chai1
Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.
Profile →npx skills add xuzhougeng/wisp-science --skill chai1 --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.
# Chai-1 Chai-1 is an all-atom diffusion co-folder in the same family as Boltz-2 and AlphaFold3: a multi-entity FASTA in, mmCIF plus pTM/ipTM/pLDDT out, with protein, RNA, DNA, and SMILES-ligand chains all first-class. It and `boltz` cover the same surface; running both and keeping designs that pass either is a common consensus filter, and Chai's Python entry point makes it the easier of the two to embed in a loop. Code and weights are Apache-2.0 — commercial use including drug discovery is explicitly permitted (github.com/chaidiscovery/chai-lab). ## Running it ```python from pathlib import Path from chai_lab.chai1 import run_inference Path("complex.fasta").write_text(""" >protein|name=target MVTPEGNVSLVDESLLVGVTDEDRAVRS... >protein|name=binder AIQRTPKIQVYSRHPAENG... >ligand|name=cofactor CCCCCCCCCCCCCC(=O)O """.strip()) candidates = run_inference( fasta_file=Path("complex.fasta"), output_dir=Path("out/"), num_trunk_recycles=3, num_diffn_timesteps=200, seed=42, device="cuda:0", use_esm_embeddings=True, ) print([rd.aggregate_score.item() for rd in candidates.ranking_data]) ``` The FASTA header is `>{entity_type}|name={id}` with `entity_type` ∈ {`protein`, `rna`, `dna`, `ligand`}; li
- Running it
- Unset CHAIDOWNLOADSDIR fails mid-run with PermissionError on a read-only image
- No-MSA mode still loads a 3 B-parameter ESM — same VRAM, not less
- Wisp execution
- Errors worth recognizing
What does the chai1 skill do?
Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill chai1 --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.