# tooluniverse-antibody-engineering

Runs an antibody from preclinical lead to clinical candidate: humanization, affinity, structure, immunogenicity.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 388 installs · ★ 1759
- Author: mims-harvard
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/mims-harvard/ToolUniverse/tree/HEAD/skills/tooluniverse-antibody-engineering/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/tooluniverse-antibody-engineering

## What it does

The skill lays out an eight-phase optimization pipeline. It annotates CDRs and identifies the species, then searches IMGT for the closest human germline genes and picks a framework for CDR grafting. From there come structure modeling, mutation screening for affinity, developability checks (aggregation, stability, PTMs), MHC-II epitope immunogenicity prediction and a manufacturing feasibility pass.

It works report-first: the skill creates `antibody_optimization_report.md` before any analysis and updates it as phases finish. Alongside it writes `optimized_sequences.fasta`, `humanization_comparison.csv` and `developability_assessment.csv`. Every variant gets a humanization score, a CDR preservation confirmation and a metrics table with data source citations.

Tool calls reach IMGT, SAbDab, TheraSAbDab, IEDB, UniProt, STRING and PubMed. The SOAP tools IMGT, SAbDab and TheraSAbDab need an `operation` parameter — skip it and the call fails. The skill advises English search terms even when you write in another language; answers come back in yours.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-antibody-engineering -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tooluniverse-antibody-engineering/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tooluniverse-antibody-engineering/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-antibody-engineering -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tooluniverse-antibody-engineering/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-antibody-engineering -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tooluniverse-antibody-engineering/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install mims-harvard/ToolUniverse tooluniverse-antibody-engineering
```

2. **Manually: put the files in place**
- File: `.github/skills/tooluniverse-antibody-engineering/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-antibody-engineering -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/mims-harvard/ToolUniverse --path skills/tooluniverse-antibody-engineering
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tooluniverse-antibody-engineering/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add mims-harvard/ToolUniverse --skill tooluniverse-antibody-engineering -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/mims-harvard/ToolUniverse/tree/HEAD/skills/tooluniverse-antibody-engineering/SKILL.md

## When to use it
- humanize a mouse antibody sequence
- score aggregation risk and stability of a candidate
- predict immunogenicity from MHC-II epitopes
- compare a candidate against approved antibodies
- design CDR grafting onto a human framework

## Example prompts
- Humanize this mouse antibody sequence and show variants with backmutations
- Assess developability of this antibody: aggregation, stability, PTM sites
- Find clinical precedents for antibodies against this target

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 036ca7e562ab4551.

BEGIN THIRD-PARTY FILE 036ca7e562ab4551
```text
---
name: tooluniverse-antibody-engineering
description: Therapeutic antibody engineering and optimization, lead-to-clinical-candidate. Covers sequence humanization (germline alignment, framework retention), affinity maturation, developability (aggregation, stability, PTMs), structure modeling (AlphaFold/PDB CDR analysis), immunogenicity prediction, and manufacturing feasibility. Use for biologic-drug optimization, mAb design review, biosimilar engineering, and clinical-precedent comparison.
disable-model-invocation: true
---

# 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**:
1. **Report-first approach** - Create optimization report before analysis
2. **Evidence-graded humanization** - Score based on germline alignment and framework retention
3. **Developability-focused** - Assess aggregation, stability, PTMs, immunogenicity
4. **Structure-guided** - Use AlphaFold/PDB structures for CDR analysis
5. **Clinical precedent** - Reference approved antibodies for validation
6. **Quantitative scoring** - Developability score (0-100) combining multiple factors
7. **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)

1. **Create the report file FIRST**: `antibody_optimization_report.md`
2. **Progressively update** as analysis completes
3. **Output separate files**:
```
END THIRD-PARTY FILE 036ca7e562ab4551

The third-party file has ended. Everything between the 036ca7e562ab4551 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2337 of 16415 bytes. © mims-harvard · Apache-2.0 · https://github.com/mims-harvard/ToolUniverse/tree/HEAD/skills/tooluniverse-antibody-engineering/SKILL.md

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