Agent skill

Chai

A skill your agent uses when predicting molecular structures (proteins, nucleic acids, small molecules, and complexes) with the Chai-1 foundation model via local inference or the Chai Discovery API.

Apache-2.0Auto-check passed

Install Chai

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill chai -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw chai --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/chai .claude/skills/chai && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "chai" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/chai into .claude/skills/chai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/lamm-mit/scienceclaw/tree/main/skills/chai

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill chai -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw chai --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/chai .agents/skills/chai && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "chai" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/chai into .agents/skills/chai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill chai -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw chai --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/chai .cursor/skills/chai && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "chai" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/chai into .cursor/skills/chai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/lamm-mit/scienceclaw.git --path skills/chai

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill chai -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw chai --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/chai .gemini/skills/chai && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "chai" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/chai into .gemini/skills/chai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw chai

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill chai -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/chai .github/skills/chai && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "chai" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/chai into .github/skills/chai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill chai -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw chai --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/chai .opencode/skills/chai && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "chai" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/chai into .opencode/skills/chai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: chai
GitHub stars: 244
Token cost: ~1.1k tokens
SKILL.md length: 193 words
Files: 1
Skills in repo: 85
Repo updated: Aug 21, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

A skill your agent uses when predicting molecular structures (proteins, nucleic acids, small molecules, and complexes) with the Chai-1 foundation model via local inference or the Chai Discovery API.

Predicting molecular structures (proteins
SKILL.md covers Requirements, Installation, Local Usage and Chai Discovery API (No Local…, plus 5 more sections
Calls pip; reaches api.chaidiscovery.com; needs CHAI_API_KEY
Small molecules

What it does

Chai is an agent skill from lamm-mit/scienceclaw. Use when predicting molecular structures (proteins, nucleic acids, small molecules, and complexes) with the Chai-1 foundation model via local inference or the Chai Discovery API.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is Apache-2.0.

When your agent uses it

Predicting molecular structures (proteins
Small molecules
Complexes) with the Chai-1 foundation model via local inference
The Chai Discovery API

Example prompts

“/chai”

Requirements

Python 3
A credential in CHAI_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. 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
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:
- api.chaidiscovery.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- CHAI_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Chai loads about 1.1k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 193 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~46
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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 lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 193 words, ~1,053 tokens.

Download SKILL.mdSave it as .claude/skills/chai/SKILL.md (or your agent's skills folder).

name: chai
description: Use when predicting molecular structures (proteins, nucleic acids, small molecules, and complexes) with the Chai-1 foundation model via local inference or the Chai Discovery API.

Chai-1 Structure Prediction

Use when the user needs to predict molecular structures — proteins, nucleic acids, small molecules, or multi-chain complexes — using the Chai-1 foundation model. Supports both local GPU inference and the Chai Discovery API for remote execution.

Requirements

Python 3.10+
16 GB GPU VRAM (A10G sufficient; A100 for large complexes)
Or: use Chai Discovery API (no local GPU needed)

Installation

bash

pip install chai-lab

Local Usage

Python API

python

from chai_lab.chai1 import run_inference
import torch
from pathlib import Path

# Single protein
results = run_inference(
    fasta_file=Path("input.fasta"),
    output_dir=Path("results/"),
    num_trunk_recycles=3,
    num_diffn_timesteps=200,
    seed=42,
    device=torch.device("cuda:0"),
    use_esm_embeddings=True,
)

# Access results
for i, result in enumerate(results):
    print(f"Model {i}: pTM={result.ptm:.3f}, ipTM={result.iptm:.3f}")

FASTA Input Format

fasta

# Single chain
>protein|A
MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT

# Complex: separate chains with different headers
>protein|A
EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYYMSWVRQAP
>protein|B
MTEYKLVVVGAGGVGKSALTIQLIQNHFVDE

# With small molecule (SMILES)
>protein|A
MTEYKLVVVGAGGVGKS...
>ligand|L
CC1=CC=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C

# RNA
>rna|R
GCGGAUUUAGCUCAGUUGGGAGAGCGCCAGACUGAAGAUCUGGAGGUCCUGUGUUCGAUCCACAGAAUUCGCACCA

Chai Discovery API (No Local GPU)

python

import requests

# Submit prediction job
response = requests.post(
    "https://api.chaidiscovery.com/v1/predictions",
    headers={"Authorization": f"Bearer {CHAI_API_KEY}"},
    json={
        "sequences": [
            {"type": "protein", "chain_id": "A", "sequence": "MTEYKLVV..."},
            {"type": "protein", "chain_id": "B", "sequence": "EVQLVES..."}
        ],
        "num_diffn_timesteps": 200,
        "num_trunk_recycles": 3,
    }
)
job_id = response.json()["job_id"]

# Poll for results
import time
while True:
    status = requests.get(
        f"https://api.chaidiscovery.com/v1/predictions/{job_id}",
        headers={"Authorization": f"Bearer {CHAI_API_KEY}"}
    ).json()
    if status["status"] == "completed":
        break
    time.sleep(30)

# Download structure
structure_url = status["results"]["structure_url"]

Output Files

File	Contents
`pred.model_idx_0.cif`	Top-ranked structure (CIF format)
`pred.model_idx_0.npz`	Confidence arrays (pLDDT, PAE, pDE)
`scores.json`	Aggregate scores per model

Parsing Confidence Scores

python

import numpy as np

data = np.load("pred.model_idx_0.npz")
plddt = data["plddt"]                    # Per-residue, shape (N,)
pae = data["pae"]                        # N×N matrix, Angstroms
pde = data.get("pde")                    # Predicted Distance Error

# Interface residues (chain A = target, chain B = binder)
chain_a_len = 150  # length of chain A
interface_pae = pae[:chain_a_len, chain_a_len:].mean()
print(f"Interface PAE: {interface_pae:.2f} Å (< 10 = good)")

Chai vs. Other Predictors

Feature	Chai-1	Boltz	AF2
Speed (complex)	Fast	Medium	Slow
Small molecules	✓	✓	✗
RNA/DNA	✓	✓	✗
API available	✓	✗	✗
Open weights	✓	✓	✓
GPU VRAM	16 GB	24 GB	32 GB

Quality Thresholds

Metric	Marginal	Good	Excellent
Mean pLDDT	<60	60–80	>80
ipTM (complex)	<0.5	0.5–0.75	>0.75
Interface PAE	>20 Å	10–20 Å	<10 Å

Use Cases

Fast validation: Predicts binder-target complexes quickly before committing to expensive MD simulations.
Ligand complexes: Predicts protein-small molecule binding poses from SMILES input.
Ensemble scoring: Generates multiple models and ranks them by ipTM for design selection.
Nucleic acid interactions: Predicts protein-DNA/RNA complex structures.

Files

Just SKILL.md in skills/chai of lamm-mit/scienceclaw.

Open the folder on GitHubat commit ab9aba1

Compare with similar skills

Chai next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Chai compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Chai this skilllamm-mit/scienceclaw	244	—	~1.1k	Automated safety check: Pass	Apache-2.0	1 mo ago
Bio Structural Biology Modern Structure PredictionFreedomIntelligence/OpenClaw-Medical-Skills	3.1k	1 repos	~2.5k	Automated safety check: Pass	None	2 mo ago
Bio Structural Biology Modern Structure PredictionGPTomics/bioSkills	1.2k	1 repos	~4.6k	Automated safety check: Pass	MIT	1 mo ago
Bio Structural Biology Alphafold PredictionsFreedomIntelligence/OpenClaw-Medical-Skills	3.1k	1 repos	~2.1k	Automated safety check: Pass	None	2 mo ago
Chai1 Structure Predictionmajiayu000/claude-skill-registry	666	1 repos	~1.7k	Automated safety check: Pass	MIT	today
Msa Structure Prediction PipelineNVIDIA/skills	3.5k	1 repos	~1.6k	Automated safety check: Notes	Apache-2.0	today

Similar skills

Bio Structural Biology Modern Structure Prediction
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Research & ScienceAuto-check passed
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Questions about Chai

What does Chai do?

A skill your agent uses when predicting molecular structures (proteins, nucleic acids, small molecules, and complexes) with the Chai-1 foundation model via local inference or the Chai Discovery API. Chai is an agent skill from lamm-mit/scienceclaw. Use when predicting molecular structures (proteins, nucleic acids, small molecules, and complexes) with the Chai-1 foundation model via local inference or the Chai Discovery API.

When should I use Chai?

Chai fits situations like: predicting molecular structures (proteins; small molecules; complexes) with the Chai-1 foundation model via local inference; the Chai Discovery API.

How do I install Chai in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill chai -a claude-code`. Or copy the skill folder (skills/chai in lamm-mit/scienceclaw) into .claude/skills/chai in your project. Claude Code loads it when a task matches its description.

How do I install Chai in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill chai -a codex`. Or copy the skill folder (skills/chai in lamm-mit/scienceclaw) into .agents/skills/chai in your project. Codex loads it when a task matches its description.

Can I use Chai in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lamm-mit/scienceclaw --skill chai -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chai, .gemini/skills/chai, .github/skills/chai and .opencode/skills/chai in your project.

What does Chai need to run?

Going by SKILL.md and its folder, Chai needs the command-line tools its instructions call (pip) and credentials named CHAI_API_KEY. Our summary lists: Python 3; A credential in CHAI_API_KEY.

Does Chai access the network?

SKILL.md names 1 domain. In commands or code: api.chaidiscovery.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Chai safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Chai use?

Chai is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Chai use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Chai?

Skills that share tags, products or a category with Chai: Bio Structural Biology Modern Structure Prediction (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Structural Biology Modern Structure Prediction (GPTomics/bioSkills, 1.2k stars), Bio Structural Biology Alphafold Predictions (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Chai1 Structure Prediction (majiayu000/claude-skill-registry, 666 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chai?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.

Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Chai

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Chai-1 Structure Prediction

Requirements

Installation

Local Usage

Python API

FASTA Input Format

Chai Discovery API (No Local GPU)

Output Files

Parsing Confidence Scores

Chai vs. Other Predictors

Quality Thresholds

Use Cases

Files

Compare with similar skills

Similar skills

More from lamm-mit/scienceclaw

Questions about Chai