactions"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-protein-interactions/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-protein-interactions"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-protein-interactions.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 | $0.00097 | $0.04225 |
| Opus 5.5 | $0.00039 | $0.01690 |
| Sonnet 5.5 | $0.00019 | $0.00845 |
| Haiku 4.5 | $0.00010 | $0.00422 |
Grade A, and why
tooluniverse-protein-interactions 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein Interaction Network Analysis
Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.
Domain Reasoning: Interaction Type Clarification
When asked about protein interactions, ask: physical interaction (do they bind?) or functional interaction (do they affect the same pathway)? STRING combines both — a high combined_score does not mean physical binding. For physical binding evidence, check the experimental score (escore) specifically. A high tscore (text mining) or dscore (database) with a low escore suggests co-annotation or co-citation, not direct binding.
LOOK UP DON'T GUESS: protein interaction scores, experimental evidence types, and whether two specific proteins have known co-crystal structures. Use STRING escore and BioGRID experimental data — do not infer binding from pathway co-membership alone.
Databases Used
| Database | Coverage | API Key | Purpose |
|---|---|---|---|
| STRING | 14M+ proteins, 5,000+ organisms | Not required | Primary interaction source |
| BioGRID | 2.3M+ interactions, 80+ organisms | Required | Fallback, curated data |
| SASBDB | 2,000+ SAXS/SANS entries | Not required | Solution structures |
| Complex Portal | Curated complexes (incl. CORUM data) | Not required | Curated complex membership/stoichiometry |
4-Phase Workflow
- Identifier Mapping —
STRING_map_identifiers(): validate protein names, get STRING IDs - Network Retrieval —
STRING_get_network()(primary);BioGRID_get_interactions()(fallback, requires API key) - Enrichment Analysis —
STRING_functional_enrichment()for GO/KEGG/Reactome;STRING_ppi_enrichment()to test functional coherence - Structural Data (optional) —
SASBDB_search_entries()for SAXS/SANS solution structures
See python_implementation.py for runnable examples (example_tp53_analysis(), analyze_protein_network()).
What ships with it
9 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 Changed · +80 lines · +22 tokens per session 283d58efaf2d
- 1mo ago First seen · 160 lines · 75 tokens per session scan A b389885b20a1
tooluniverse-protein-interactions is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,744 stars, last pushed yesterday), licensed Apache-2.0. It adds 97 tokens to every session and 4,225 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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