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 generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill boltzgen --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal (recommended) ```bash # 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 ```bash git clone https://github.com/HannesStark/boltzgen.git cd boltzgen pip install -e . python sample.py config=config.yaml ``` ### Option 3: Python API ```python from boltzgen import BoltzGen model = B
- Prerequisites
- How to run
- Option 1: Modal (recommended)
- Option 2: Local installation
- Option 3: Python API
- Key parameters (CLI)
- YAML configuration
- Basic Binder Config
- Binder with Specific Binding Site
- Peptide Design (Cyclic)
- Design protocols
- Output format
- Sample output
- Successful run
Clone biomodals git clone https://github.com/hgbrian/biomodals && cd biomodals Run BoltzGen (requires YAML config file) modal run modal_boltzgen.py \ With custom GPU git clone https://github.com/HannesStark/boltzgen.git cd boltzgen pip install -e . python sample.py config=config.yaml find output -name "*.cif" | wc -l # Should match num_samples
What does the boltzgen skill do?
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 generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill boltzgen --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.