Is unimol safe to install?

SaferSkills independently audited unimol (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

molecular-representation/unimol/SKILL.md · 1 file

molecular-representation/unimol/SKILL.md5.7 KB · Markdown

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

version: '1.0'

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:

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

Check subcommand help:

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):

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:

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

From CSV (default SMILES column is smiles):

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

From SMI:

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

Force CPU / GPU:

# 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):

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:

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):

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:

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:

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:

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:

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/

unimol— agent skill

Is unimol safe to install?

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

Audit the pieces. Scan the whole. Decide.