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-antibody-engineeringgit 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-antibody-engineering)<a href="https://agentmods.dev/skills/mims-harvard/tooluniverse/tooluniverse-antibody-engineering"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-antibody-engineering/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-antibody-engineering"><img src="https://agentmods.dev/badge/skills/mims-harvard/tooluniverse/tooluniverse-antibody-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00095 | $0.03096 |
| Opus 5 | $0.00048 | $0.01548 |
| Sonnet 5 | $0.00019 | $0.00619 |
| Haiku 4.5 | $0.00010 | $0.00310 |
Grade A, and why
tooluniverse-antibody-engineering 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 12d 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Antibody Engineering & Optimization
AI-guided antibody optimization pipeline from preclinical lead to clinical candidate. Covers sequence humanization, structure modeling, affinity optimization, developability assessment, immunogenicity prediction, and manufacturing feasibility.
KEY PRINCIPLES:
- Report-first approach - Create optimization report before analysis
- Evidence-graded humanization - Score based on germline alignment and framework retention
- Developability-focused - Assess aggregation, stability, PTMs, immunogenicity
- Structure-guided - Use AlphaFold/PDB structures for CDR analysis
- Clinical precedent - Reference approved antibodies for validation
- Quantitative scoring - Developability score (0-100) combining multiple factors
- English-first queries - Always use English terms in tool calls, even if user writes in another language. Respond in user's language
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
When to Use
Apply when user asks:
- "Humanize this mouse antibody sequence"
- "Optimize antibody affinity for [target]"
- "Assess developability of this antibody"
- "Predict immunogenicity risk for [sequence]"
- "Engineer bispecific antibody against [targets]"
- "Reduce aggregation in antibody formulation"
- "Design pH-dependent binding antibody"
- "Analyze CDR sequences and suggest mutations"
Critical Workflow Requirements
1. Report-First Approach (MANDATORY)
- Create the report file FIRST:
antibody_optimization_report.md - Progressively update as analysis completes
- Output separate files:
optimized_sequences.fasta- All optimized variantshumanization_comparison.csv- Before/after comparisondevelopability_assessment.csv- Detailed scores
See REPORT_TEMPLATE.md for the full report template with section formats.
What ships with it
8 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.
- 12d ago First seen · 324 lines · 95 tokens per session scan A 527defa9bd53
tooluniverse-antibody-engineering is a skill published in the GitHub repository mims-harvard/ToolUniverse (1,680 stars, last pushed 2d ago), licensed Apache-2.0. It adds 95 tokens to every session and 3,096 once invoked, about $0.0005 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.
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