How do I install the Boltz skill?

Run npx skillmds add majiayu000/boltz 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 Boltz safe to use?

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

Which AI agents work with Boltz?

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 Boltz free to use?

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

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Boltz by majiayu000 — Agent Skill

Name: Boltz
Rating: 4.2 (5 reviews)
Author: majiayu000

Boltz

Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.

majiayu000 09fdf91 2 files · 5.2 KB Updated Sep 9, 2026 567 repo stars

File contents

Boltz Structure Prediction

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

cd biomodals
modal run modal_boltz.py \
  --input-faa complex.fasta \
  --out-dir predictions/

GPU: L40S (48GB) | Timeout: 1800s default

Option 2: Local installation

pip install boltz

boltz predict \
  --fasta complex.fasta \
  --output predictions/

Key parameters

Parameter	Default	Range	Description
`--recycling_steps`	3	1-10	Recycling iterations
`--sampling_steps`	200	50-500	Diffusion steps
`--use_msa_server`	true	bool	Use MSA server

FASTA Format

>protein_A
MKTAYIAKQRQISFVK...
>protein_B
MVLSPADKTNVKAAWG...

Output format

predictions/
├── model_0.cif       # Best model (CIF format)
├── confidence.json   # pLDDT, pTM, ipTM
└── pae.npy          # PAE matrix

Note: Boltz outputs CIF format. Convert to PDB if needed:

from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("model", "model_0.cif")
io = PDBIO()
io.set_structure(structure)
io.save("model_0.pdb")

Comparison

Feature	Boltz-1	Boltz-2	AF2-Multimer
MSA-free mode	Yes	Yes	No
Diffusion	Yes	Yes	No
Speed	Fast	Faster	Slower
Open source	Yes	Yes	Yes

Sample output

Successful run

$ boltz predict --fasta complex.fasta --output predictions/
[INFO] Loading Boltz-1 weights...
[INFO] Predicting structure...
[INFO] Saved model to predictions/model_0.cif

predictions/confidence.json:
{
  "ptm": 0.78,
  "iptm": 0.65,
  "plddt": 0.81
}

What good output looks like:

pTM: > 0.7 (confident global structure)
ipTM: > 0.5 (confident interface)
pLDDT: > 0.7 (confident per-residue)
CIF file: ~100-500 KB for typical complex

Decision tree

Should I use Boltz?
│
├─ What are you predicting?
│  ├─ Protein-protein complex → Boltz ✓ or Chai or ColabFold
│  ├─ Protein + ligand → Boltz ✓ or Chai
│  └─ Single protein → Use ESMFold (faster)
│
├─ Need MSA?
│  ├─ No / want speed → Boltz ✓
│  └─ Yes / maximum accuracy → ColabFold
│
└─ Why Boltz over Chai?
   ├─ Open weights preference → Boltz ✓
   ├─ Boltz-2 speed → Boltz ✓
   └─ DNA/RNA support → Consider Chai

Typical performance

Campaign Size	Time (L40S)	Cost (Modal)	Notes
100 complexes	30-45 min	~$8	Standard validation
500 complexes	2-3h	~$35	Large campaign
1000 complexes	4-6h	~$70	Comprehensive

Per-complex: ~15-30s for typical binder-target complex.

Verify

find predictions -name "*.cif" | wc -l  # Should match input count

Troubleshooting

Low confidence: Increase recycling_steps OOM errors: Use MSA-free mode or A100-80GB Slow prediction: Reduce sampling_steps

Error interpretation

Error	Cause	Fix
`RuntimeError: CUDA out of memory`	Complex too large	Use `--use_msa_server false` or larger GPU
`KeyError: 'iptm'`	Single chain only	Ensure FASTA has 2+ chains
`FileNotFoundError: weights`	Missing model	Run `boltz download` first
`ValueError: invalid residue`	Non-standard AA	Check for modified residues in sequence

Boltz-1 vs Boltz-2

Aspect	Boltz-1	Boltz-2
Speed	Fast	~2x faster
Accuracy	Good	Improved
Ligands	Basic	Better support
Release	2024	Late 2024

Next: protein-qc for filtering and ranking.

---name: boltzdescription: Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.license: MIT--- # Boltz Structure Prediction ## 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 ```bash cd biomodals modal run modal_boltz.py \ --input-faa complex.fasta \ --out-dir predictions/ ``` **GPU**: L40S (48GB) | **Timeout**: 1800s default ### Option 2: Local installation ```bash pip install boltz boltz predict \ --fasta complex.fasta \ --output predictions/ ``` ## Key parameters | Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | `--recycling_steps` | 3 | 1-10 | Recycling iterations | | `--sampling_steps` | 200 | 50-500 | Diffusion steps | | `--use_msa_server` | true | bool | Use MSA server | ## FASTA Format ``` >protein_A MKTAYIAKQRQISFVK... >protein_B MVLSPADKTNVKAAWG... ``` ## Output format ``` predictions/ ├── model_0.cif # Best model (CIF format) ├── confidence.json # pLDDT, pTM, ipTM └── pae.npy # PAE matrix ``` **Note**: Boltz outputs CIF format. Convert to PDB if needed: ```python from Bio.PDB import MMCIFParser, PDBIO parser = MMCIFParser() structure = parser.get_structure("model", "model_0.cif") io = PDBIO() io.set_structure(structure) io.save("model_0.pdb") ``` ## Comparison | Feature | Boltz-1 | Boltz-2 | AF2-Multimer | |---------|---------|---------|--------------| | MSA-free mode | Yes | Yes | No | | Diffusion | Yes | Yes | No | | Speed | Fast | Faster | Slower | | Open source | Yes | Yes | Yes | ## Sample output ### Successful run ``` $ boltz predict --fasta complex.fasta --output predictions/ [INFO] Loading Boltz-1 weights... [INFO] Predicting structure... [INFO] Saved model to predictions/model_0.cif predictions/confidence.json: { "ptm": 0.78, "iptm": 0.65, "plddt": 0.81 } ``` **What good output looks like:** - pTM: > 0.7 (confident global structure) - ipTM: > 0.5 (confident interface) - pLDDT: > 0.7 (confident per-residue) - CIF file: ~100-500 KB for typical complex ## Decision tree ``` Should I use Boltz? │ ├─ What are you predicting? │ ├─ Protein-protein complex → Boltz ✓ or Chai or ColabFold │ ├─ Protein + ligand → Boltz ✓ or Chai │ └─ Single protein → Use ESMFold (faster) │ ├─ Need MSA? │ ├─ No / want speed → Boltz ✓ │ └─ Yes / maximum accuracy → ColabFold │ └─ Why Boltz over Chai? ├─ Open weights preference → Boltz ✓ ├─ Boltz-2 speed → Boltz ✓ └─ DNA/RNA support → Consider Chai ``` ## Typical performance | Campaign Size | Time (L40S) | Cost (Modal) | Notes | |---------------|-------------|--------------|-------| | 100 complexes | 30-45 min | ~$8 | Standard validation | | 500 complexes | 2-3h | ~$35 | Large campaign | | 1000 complexes | 4-6h | ~$70 | Comprehensive | **Per-complex**: ~15-30s for typical binder-target complex. --- ## Verify ```bash find predictions -name "*.cif" | wc -l # Should match input count ``` --- ## Troubleshooting **Low confidence**: Increase recycling_steps **OOM errors**: Use MSA-free mode or A100-80GB **Slow prediction**: Reduce sampling_steps ### Error interpretation | Error | Cause | Fix | |-------|-------|-----| | `RuntimeError: CUDA out of memory` | Complex too large | Use `--use_msa_server false` or larger GPU | | `KeyError: 'iptm'` | Single chain only | Ensure FASTA has 2+ chains | | `FileNotFoundError: weights` | Missing model | Run `boltz download` first | | `ValueError: invalid residue` | Non-standard AA | Check for modified residues in sequence | ### Boltz-1 vs Boltz-2 | Aspect | Boltz-1 | Boltz-2 | |--------|---------|---------| | Speed | Fast | ~2x faster | | Accuracy | Good | Improved | | Ligands | Basic | Better support | | Release | 2024 | Late 2024 | --- **Next**: `protein-qc` for filtering and ranking.

Frequently asked questions