How do I install the Germinal skill?

Run npx skillmds@latest add adaptyvbio/germinal in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Germinal safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Germinal?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Germinal free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Germinal?

adaptyvbio (@adaptyvbio) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Germinal by adaptyvbio — Agent Skill

Name: Germinal
Rating: 4.2 (5 reviews)
Author: adaptyvbio

Germinal

De novo antibody and nanobody (VHH) design with Germinal. Use this skill when: (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.

adaptyvbio Updated Sep 10, 2026

File contents

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.

---name: germinaldescription: De novo antibody and nanobody (VHH) design with Germinal. Use this skill when: (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.license: MIT---# Germinal Antibody and Nanobody Design[Germinal](https://github.com/SantiagoMille/germinal) is an open pipeline forepitope-targeted de novo antibody and nanobody design. It hallucinates CDRs on afixed framework, designs sequences with AbMPNN, and cofolds with a structurepredictor (it downloads AlphaFold-Multimer params). Runnable through biomodals.The biomodals author notes Germinal is finicky and suggests BoltzGen for generalbinder 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](../../docs/getting-started.md) |## How to run```bashgit clone https://github.com/hgbrian/biomodals && cd biomodalsuv 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```yamltarget_name: PDL1target_pdb_path: target.pdbtarget_chain: Abinder_chain: Btarget_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.## CostAdaptyv's own tests of these models showed Germinal costing about $1.60 per accepteddesign, 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`.

Frequently asked questions