DiffDock Molecular Docking
K-Dense-AI/scientific-agent-skills
Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.
Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab).
Install Chai1
$ npx skills add JimLiu/science-skills --skill chai1 -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install JimLiu/science-skills chai1 --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/chai1 .claude/skills/chai1 && rm -rf skills-srcUse ~/.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/
Install the "chai1" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/chai1 into .claude/skills/chai1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai1", 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 install https://github.com/JimLiu/science-skills/tree/main/skills/chai1Type 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.
$ npx skills add JimLiu/science-skills --skill chai1 -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install JimLiu/science-skills chai1 --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/chai1 .agents/skills/chai1 && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "chai1" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/chai1 into .agents/skills/chai1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai1", 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.
$ npx skills add JimLiu/science-skills --skill chai1 -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install JimLiu/science-skills chai1 --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/chai1 .cursor/skills/chai1 && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "chai1" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/chai1 into .cursor/skills/chai1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai1", 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 https://github.com/JimLiu/science-skills.git --path skills/chai1--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add JimLiu/science-skills --skill chai1 -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install JimLiu/science-skills chai1 --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/chai1 .gemini/skills/chai1 && rm -rf skills-srcUse ~/.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/
Install the "chai1" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/chai1 into .gemini/skills/chai1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai1", 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.
$ gh skill install JimLiu/science-skills chai1Installs 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).
$ npx skills add JimLiu/science-skills --skill chai1 -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/chai1 .github/skills/chai1 && rm -rf skills-srcUse ~/.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/
Install the "chai1" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/chai1 into .github/skills/chai1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai1", 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.
$ npx skills add JimLiu/science-skills --skill chai1 -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install JimLiu/science-skills chai1 --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/chai1 .opencode/skills/chai1 && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "chai1" agent skill from https://github.com/JimLiu/science-skills/tree/main/skills/chai1 into .opencode/skills/chai1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chai1", 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
chai1- GitHub stars
- 227
- Used in
- 4 other repos
- Token cost
- ~1.2k tokens
- SKILL.md length
- 407 words
- Files
- 1
- Skills in repo
- 27
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab).
- Tasks that involve Protein structure and design
- SKILL.md covers Running it, Unset CHAI_DOWNLOADS_DIR fails…, No-MSA mode still loads a 3… and Errors worth recognizing
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Tasks that involve Drug discovery and cheminformatics
What it does
Chai1 is an agent skill from JimLiu/science-skills. 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.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Protein structure and design and Drug discovery and cheminformatics. It works with Python, GitHub and AlphaFold. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Protein structure and design
- Tasks that involve Drug discovery and cheminformatics
Example prompts
- “/chai1”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit fb309c3. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Chai1 loads about 1.2k tokens when it runs. Until then it costs about 101 tokens; SKILL.md has 407 words of instructions outside code blocks.
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 JimLiu/science-skills at commit fb309c3, republished under its Apache-2.0 licence (© JimLiu). 407 words, ~1,151 tokens.
Download SKILL.mdSave it as
.claude/skills/chai1/SKILL.md (or your agent's skills folder).- name
- chai1
- description
- 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.
- license
- Apache-2.0
- category
- biomodels
- requirements
- gpu
- metadata.display-name
- Chai-1
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
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}; ligand records carry a SMILES string as
the sequence body, and modified residues are written inline as
...AAK(SEP)AAG.... From the shell the same job is chai-lab fold complex.fasta out/ --use-msa-server. Without --use-msa-server (or
use_msa_server=True in Python) the model runs on ESM embeddings alone, which
is faster but typically a few ipTM points behind the MSA-backed run.
output_dir receives pred.model_idx_{0..4}.cif plus a matching
scores.model_idx_{N}.npz per sample with aggregate_score, ptm, iptm,
per_chain_ptm, and clash flags. Rank by aggregate_score; treat iptm >
0.5 as a soft pass for an interface. The function refuses a non-empty output_dir, so
clear or rotate it between calls.
Unset CHAI_DOWNLOADS_DIR fails mid-run with PermissionError on a read-only image
Chai downloads ~5 GB on the first inference call (not at install time),
including its own traced ESM2-3B for the embedding path. If
CHAI_DOWNLOADS_DIR is unset, the default is inside site-packages: on a
read-only image that fails with a confusing PermissionError mid-run, and on
a writable one it silently re-downloads ~5 GB into the container on every cold
start. Export the variable to a persisted volume so the download happens once.
Show full SKILL.md (131 more words)Show less
No-MSA mode still loads a 3 B-parameter ESM — same VRAM, not less
use_esm_embeddings=True without an MSA still loads a 3-billion-parameter
language model into GPU memory alongside the trunk; it removes the MSA-server
round-trip, not the VRAM cost. If you OOM, drop num_diffn_timesteps or fold
fewer chains per call rather than expecting the no-MSA mode to fit a smaller
card.
Errors worth recognizing
| You see | It means / do this |
|---|---|
PermissionError under site-packages/chai_lab/... | CHAI_DOWNLOADS_DIR not set on a read-only image — export it to a writable path or the pre-populated mount. |
RuntimeError: CUDA out of memory during ESM embedding | The traced ESM2-3B is loading alongside the trunk — use an 80 GB tier or split chains across calls. |
Next: filter survivors on confidence/clash metrics or feed them back to
proteinmpnn for the next design round.
© JimLiu, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/chai1 of JimLiu/science-skills.
Open the folder on GitHubat commit fb309c3
Used in 4 other repositories
We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in JimLiu/science-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Chai1 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Chai1 this skillJimLiu/science-skills | 227 | 4 repos | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3k | Automated safety check: Notes | MIT | |
| Biopipelineslocbp-uzh/biopipelines | 109 | — | ~2.4k | Automated safety check: Pass | MIT | |
| Ggetdavila7/claude-code-templates | 32k | 10 repos | ~6.3k | Automated safety check: Pass | MIT | |
| Alphafold3VectorSpaceLab/AREX-Skill | 330 | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Ggetaipoch/medical-research-skills | 2k | — | ~816 | Automated safety check: Pass | MIT |
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locbp-uzh/biopipelines
Design and run computational protein and ligand workflows on a GPU: binder and enzyme design, de novo backbone generation, inverse folding and sequence redesign, structure prediction, protein-ligand…
Gget
davila7/claude-code-templates
CLI/Python toolkit for rapid bioinformatics queries. An agent skill from davila7/claude-code-templates.
Alphafold3
VectorSpaceLab/AREX-Skill
A skill your agent uses for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection.
Gget
aipoch/medical-research-skills
Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or…
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Python API for RCSB PDB 3D structures (search, fetch coordinates, metadata).
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Related
Categories
Questions about Chai1
What does Chai1 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). Chai1 is an agent skill from JimLiu/science-skills.com/chaidiscovery/chai-lab).
When should I use Chai1?
Chai1 fits situations like: tasks that involve Protein structure and design; tasks that involve Drug discovery and cheminformatics.
How do I install Chai1 in Claude Code?
Run `npx skills add JimLiu/science-skills --skill chai1 -a claude-code`. Or copy the skill folder (skills/chai1 in JimLiu/science-skills) into .claude/skills/chai1 in your project. Claude Code loads it when a task matches its description.
How do I install Chai1 in Codex?
Run `npx skills add JimLiu/science-skills --skill chai1 -a codex`. Or copy the skill folder (skills/chai1 in JimLiu/science-skills) into .agents/skills/chai1 in your project. Codex loads it when a task matches its description.
Can I use Chai1 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 JimLiu/science-skills --skill chai1 -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chai1, .gemini/skills/chai1, .github/skills/chai1 and .opencode/skills/chai1 in your project.
What does Chai1 need to run?
SKILL.md names no scripts, command-line tools or credentials: Chai1 is instructions for the agent only. Our summary lists: Python 3.
Does Chai1 access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Chai1 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 Chai1 use?
Chai1 is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Chai1 use?
About 1.2k tokens (SKILL.md is roughly 4.6k 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 Chai1?
Skills that share tags, products or a category with Chai1: DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars), Biopipelines (locbp-uzh/biopipelines, 109 stars), Gget (davila7/claude-code-templates, 32k stars) and Alphafold3 (VectorSpaceLab/AREX-Skill, 330 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Chai1?
JimLiu (a GitHub user) maintains it in JimLiu/science-skills, which has 227 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on July 1, 2026.
Source: JimLiu/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.