Individual skill
boltz
Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.
resources/skills/boltz/SKILL.md
Source commit 357d1a980056e191f380d0e01ec4fbc7cf529d1b
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 3, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 5,493
- Repository last updated
- Nov 8, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-for-science Repository topic
- local-first Repository topic
- reproducible-research Repository topic
- open-science Repository topic
- ai-research Repository topic
- model-agnostic Repository topic
- scientific-ai Repository topic
- desktop-app Repository topic
- ai-workbench Repository topic
- open-source Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
.catalog_stampasset · 13 bytesSKILL.mdcore · 5,687 bytesSKILL.mdcore · 5,687 bytesSKILL.mdcore · 5,687 bytes
Read excerpt
# Boltz-2
Boltz-2 is the open-weights diffusion co-folder closest in surface to
AlphaFold3: a YAML describing protein, DNA, RNA, and ligand chains in, mmCIF
plus pTM/ipTM/pLDDT confidences out, with an optional small-molecule affinity
head. Among our four co-fold skills it is the default for binder-validation
campaigns — fully open MIT weights and the fastest sampler; pick `chai1` when
you want a second independent model for consensus, `openfold3` when AF3-faithful
settings matter, and `esmfold2` when you can live without an MSA. Code and
weights are MIT (PyPI `boltz`, github.com/jwohlwend/boltz).
## Running it
```yaml