Is rdkit-repr safe to install?

SaferSkills independently audited rdkit-repr (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/rdkit-repr/SKILL.md · 1 file

molecular-representation/rdkit-repr/SKILL.md9.0 KB · Markdown

name: rdkit-repr

description: >

A standardized CLI wrapper for RDKit molecular featurization workflows that handles

physicochemical descriptor computation (outputs .csv) and molecular fingerprint extraction

(outputs .npy or .csv), with built-in SMILES validation.

USE WHEN you need to compute RDKit molecular descriptors or fingerprints from SMILES

datasets (.csv/.smi), or when you want to list all available descriptor names and presets.

compatibility: Requires uv. Dependencies (rdkit, pandas, numpy) are declared as PEP 723 inline script metadata and are installed automatically when the script is invoked with `uv run <script_path>` (do NOT use `uv run python <script_path>` -- that bypasses the inline metadata and will not install dependencies automatically).

metadata:

author: luzitian

version: '1.0'

repository: https://github.com/rdkit/rdkit

RDKit Molecular Featurization

This skill provides practical command patterns for RDKit descriptor and fingerprint extraction using the standardized CLI wrapper: <skill_path>/scripts/rdkit_helper.py.

Key behaviors (important for Agents):

The script prints environment detection (Python/RDKit/NumPy/Pandas) by default.
Bad/illegal SMILES are skipped and logged to *.skipped.csv (no crash).
Each run ends by printing absolute output paths like:
[RESULT] desc_csv=/abs/path.csv
[RESULT] fp_npy=/abs/path.npy
[RESULT] fp_csv=/abs/path.csv

Quick Start

Check CLI help:

uv run <skill_path>/scripts/rdkit_helper.py --help

Check subcommand help:

uv run <skill_path>/scripts/rdkit_helper.py desc --help
uv run <skill_path>/scripts/rdkit_helper.py fp --help
uv run <skill_path>/scripts/rdkit_helper.py list-desc --help

Disable environment printing (optional):

uv run <skill_path>/scripts/rdkit_helper.py --no-env desc --smiles "CCO" --output out.csv

Core Tasks

1) Compute physicochemical descriptors → .csv

Single SMILES (default preset: physchem, 25 descriptors):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --smiles "CCO" \
    --output /tmp/CCO.desc.csv

From CSV (default SMILES column is smiles):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv \
    --smiles-col smiles \
    --output data.desc.csv

From SMI:

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file molecules.smi \
    --output molecules.desc.csv

Choose a descriptor preset:

# Lipinski drug-likeness (6 descriptors: MolWt, MolLogP, NumHDonors, ...)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset lipinski --output data.lipinski.csv

# Extended physicochemical (25 descriptors, default)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset physchem --output data.physchem.csv

# Topological / graph indices (56 descriptors: BalabanJ, BertzCT, Chi*, PEOE_VSA*, ...)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset topological --output data.topo.csv

# All RDKit descriptors (~200 descriptors)
uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset all --output data.all_desc.csv

Select specific descriptors (overrides --preset):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv \
    --descriptors "MolWt,MolLogP,TPSA,NumHDonors,NumHAcceptors" \
    --output data.custom.csv

Suppress merging back original CSV columns (output only smiles + descriptors):

uv run <skill_path>/scripts/rdkit_helper.py desc \
    --file data.csv --preset physchem --no-merge --output data.desc_only.csv

______________________________________________________________________

2) Compute molecular fingerprints → .npy or .csv

Available fingerprint types:

Type	Description	Default bits
`morgan2`	Morgan circular FP radius 2 (ECFP4-like), bit vector	2048
`morgan3`	Morgan circular FP radius 3 (ECFP6-like), bit vector	2048
`morgan2_count`	Morgan radius-2 count vector	2048
`rdkit`	RDKit path-based FP, bit vector	2048
`maccs`	MACCS 167 structural keys (bit vector, `--nbits` ignored)	167
`topological`	Topological torsion FP (count vector, hashed to `--nbits`)	2048
`atompair`	Atom-pair FP (count vector, hashed to `--nbits`)	2048
`layered`	Layered substructure FP, bit vector	2048
`pattern`	SMARTS pattern FP, bit vector	2048

Single SMILES, output as NumPy array (.npy):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --smiles "CCO" \
    --type morgan2 \
    --output /tmp/CCO.morgan2.npy

From CSV, Morgan ECFP4 (2048 bits):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file data.csv \
    --smiles-col smiles \
    --type morgan2 \
    --nbits 2048 \
    --output data.morgan2.npy

From SMI, MACCS keys (always 167 bits):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file molecules.smi \
    --type maccs \
    --output molecules.maccs.npy

Output as CSV (smiles + bit_0 … bit_N-1 columns):

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file data.csv \
    --type rdkit \
    --nbits 1024 \
    --format csv \
    --output data.rdkfp.csv

Atom-pair fingerprint, 4096 bits:

uv run <skill_path>/scripts/rdkit_helper.py fp \
    --file data.csv \
    --type atompair \
    --nbits 4096 \
    --output data.atompair.npy

______________________________________________________________________

3) List available descriptors

List all descriptors and built-in presets:

uv run <skill_path>/scripts/rdkit_helper.py list-desc

List descriptors in a specific preset group:

uv run <skill_path>/scripts/rdkit_helper.py list-desc --group lipinski
uv run <skill_path>/scripts/rdkit_helper.py list-desc --group physchem
uv run <skill_path>/scripts/rdkit_helper.py list-desc --group topological
uv run <skill_path>/scripts/rdkit_helper.py list-desc --group all

______________________________________________________________________

Descriptor Presets Reference

Preset	Count	Typical Use
`lipinski`	6	Quick drug-likeness screening (Ro5 filter)
`physchem`	25	General ML features: MW, logP, TPSA, ring counts, charge stats, …
`topological`	56	Graph/topology indices: Balaban J, Kappa, Chi, PEOE_VSA, EState_VSA, …
`all`	~200	Full RDKit descriptor set (includes fragment counts, MQN, etc.)

______________________________________________________________________

Output Format Notes

`desc` output (CSV):

Columns: smiles, then one column per descriptor.
When --file is a .csv and --no-merge is not set, original CSV columns are appended.
Rows only contain valid SMILES (invalid ones are logged to *.skipped.csv).

`fp` output:

.npy (default): NumPy array of shape (N_valid, nbits), dtype uint8 (bit) or int32 (count).
.csv: smiles column followed by bit_0 … bit_{nbits-1} columns.
MACCS keys always produce 167 bits regardless of --nbits.

______________________________________________________________________

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 shell characters (brackets/parentheses):

Example: --smiles "[C@@H](O)(F)Cl"

For CSV workflows, verify column names:

desc: --smiles-col
fp: --smiles-col

Choose the right preset or fingerprint type for the downstream task:

Drug screening / Ro5: --preset lipinski
General ML featurization: --preset physchem or --type morgan2
Structural similarity search: --type morgan2 or --type rdkit
Substructure matching: --type maccs or --type pattern

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:

RDKIT_HELPER_TRACE=1 uv run <skill_path>/scripts/rdkit_helper.py ...

References

RDKit documentation: https://www.rdkit.org/docs/
RDKit descriptor list: https://www.rdkit.org/docs/GettingStartedInPython.html#list-of-available-descriptors
RDKit fingerprint guide: https://www.rdkit.org/docs/GettingStartedInPython.html#fingerprinting-and-molecular-similarity

rdkit-repr— agent skill

Is rdkit-repr safe to install?

RDKit Molecular Featurization

Quick Start

Core Tasks

1) Compute physicochemical descriptors → .csv

2) Compute molecular fingerprints → .npy or .csv

3) List available descriptors

Descriptor Presets Reference

Output Format Notes

Agent Checklist

References

Audit the pieces. Scan the whole. Decide.