Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_Hubnpx agentmods add skills/zongtingwei/bioclaw_skills_hub/boltzgenWrote 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/zongtingwei/bioclaw_skills_hub/boltzgen)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/boltzgen"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/boltzgen/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/zongtingwei/bioclaw_skills_hub/boltzgen"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/boltzgen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00108 | $0.02405 |
| Opus 5 | $0.00054 | $0.01203 |
| Sonnet 5 | $0.00022 | $0.00481 |
| Haiku 4.5 | $0.00011 | $0.00241 |
Grade A, and why
boltzgen 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BoltzGen All-Atom Design
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.0+ | 12.1+ |
| GPU VRAM | 24GB | 48GB (L40S) |
| RAM | 32GB | 64GB |
How to run
First time? See Installation Guide to set up Modal and biomodals.
Option 1: Modal (recommended)
# Clone biomodals
git clone https://github.com/hgbrian/biomodals && cd biomodals
# Run BoltzGen (requires YAML config file)
modal run modal_boltzgen.py \
--input-yaml binder_config.yaml \
--protocol protein-anything \
--num-designs 50
# With custom GPU
GPU=L40S modal run modal_boltzgen.py \
--input-yaml binder_config.yaml \
--protocol protein-anything \
--num-designs 100
GPU: L40S (48GB) recommended | Timeout: 120min default
Available protocols: protein-anything, peptide-anything, protein-small_molecule, nanobody-anything, antibody-anything
Option 2: Local installation
git clone https://github.com/HannesStark/boltzgen.git
cd boltzgen
pip install -e .
python sample.py config=config.yaml
Option 3: Python API
from boltzgen import BoltzGen
model = BoltzGen.load_pretrained()
designs = model.sample(
target_pdb="target.pdb",
num_samples=50,
binder_length=80
)
GPU: L40S (48GB) | Time: ~30-60s per design
Key parameters (CLI)
| Parameter | Default | Description |
|---|---|---|
--input-yaml |
required | Path to YAML design specification |
--protocol |
protein-anything |
Design protocol |
--num-designs |
10 | Number of designs to generate |
--steps |
all | Pipeline steps to run (e.g., design inverse_folding) |
YAML configuration
BoltzGen uses an entity-based YAML format where you specify designed proteins and target structures as entities.
Important notes:
- Residue indices use
label_seq_id(1-indexed), not author residue numbers - File paths are relative to the YAML file location
- Target files should be in CIF format (PDB also works but CIF preferred)
- Run
boltzgen check config.yamlto verify your specification before running
What ships with it
2 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 · 310 lines · 108 tokens per session scan A 31163b83cf2c
boltzgen is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 108 tokens to every session and 2,405 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
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 backbone-only…
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…
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 backbone-only…
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 sequence-only…
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…
rfdiffusion
Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying…