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.
Profile →npx skills add xuzhougeng/wisp-science --skill boltz --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 # complex.yaml version: 1 sequences: - protein: id: A sequence: MVTPEGNVSLVDESLLVGVTDEDRAVRS... # target - protein: id: B sequence: AIQRTPKIQVYSRHPAENG... # binder - ligand: id: L smiles: 'N[C@@H](Cc1ccc(O)cc1)C(=O)O' # or ccd: SAH ``` ```bash boltz predict complex.yaml \ --use_msa_server --out_dir out/ --recycling_steps 3 --diffusion_samples 5 ``` Each protein chain needs an MSA; without one the run exits before the model loads. `--use_msa_server` queries `api.colabfold.com` (expect a 30–90 s pause per chain) and is the right default unless you already have an `.a3m
- Running it
- Affinity head
- msa: empty is an accuracy hit, not a memory save
- Missing fast kernels are slow, not fatal
- Wisp execution
- Errors worth recognizing
boltz predict complex.yaml \
What does the boltz skill do?
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.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill boltz --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.