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 adaptyvbio/protein-design-skills --skill germinalgit clone --depth 1 https://github.com/adaptyvbio/protein-design-skillsWrote 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/adaptyvbio/protein-design-skills/germinal)<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/germinal"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/germinal/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/adaptyvbio/protein-design-skills/germinal"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/germinal.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 | $0.00105 | $0.00847 |
| Opus 5 | $0.00053 | $0.00424 |
| Sonnet 5 | $0.00021 | $0.00169 |
| Haiku 4.5 | $0.00011 | $0.00085 |
Grade A, and why
germinal 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 11d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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.
- 11d ago First seen · 95 lines · 105 tokens per session scan A 80d3e63ef18e
germinal is a skill published in the GitHub repository adaptyvbio/protein-design-skills (158 stars, last pushed 3mo ago), licensed MIT. It adds 105 tokens to every session and 847 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.
Other skills, from other repositories
iggm
Antibody and nanobody CDR design using IgGM (generative model by TencentAI4S). Use this skill when: (1) Designing nanobody (VHH) CDR loops against a target, (2) Designing full antibody (heavy + light chain) CDRs, (3) Redesigning existing antibody CDRs, (4) Need antigen-conditioned antibody generation, (5) Generating…
mber
VHH nanobody design using mBER (Manifold Binder Engineering and Refinement). Use this skill when: (1) Designing VHH nanobody CDRs against a target protein, (2) Have an existing VHH scaffold and want to redesign CDR1/CDR2/CDR3, (3) Optimizing a known VHH binder, (4) Targeting specific hotspot residues on the antigen…
antibody-design-iggm
Antibody design using IgGM model. Use this skill when: (1) Epitope-conditioned de novo antibody design, (2) Antibody affinity maturation, (3) Using antigen PDB structure and epitope information. For binding affinity evaluation, use prodigy.
antibody-structure-prediction-tfold
Antibody-related structure prediction using tfold model. Use this skill when: (1) Predict antibody and nanobody structure of a given sequence, (2) Predict antigen-antibody complex structure of given sequences, (3) Using local GPU resources. For binding affinity evaluation, use prodigy.
protein-structure-design-boltzgen
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For structure…
bindcraft
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high…