Agent skill

Unimol

by jinzhezenggroup in jinzhezenggroup/computational-chemistry-agent-skills

A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in…

LGPL-3.0-or-laterAuto-check passedAI & LLM Engineering

Install Unimol

skills CLI

$ npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a claude-code

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

GitHub CLI

$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills unimol --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/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/molecular-representation/unimol .claude/skills/unimol && 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 "unimol" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/molecular-representation/unimol into .claude/skills/unimol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unimol", 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/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/molecular-representation/unimol

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 jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a codex

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

GitHub CLI

$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills unimol --agent codex

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

Manual copy

$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/molecular-representation/unimol .agents/skills/unimol && 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 "unimol" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/molecular-representation/unimol into .agents/skills/unimol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unimol", 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 jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a cursor

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

GitHub CLI

$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills unimol --agent cursor

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

Manual copy

$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/molecular-representation/unimol .cursor/skills/unimol && 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 "unimol" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/molecular-representation/unimol into .cursor/skills/unimol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unimol", 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/jinzhezenggroup/computational-chemistry-agent-skills.git --path molecular-representation/unimol

--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 jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a gemini-cli

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

GitHub CLI

$ gh skill install jinzhezenggroup/computational-chemistry-agent-skills unimol --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/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/molecular-representation/unimol .gemini/skills/unimol && 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 "unimol" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/molecular-representation/unimol into .gemini/skills/unimol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unimol", 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 jinzhezenggroup/computational-chemistry-agent-skills unimol

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 jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a github-copilot

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

Manual copy

$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/molecular-representation/unimol .github/skills/unimol && 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 "unimol" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/molecular-representation/unimol into .github/skills/unimol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unimol", 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 jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -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 jinzhezenggroup/computational-chemistry-agent-skills unimol --agent opencode

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

Manual copy

$ git clone --depth 1 https://github.com/jinzhezenggroup/computational-chemistry-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/molecular-representation/unimol .opencode/skills/unimol && 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 "unimol" agent skill from https://github.com/jinzhezenggroup/computational-chemistry-agent-skills/tree/master/molecular-representation/unimol into .opencode/skills/unimol/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unimol", 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: unimol
GitHub stars: 148
Token cost: ~1.5k tokens
SKILL.md length: 299 words
Files: 2 (incl. scripts)
Skills in repo: 62
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: LGPL-3.0-or-later

At a glance

A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in…

Works in 3 steps: Extract molecular representations… → Train a property model (classification /… → Predict properties to .csv
You need to generate molecular embeddings
SKILL.md covers Quick Start, Core Tasks, Agent Checklist and References
Runs Python scripts from its folder; calls uv

What it does

Unimol is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation. USE WHEN you need to generate molecular embeddings, train machine learning models for chemical properties, or run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/unimol_helper.py`). Compatibility notes: Requires uv. Dependencies (unimol-tools, rdkit, etc.) are handled automatically via inline script metadata in unimolhelper.py.

It sits in AI & LLM Engineering, covering Drug discovery and cheminformatics, Embeddings and CSV and tabular files. It works with RDKit. The repository describes itself as: Agent skills to run computational-chemistry tasks, used in OpenClaw. The licence is LGPL-3.0-or-later.

When your agent uses it

You need to generate molecular embeddings
Train machine learning models for chemical properties
Run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework

Example prompts

“/unimol”

Requirements

Python 3
Compatibility (from SKILL.md): Requires uv. Dependencies (unimol-tools, rdkit, etc.) are handled automatically via inline script metadata in unimol_helper.py.

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Extract molecular representations (embedding) to .npy
Train a property model (classification / regression / multilabel_*)
Predict properties to .csv

What it can do on your machine

Read from SKILL.md and the folder at commit 5c19e75. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- github.com
- rdkit.org
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.
Compatibility
Requires uv. Dependencies (unimol-tools, rdkit, etc.) are handled automatically via inline script metadata in unimol_helper.py.
From compatibility in the SKILL.md frontmatter.

Context cost

Unimol loads about 1.5k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 299 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~104
When it runs · the whole SKILL.md, loaded when a task matches: ~1.5k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from jinzhezenggroup/computational-chemistry-agent-skills at commit 5c19e75, republished under its LGPL-3.0-or-later licence (© jinzhezenggroup). 299 words, ~1,471 tokens.

Download SKILL.mdSave it as .claude/skills/unimol/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: unimol
description: A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation. USE WHEN you need to generate molecular embeddings, train machine learning models for chemical properties, or run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework.
compatibility: Requires uv. Dependencies (unimol-tools, rdkit, etc.) are handled automatically via inline script metadata in unimol_helper.py.
license: LGPL-3.0-or-later
metadata.author: luzitian
metadata.version: 1.0
metadata.repository: https://github.com/deepmodeling/Uni-Mol

Uni-Mol

This skill provides practical command patterns for Uni-Mol molecular representation / training / prediction using the standardized CLI wrapper: <skill_path>/scripts/unimol_helper.py.

Key behaviors (important for Agents):

The script prints environment detection (Python/Torch/CUDA) by default.
Bad/illegal SMILES are skipped and logged to *.skipped.csv (no crash).
Each run ends by printing absolute output paths like:
- [RESULT] repr_npy=/abs/path.npy
- [RESULT] model_dir=/abs/model_dir
- [RESULT] pred_csv=/abs/pred.csv

Quick Start

Check CLI help:

bash

uv run python <skill_path>/scripts/unimol_helper.py --help

Check subcommand help:

bash

uv run python <skill_path>/scripts/unimol_helper.py repr --help
uv run python <skill_path>/scripts/unimol_helper.py train --help
uv run python <skill_path>/scripts/unimol_helper.py predict --help

Disable environment printing (optional):

bash

uv run python <skill_path>/scripts/unimol_helper.py --no-env repr --smiles "CCO" --output out.npy

Core Tasks

1) Extract molecular representations (embedding) to .npy

Single SMILES:

bash

uv run python <skill_path>/scripts/unimol_helper.py repr \
    --smiles "CCO" \
    --output /tmp/ccO.repr.npy

From CSV (default SMILES column is smiles):

bash

uv run python <skill_path>/scripts/unimol_helper.py repr \
    --file data.csv \
    --smiles-col smiles \
    --output data.repr.npy

From SMI:

bash

uv run python <skill_path>/scripts/unimol_helper.py repr \
    --file molecules.smi \
    --output molecules.repr.npy

Force CPU / GPU:

bash

# Force CPU
uv run python <skill_path>/scripts/unimol_helper.py repr --smiles "CCO" --no-gpu --output out.npy

# Force GPU (will warn & fall back if CUDA is unavailable)
uv run python <skill_path>/scripts/unimol_helper.py repr --smiles "CCO" --use-gpu --output out.npy

2) Train a property model (classification / regression / multilabel_*)

Regression training (CSV must contain smiles and target columns):

bash

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task regression \
    --input train.csv \
    --smiles-col smiles \
    --target-col target \
    --epochs 50 \
    --output ./model_reg

Classification training:

bash

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task classification \
    --input train.csv \
    --smiles-col smiles \
    --target-col target \
    --epochs 50 \
    --output ./model_cls

Multilabel regression training (explicit multi-target columns):

bash

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task multilabel_regression \
    --input train.csv \
    --smiles-col smiles \
    --target-cols target_0,target_1,target_2 \
    --epochs 50 \
    --output ./model_mreg

Multilabel classification training:

bash

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task multilabel_classification \
    --input train.csv \
    --smiles-col smiles \
    --target-cols y_cls_0,y_cls_1,y_cls_2 \
    --epochs 50 \
    --output ./model_mcls

Target recognition for training:

Single-task (classification / regression): use --target-col (default target).
Multilabel tasks: prefer --target-cols (comma-separated).
If --target-cols is omitted for multilabel tasks, the helper auto-detects columns named target or prefixed with target_ (case-insensitive).

Force CPU:

bash

uv run python <skill_path>/scripts/unimol_helper.py train \
    --task regression \
    --input train.csv \
    --epochs 50 \
    --output ./model_cpu \
    --no-cuda

3) Predict properties to .csv

Predict from CSV:

bash

uv run python <skill_path>/scripts/unimol_helper.py predict \
    --model ./model_reg \
    --input test.csv \
    --smiles-col smiles \
    --output pred.csv

Predict from SMI:

bash

uv run python <skill_path>/scripts/unimol_helper.py predict \
    --model ./model_reg \
    --input test.smi \
    --output pred.csv

Notes:

Output CSV contains the input rows (for valid SMILES) plus pred / pred_* columns.
If there are bad SMILES, they are skipped and saved to pred.csv.skipped.csv (or your --error-log path).

Agent Checklist

When using this skill for users:

Confirm input format:
- .csv requires a SMILES column (default smiles)
- .smi uses the first token of each line as SMILES
Quote SMILES containing special characters (brackets/parentheses):
- Example: --smiles "[C]([H])([H])[H]"
For CSV workflows, verify column names:
- repr: --smiles-col
- train: --smiles-col and --target-col / --target-cols
- predict: --smiles-col
Watch for skipped SMILES:
- Check *.skipped.csv and decide whether to fix or permanently drop them
Always capture absolute output paths:
- Look for [RESULT] ...=/abs/path in stdout
If debugging is needed, enable full traceback:
- UNIMOL_HELPER_TRACE=1 uv run python <skill_path>/scripts/unimol_helper.py ...

References

Uni-Mol project: https://github.com/fanxiaoyu0/Uni-Mol
RDKit: https://www.rdkit.org/

Files

SKILL.md and 1 other file (scripts) in molecular-representation/unimol of jinzhezenggroup/computational-chemistry-agent-skills.

SKILL.md
scripts/unimol_helper.py

Open the folder on GitHubat commit 5c19e75

Compare with similar skills

Unimol 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.

Unimol compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Unimol this skilljinzhezenggroup/computational-chemistry-agent-skills	148	—	~1.5k	Automated safety check: Pass	LGPL-3.0-or-later	2 days ago
Kermt InferNVIDIA/skills	3.6k	1 repos	~1.5k	Automated safety check: Pass	Apache-2.0	yesterday
Nvmolkit UsageNVIDIA-BioNeMo/bionemo-agent-toolkit	479	—	~4.4k	Automated safety check: Pass	Apache-2.0	yesterday
MolfeatK-Dense-AI/scientific-agent-skills	48k	1 repos	~2.4k	Automated safety check: Notes	Apache-2.0	6 days ago
Bio Qsar ModelingGPTomics/bioSkills	1.2k	2 repos	~5.5k	Automated safety check: Pass	MIT	1 mo ago
Molfeat Molecular Featurizationjaechang-hits/SciAgent-Skills	374	1 repos	~4.3k	Automated safety check: Pass	Apache-2.0	12 days ago

Similar skills

Kermt Infer
NVIDIA/skills
Official
Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV.
3.6k GitHub starsUsed in 1 repo~1.5k tokens
Research & ScienceAuto-check passed
Nvmolkit Usage
NVIDIA-BioNeMo/bionemo-agent-toolkit
Write code that calls the installed nvMolKit Python API for GPU-accelerated, batched RDKit-style operations - Morgan fingerprints, Tanimoto/cosine similarity, ETKDG conformer embedding, MMFF/UFF…
479 GitHub stars~4.4k tokensUpdated yesterday
Research & ScienceAuto-check passed
Molfeat
K-Dense-AI/scientific-agent-skills
Featurizes small molecules with Molfeat for QSAR/QSPR, chemical similarity, virtual screening, and molecular ML.
48k GitHub starsUsed in 1 repo~2.4k tokens
Research & ScienceAuto-check: notes
Bio Qsar Modeling
GPTomics/bioSkills
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain…
1.2k GitHub starsUsed in 2 repos~5.5k tokens
Data & AnalyticsAuto-check passed
Molfeat Molecular Featurization
jaechang-hits/SciAgent-Skills
Molecular featurization hub (100+ featurizers) for ML. An agent skill from jaechang-hits/SciAgent-Skills.
374 GitHub starsUsed in 1 repo~4.3k tokens
Research & ScienceAuto-check passed
Bio Molecular Descriptors
FreedomIntelligence/OpenClaw-Medical-Skills
Calculates molecular descriptors and fingerprints using RDKit.
3.1k GitHub stars~1.6k tokensUpdated 2 mo ago
Research & ScienceAuto-check passed

More from jinzhezenggroup/computational-chemistry-agent-skills

All 62 skills in this repo

Quantum ESPRESSO DFT Task Builder
jinzhezenggroup/computational-chemistry-agent-skills
Turns a user-supplied atomic structure and DFT settings into a runnable Quantum ESPRESSO input file, stopping short of submitting the job.
148 GitHub stars~1.8k tokensUpdated 2 days ago
Auto-check passed
DP-GEN Simplify Workflow
jinzhezenggroup/computational-chemistry-agent-skills
Prepares, validates and runs DP-GEN simplify jobs that thin out repeated or redundant DeepMD datasets, generating param.json and machine.json for local or scheduler runs.
148 GitHub stars~2.7k tokensUpdated 2 days ago
Auto-check passed
LAMMPS with DeePMD-kit
jinzhezenggroup/computational-chemistry-agent-skills
Prepares and runs molecular dynamics simulations in LAMMPS with a DeePMD machine-learning potential, writing the input script and choosing NVE, NVT or NPT.
148 GitHub stars~2.8k tokensUpdated 2 days ago
Auto-check passed
LAMMPS ReaxFF Setup
jinzhezenggroup/computational-chemistry-agent-skills
Prepares and explains LAMMPS input scripts for reactive molecular dynamics with the ReaxFF potential, including charge equilibration and ensemble choice.
148 GitHub stars~1.8k tokensUpdated 2 days ago
Auto-check passed
RDKit Conformer Generator
jinzhezenggroup/computational-chemistry-agent-skills
Generates 3D molecular conformers from SMILES strings or files with RDKit, keeps the lowest-energy one per molecule, and falls back to 2D coordinates when embedding fails.
148 GitHub stars~2.4k tokensUpdated 2 days ago
Auto-check passed
RDKit Descriptors and Fingerprints
jinzhezenggroup/computational-chemistry-agent-skills
Computes RDKit physicochemical descriptors and molecular fingerprints from SMILES through a uv-run CLI script that skips and logs invalid molecules.
148 GitHub stars~2.3k tokensUpdated 2 days ago
Auto-check passed

Works with

RDKit

Questions about Unimol

What does Unimol do?

A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in…. Unimol is an agent skill from jinzhezenggroup/computational-chemistry-agent-skills. A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation.

When should I use Unimol?

Unimol fits situations like: you need to generate molecular embeddings; train machine learning models for chemical properties; run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework.

How do I install Unimol in Claude Code?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a claude-code`. Or copy the skill folder (molecular-representation/unimol in jinzhezenggroup/computational-chemistry-agent-skills) into .claude/skills/unimol in your project. Claude Code loads it when a task matches its description.

How do I install Unimol in Codex?

Run `npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a codex`. Or copy the skill folder (molecular-representation/unimol in jinzhezenggroup/computational-chemistry-agent-skills) into .agents/skills/unimol in your project. Codex loads it when a task matches its description.

Can I use Unimol 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 jinzhezenggroup/computational-chemistry-agent-skills --skill unimol -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/unimol, .gemini/skills/unimol, .github/skills/unimol and .opencode/skills/unimol in your project.

What does Unimol need to run?

Going by SKILL.md and its folder, Unimol needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires uv. Dependencies (unimol-tools, rdkit, etc.) are handled automatically via inline script metadata in unimol_helper.py..

Does Unimol access the network?

SKILL.md names 2 domains. As links in the text: github.com and rdkit.org. This is read from the text; nothing was executed.

Is Unimol 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Unimol use?

Unimol is published under the LGPL-3.0-or-later licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Unimol use?

About 1.5k tokens (SKILL.md is roughly 5.9k 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 Unimol?

Skills that share tags, products or a category with Unimol: Kermt Infer (NVIDIA/skills, 3.6k stars), Nvmolkit Usage (NVIDIA-BioNeMo/bionemo-agent-toolkit, 479 stars), Molfeat (K-Dense-AI/scientific-agent-skills, 48k stars) and Bio Qsar Modeling (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Unimol?

jinzhezenggroup (a GitHub organization) maintains it in jinzhezenggroup/computational-chemistry-agent-skills, which has 148 GitHub stars. The repository holds 62 skills in this directory. The repository was last updated on October 9, 2026.

Source: jinzhezenggroup/computational-chemistry-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Unimol

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Compatibility

Context cost

Safety

SKILL.md

Uni-Mol

Quick Start

Core Tasks

1) Extract molecular representations (embedding) to .npy

2) Train a property model (classification / regression / multilabel_*)

3) Predict properties to .csv

Agent Checklist

References

Files

Compare with similar skills

Similar skills

More from jinzhezenggroup/computational-chemistry-agent-skills

Related

Questions about Unimol