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/naity/FM4Lifenpx agentmods add skills/naity/fm4life/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/naity/fm4life/boltz)<a href="https://agentmods.dev/skills/naity/fm4life/boltz"><img src="https://agentmods.dev/badge/skills/naity/fm4life/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/naity/fm4life/boltz"><img src="https://agentmods.dev/badge/skills/naity/fm4life/boltz.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 | $0.00133 | $0.02467 |
| Opus 5 | $0.00067 | $0.01234 |
| Sonnet 5 | $0.00027 | $0.00493 |
| Haiku 4.5 | $0.00013 | $0.00247 |
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 9d 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 — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Boltz-2: Biomolecular Structure and Binding Affinity Prediction
Overview
Boltz-2 is a biomolecular foundation model that jointly predicts complex 3D structure and binding affinity in a single inference pass. It is the first fully open-source model to approach AlphaFold 3-level structural accuracy while also providing quantitative affinity predictions 1000× faster than physics-based FEP methods.
| Capability | Boltz-2 | AlphaFold 3 |
|---|---|---|
| Proteins, RNA, DNA, ligands | ✓ | ✓ |
| Binding affinity prediction | ✓ | ✗ |
| Pocket/contact constraints | ✓ | ✗ |
| Cyclic peptides | ✓ | ✗ |
| License | MIT (commercial OK) | CC-BY-NC-SA 4.0 |
| Model weights | Freely available | Must apply to Google |
| Input format | YAML | JSON |
| Output format | mmCIF or PDB | mmCIF only |
Installation
pip install boltz[cuda] -U
For CPU-only (slow, for testing):
pip install boltz -U
Requirements: Python ≥ 3.10, < 3.13; NVIDIA GPU recommended.
Quick Start
# Predict structure (auto-generate MSA via server)
boltz predict input.yaml --use_msa_server
# Predict structure + binding affinity
boltz predict complex.yaml --use_msa_server --diffusion_samples 5
# Multiple samples for ensemble
boltz predict input.yaml --use_msa_server --diffusion_samples 5
Input Format (YAML)
Boltz-2 uses YAML input files. See references/input-format.md for the full schema.
Single protein
version: 1
sequences:
- protein:
id: A
sequence: MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGD
Protein–ligand complex
version: 1
sequences:
- protein:
id: A
sequence: MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGD
msa: ./msas/protein_a.a3m
- ligand:
id: B
ccd: ATP
- ligand:
id: C
ccd: MG
With binding affinity prediction
Add a properties block with one small molecule as the binder:
version: 1
sequences:
- protein:
id: A
sequence: MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGD
msa: ./msas/protein_a.a3m
- ligand:
id: B
smiles: "CC(=O)Nc1ccc(O)cc1"
properties:
- affinity:
binder: B
What ships with it
3 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.
- 9d ago First seen · 282 lines · 133 tokens per session scan A d3b082f2a001
boltz is a skill published in the GitHub repository naity/FM4Life (2 stars, last pushed 5mo ago), licensed MIT. It adds 133 tokens to every session and 2,467 once invoked, about $0.0007 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 2026-08-31.
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