Chai Lab Repo Skill

Use this skill when a task mentions Chai Lab, Chai-1, chai_lab, chai-lab fold, molecular structure prediction, protein/ligand/DNA/RNA/glycan complexes, MSA/template setup, or Chai restraint files.

Start Here

1. Check package availability with scripts/check_chai_lab_environment.py --help, then run the checks that are safe for the target machine.
2. Read references/troubleshooting.md if install/import, CUDA, model download, network server, or output-directory failures are likely.
3. Read references/repo-provenance.md before refreshing this skill against a newer checkout.
4. Route to one focused sub-skill rather than trying to keep all Chai details in the root context.

Sub-Skill Routing

• sub-skills/cli-inference/SKILL.md: run or template chai-lab fold, call chai_lab.chai1.run_inference, inspect StructureCandidates, tune sample/recycle/device options, and debug inference runtime failures.
• sub-skills/input-data-formats/SKILL.md: author and validate Chai FASTA records for proteins, ligands, DNA, RNA, modified residues, glycan headers, entity names, and chain-name mode decisions.
• sub-skills/msa-templates/SKILL.md: prepare .aligned.pqt files, convert A3M to Chai MSA parquet, use ColabFold server flags, prepare template m8 inputs, and stage existing ColabFold outputs.
• sub-skills/restraints-glycans/SKILL.md: write and validate contact, pocket, covalent, and glycan restraint CSVs, including atom notation and chain-name consistency.

Common Public Setup

Chai Lab publishes the Python distribution chai_lab and import package chai_lab. Prefer a pinned public release when reproducibility matters:

pip install chai_lab==0.6.1
python - <<'PY'
import chai_lab
from chai_lab.chai1 import run_inference
print(chai_lab.__version__, run_inference)
PY

For unreleased changes, install from the public Git repository instead of mixing source files into a skill workflow:

pip install git+https://github.com/chaidiscovery/chai-lab.git

Chai-1 inference is intended for Linux, Python >=3.10, and a CUDA GPU with bfloat16 support. The package can be imported and many input validators can run without launching a fold, but practical folding should be treated as GPU-backed and potentially memory-intensive.

High-Level Workflows

• Basic CLI fold: validate the FASTA with sub-skills/input-data-formats/scripts/validate_chai_fasta.py, choose a fresh output directory, then build chai-lab fold input.fasta output_dir with options from sub-skills/cli-inference/SKILL.md.
• Python inference: use sub-skills/cli-inference/scripts/write_inference_template.py to generate a safe script template, then add MSA/template/restraint options from sibling sub-skills.
• MSA/template-backed fold: validate .aligned.pqt files and template m8 inputs through sub-skills/msa-templates/SKILL.md before passing --msa-directory, --use-msa-server, --use-templates-server, or --template-hits-path.
• Restrained fold: validate contact, pocket, covalent, and glycan CSVs through sub-skills/restraints-glycans/SKILL.md, then pass the CSV as constraint_path or --constraint-path.

Shared Checks

Run the root helper for lightweight environment and backend visibility checks:

python scripts/check_chai_lab_environment.py --json
python scripts/check_chai_lab_environment.py --require-cuda --check-cli

This helper imports Chai Lab, checks the CLI, reports PyTorch/CUDA visibility when PyTorch is installed, and prints CHAI_DOWNLOADS_DIR status. It does not download model weights or run inference.

Important Boundaries

• Do not run full Chai inference as a cheap smoke test; use CLI --help, parser validators, and tiny data-format checks first.
• Do not tell future agents to open original repository examples, tests, or scripts. The useful details are distilled into this skill's references and bundled scripts.
• Keep local machine paths, private environment prefixes, cache paths, and artifact directories out of public instructions.
• Treat network-backed MSA/template generation and first-time model downloads as explicit side effects that may need user approval in restricted environments.
• When a repository checkout has changed, compare it with references/repo-provenance.md and refresh this skill instead of relying on stale API details.

Reference Map

• references/troubleshooting.md: cross-cutting install/import, CUDA, download, output-directory, and network-service failure modes.
• references/repo-provenance.md: source snapshot and evidence paths used to create this skill.
• references/repo-routing-metadata.json: structured import metadata for repo-skills-router.