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/adaptyvbio/protein-design-skillsnpx agentmods add skills/adaptyvbio/protein-design-skills/boltzWrote 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/boltz)<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/boltz"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/boltz/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/boltz"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/boltz.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 | $0.00104 | $0.01512 |
| Opus 5 | $0.00052 | $0.00756 |
| Sonnet 5 | $0.00021 | $0.00302 |
| Haiku 4.5 | $0.00010 | $0.00151 |
Grade A, and why
boltz 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 Getting started 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
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 · 201 lines · 104 tokens per session scan A 4cbaac8b6ea6
boltz is a skill published in the GitHub repository adaptyvbio/protein-design-skills (158 stars, last pushed 3mo ago), licensed MIT. It adds 104 tokens to every session and 1,512 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
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. 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…
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. 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…
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…
protenix
Structure prediction using Protenix, ByteDance's open-source PyTorch reproduction of AlphaFold 3. Use this skill when: (1) Predicting protein/DNA/RNA/ligand/ion complex structures, (2) Need AF3-level accuracy with open-source code, (3) MSA-free fast prediction (--no-use-msa), (4) Multi-seed ensemble predictions, (5)…
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…
alphafold
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction…