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-structure-predictiongit 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-structure-prediction)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-protein-structure-prediction"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-protein-structure-prediction/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-structure-prediction"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-protein-structure-prediction.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 | $0.00071 | $0.03557 |
| Opus 5 | $0.00036 | $0.01778 |
| Sonnet 5 | $0.00014 | $0.00711 |
| Haiku 4.5 | $0.00007 | $0.00356 |
Grade A, and why
tooluniverse-protein-structure-prediction 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.
How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein Structure Prediction and Analysis
End-to-end workflow for protein structure prediction starting from a sequence or UniProt accession. Combines ESMFold de novo prediction, AlphaFold database retrieval, experimental structure benchmarking from RCSB, ProtVar variant impact assessment, and ProtParam sequence property calculation.
KEY PRINCIPLES:
- Sequence first — obtain or verify the protein sequence before prediction
- ESMFold for fast de novo — works directly on sequence (up to ~800 residues); no database lookup needed
- AlphaFold for reference — retrieve precomputed AlphaFold model for comparison; use
qualifierparameter (UniProt accession) - Quality before interpretation — always report pLDDT scores; do not interpret low-confidence regions as folded
- Experimental validation — compare predictions to RCSB experimental structures when available
- ProtVar for variants — use when the question involves mutations or SNVs affecting structure
- English-first queries — use English protein names in all tool calls; respond in the user's language
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
When to Use
Apply when users ask:
- "Predict the structure of this sequence: [FASTA]"
- "What does the AlphaFold model for [protein] look like?"
- "How confident is the AlphaFold prediction for [protein]?"
- "Is there an experimental structure for [protein] and how does it compare to AlphaFold?"
- "How does mutation [variant] affect the structure of [protein]?"
- "What are the physicochemical properties of [protein] sequence?"
- "Predict the structure of this novel protein" / "I have a new sequence, can you model it?"
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 · 341 lines · 71 tokens per session scan A df814339216c
tooluniverse-protein-structure-prediction is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 2d ago), licensed Apache-2.0. It adds 71 tokens to every session and 3,557 once invoked, about $0.0004 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 2026-09-03.
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