Chai

What it does

When your agent uses it

• Predicting protein-protein complex structures
• Validating designed binders
• Predicting protein-ligand complexes
• Using the Chai API for high-throughput prediction

Example prompts

• “/chai”

Requirements

• Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 59dd633. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • pip
  • modal
  • python
  • git

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Chai loads about 1.5k tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 265 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~108

When it runs · the whole SKILL.md, loaded when a task matches

~1.5k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 265 words, ~1,501 tokens.

name

chai

description

Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.

license

MIT

category

design-tools

tags

structure-prediction, validation, foundation-model

biomodals_script

modal_chai1.py

cd biomodals
modal run modal_chai1.py \
  --input-faa complex.fasta \
  --out-dir predictions/

pip install chai_lab

python -c "
import chai_lab
from chai_lab.chai1 import run_inference

# Run prediction
run_inference(
    fasta_file='complex.fasta',
    output_dir='predictions/',
    num_trunk_recycles=3
)
"

git clone https://github.com/chaidiscovery/chai-lab.git
cd chai-lab
pip install -e .

chai-lab predict \
  --fasta complex.fasta \
  --output predictions/

>binder
MKTAYIAKQRQISFVKSHFSRQLE...
>target
MVLSPADKTNVKAAWGKVGAHAGE...

>protein
MKTAYIAKQRQISFVKSHFSRQLE...
>ligand|smiles
CCO

>protein
MKTAYIAKQRQISFVKSHFSRQLE...
>dna
ATCGATCGATCG

predictions/
├── pred.model_idx_0.cif    # Best model (CIF format)
├── pred.model_idx_1.cif    # Second model
├── scores.json             # Confidence scores
├── pae.npy                 # PAE matrix
└── plddt.npy               # pLDDT values

from Bio.PDB import MMCIFParser, PDBIO
parser = MMCIFParser()
structure = parser.get_structure("pred", "pred.model_idx_0.cif")
io = PDBIO()
io.set_structure(structure)
io.save("pred.model_idx_0.pdb")

import numpy as np
import json

# Load scores
with open('predictions/scores.json') as f:
    scores = json.load(f)

plddt = np.load('predictions/plddt.npy')
pae = np.load('predictions/pae.npy')

print(f"pLDDT: {plddt.mean():.3f}")
print(f"pTM: {scores['ptm']:.3f}")
print(f"ipTM: {scores.get('iptm', 'N/A')}")

# Predict complex with Chai
chai-lab predict --fasta binder_target.fasta --output val/

# Check ipTM > 0.5
scores = json.load(open('val/scores.json'))
if scores['iptm'] > 0.5:
    print("Design passes validation")

# FASTA with SMILES
fasta = """
>protein
MKTA...
>ligand|smiles
CCO
"""

# Chai handles both protein and small molecules

# Multiple sequences
for fasta in sequences/*.fasta; do
    chai-lab predict \
        --fasta "$fasta" \
        --output "predictions/$(basename $fasta .fasta)"
done

$ chai-lab predict --fasta complex.fasta --output predictions/
[INFO] Loading Chai-1 model...
[INFO] Running inference...
[INFO] Saved 5 models to predictions/

predictions/scores.json:
{
  "ptm": 0.82,
  "iptm": 0.71,
  "ranking_score": 0.76
}

Should I use Chai?
│
├─ What are you predicting?
│  ├─ Protein-protein complex → Chai ✓ or ColabFold
│  ├─ Protein + small molecule → Chai ✓
│  ├─ Protein + DNA/RNA → Chai ✓
│  └─ Single protein only → Use ESMFold (faster)
│
├─ Need MSA?
│  ├─ No / want speed → Chai ✓
│  └─ Yes / want accuracy → ColabFold
│
└─ Priority?
   ├─ Highest accuracy → ColabFold with MSA
   ├─ Speed / no MSA → Chai ✓
   └─ Ligand binding → Chai ✓

find predictions -name "*.cif" | wc -l  # Should match input count

© adaptyvbio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/chai of adaptyvbio/protein-design-skills.

• SKILL.md
• references/api-reference.md

Open the folder on GitHubat commit 59dd633

Used in 4 other repositories

We found 10 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.

• majiayu000/claude-skill-registryskills/bash/chaiskills/bash/chai-r2ascension-skill-repo+5 more
• NeverSight/learn-skills.devdata/skills-md/adaptyvbio/protein-design-skills/chai
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/chai
• BioTender-max/awesome-bio-agent-skillsskills/adaptyv/chai