Germinal Agent Skill

> (1) Designing epitope-targeted nanobodies or scFvs, (2) Needing CDR design on a fixed framework, (3) Working on antibody-format binders rather than miniproteins. For miniprotein binders, use binder-design (BoltzGen, BindCraft, RFdiffusion, Mosaic). For structure validation, use boltz or chai.

743 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

151

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/adaptyvbio/protein-design-skills --skill germinal

The instruction itself

8 sections, as written by the author

Germinal Antibody and Nanobody Design

Germinal is an open pipeline for

epitope-targeted de novo antibody and nanobody design. It hallucinates CDRs on a

fixed framework, designs sequences with AbMPNN, and cofolds with a structure

predictor (it downloads AlphaFold-Multimer params). Runnable through biomodals.

The biomodals author notes Germinal is finicky and suggests BoltzGen for general

binder design; treat Germinal as the antibody-format option, not a default.

Prerequisites

| Requirement | Value |

|-------------|-------|

| Runner | Modal (biomodals) |

| GPU | H100 (default; GPU env var) |

| Setup | See Getting started |

How to run

git clone https://github.com/hgbrian/biomodals && cd biomodals

uv run --with modal --with PyYAML modal run modal_germinal.py \
  --target-yaml target_example.yaml \
  --max-trajectories 1 \
  --max-passing-designs 1

Key parameters

| Parameter | Default | Description |

|-----------|---------|-------------|

| --target-yaml | required | Target config (target_name, target_pdb_path, target_chain, binder_chain, target_hotspots, length) |

| --run-type | vhh | vhh (nanobody) or scfv |

| --max-trajectories | 100 | Trajectories to run |

| --max-passing-designs | 10 | Stop after this many passing designs |

| --out-dir | ./out/germinal | Output directory |

Target YAML

target_name: PDL1
target_pdb_path: target.pdb
target_chain: A
binder_chain: B
target_hotspots: "45,67,89"
length: 120

Decision tree

Antibody-format binder?
│
├─ Nanobody / VHH → germinal (run-type vhh) or mber
├─ scFv → germinal (run-type scfv)
└─ Miniprotein (not antibody) → binder-design (boltzgen, bindcraft, mosaic)

For VHH nanobodies, biomodals also has modal_mber.py (mBER) and modal_iggm.py

(IgGM) as alternatives.

Cost

Adaptyv's own tests of these models showed Germinal costing about $1.60 per accepted

design, averaged across 7 targets.

Troubleshooting

| Issue | Cause | Fix |

|-------|-------|-----|

| Pipeline fails early | Missing PyYAML | Add --with PyYAML to the invocation |

| No passing designs | Hard epitope or low budget | Raise --max-trajectories |

| OOM | Large target | Use the default H100 or trim the target |

Next: Validate with boltz or chai, rank with ipsae, filter with protein-qc.

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How to use it

Copy the folder

Take adaptyvbio/germinal from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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