Boltzgen Agent Skill
> (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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill boltzgenWhat comes with it
6 162 bytes besides the instructionThe instruction itself
20 sections, as written by the authorBoltzGen 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
Basic Binder Config
entities:
# Designed protein (variable length 80-140 residues)
- protein:
id: B
sequence: 80..140
# Target from structure file
- file:
path: target.cif
include:
- chain:
id: A
# Specify binding site residues (optional but recommended)
binding_types:
- chain:
id: A
binding: 45,67,89
Binder with Specific Binding Site
entities:
- protein:
id: G
sequence: 60..100
- file:
path: 5cqg.cif
include:
- chain:
id: A
binding_types:
- chain:
id: A
binding: 343,344,251
structure_groups: "all"
Peptide Design (Cyclic)
entities:
- protein:
id: S
sequence: 10..14C6C3 # With cysteines for disulfide
- file:
path: target.cif
include:
- chain:
id: A
constraints:
- bond:
atom1: [S, 11, SG]
atom2: [S, 18, SG] # Disulfide bond
Design protocols
| Protocol | Use Case |
|----------|----------|
| protein-anything | Design proteins to bind proteins or peptides |
| peptide-anything | Design cyclic peptides to bind proteins |
| protein-small_molecule | Design proteins to bind small molecules |
| nanobody-anything | Design nanobody CDRs |
| antibody-anything | Design antibody CDRs |
Output format
output/
├── sample_0/
│ ├── design.cif # All-atom structure (CIF format)
│ ├── metrics.json # Confidence scores
│ └── sequence.fasta # Sequence
├── sample_1/
│ └── ...
└── summary.csv
Note: BoltzGen outputs CIF format. Convert to PDB if needed:
from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("design", "design.cif")
io = PDBIO()
io.set_structure(structure)
io.save("design.pdb")
Sample output
Successful run
$ modal run modal_boltzgen.py --input-yaml binder.yaml --protocol protein-anything --num-designs 10
Running: boltzgen run binder.yaml --output /tmp/out --protocol protein-anything --num_designs 10
[INFO] Loading BoltzGen model...
[INFO] Generating designs...
[INFO] Running inverse folding...
[INFO] Running structure prediction...
[INFO] Filtering and ranking...
[INFO] Pipeline complete
Results saved to: ./out/boltzgen/2501161234/
Output directory structure:
out/boltzgen/2501161234/
├── intermediate_designs/ # Raw diffusion outputs
│ ├── design_0.cif
│ └── design_0.npz
├── intermediate_designs_inverse_folded/
│ ├── refold_cif/ # Refolded complexes
│ └── aggregate_metrics_analyze.csv
└── final_ranked_designs/
├── final_10_designs/ # Top designs
└── results_overview.pdf # Summary plots
What good output looks like:
- Refolding RMSD < 2.0A (design folds as predicted)
- ipTM > 0.5 (confident interface)
- All designs complete pipeline without errors
Decision tree
Should I use BoltzGen?
│
├─ What type of design?
│ ├─ All-atom precision needed → BoltzGen ✓
│ ├─ Ligand binding pocket → BoltzGen ✓
│ └─ Standard miniprotein → RFdiffusion (faster)
│
├─ What matters most?
│ ├─ Side-chain packing → BoltzGen ✓
│ ├─ Speed / diversity → RFdiffusion
│ ├─ Highest success rate → BindCraft
│ └─ AF2 optimization → ColabDesign
│
└─ Compute resources?
├─ Have L40S/A100 (48GB+) → BoltzGen ✓
└─ Only A10G (24GB) → Consider RFdiffusion
Typical performance
| Campaign Size | Time (L40S) | Cost (Modal) | Notes |
|---------------|-------------|--------------|-------|
| 50 designs | 30-45 min | ~$8 | Quick exploration |
| 100 designs | 1-1.5h | ~$15 | Standard campaign |
| 500 designs | 5-8h | ~$70 | Large campaign |
Per-design: ~30-60s for typical binder.
Verify
find output -name "*.cif" | wc -l # Should match num_samples
Troubleshooting
Verify config first: Always run boltzgen check config.yaml before running the full pipeline
Slow generation: Use fewer designs for initial testing, then scale up
OOM errors: Use A100-80GB or reduce --num-designs
Wrong binding site: Residue indices use label_seq_id (1-indexed), check in Molstar viewer
Error interpretation
| Error | Cause | Fix |
|-------|-------|-----|
| RuntimeError: CUDA out of memory | Large design or long protein | Use A100-80GB or reduce designs |
| FileNotFoundError: *.cif | Target file not found | File paths are relative to YAML location |
| ValueError: invalid chain | Chain not in target | Verify chain IDs with Molstar or PyMOL |
| modal: command not found | Modal CLI not installed | Run pip install modal && modal setup |
Next: Validate with boltz or chai → protein-qc for filtering.
Other skills for the same job
different authors, same section of the catalogue
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
How to use it
Take biotender-max/boltzgen from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.