From the author
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.
Install
npx skills add jinzhezenggroup/computational-chemistry-agent-skills --skill rdkit-repr -a claude-code
Installs to .claude/skills/rdkit-repr/ · Each command fetches the skill straight from GitHub with the open skills CLI.
No download: this skill’s licence (LGPL-3.0) does not let us redistribute its files. The install commands fetch them from GitHub instead.
Want your agent to suggest skills while you type? See how to use skills from your IDE.
Safety checks on SKILL.md
- Ships scripts we have not reviewedLow
Why 76
aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.
Similar skills
Closest to this one by what they do and when they are used.
- rdkit-cheminformaticsjaechang-hits/SciAgent-SkillsCheminformatics toolkit for molecular analysis and virtual screening: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints (Morgan/ECFP, MACCS), Tanimoto similarity, SMARTS substructure filtering, Lipinski drug-likeness, reaction enumeration, 2D/3D coordinates. For simpler API use datamol; use RDKit for fine-grained sanitization, custom fingerprints, or SMARTS/reaction control.Science★ 383
- datamol-cheminformaticsjaechang-hits/SciAgent-SkillsPythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.Science★ 383
- rdkitforyourhealth111-pixel/Vibe-SkillsCore cheminformatics toolkit for SMILES/SDF/InChI parsing, descriptors (MW, LogP, TPSA), fingerprints, ECFP/Morgan fingerprints, substructure search, 2D/3D generation, similarity, reactions, and datamol-style molecule standardization when no separate wrapper skill is routed.Science★ 3.8K
- datamolK-Dense-AI/scientific-agent-skillsPythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.Design★ 54.5K
- unimoljinzhezenggroup/computational-chemistry-agent-skillsA 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.Data★ 154
- rdkit-confjinzhezenggroup/computational-chemistry-agent-skillsA standardized CLI wrapper for RDKit 3D/2D conformer generation that samples multiple conformers per molecule (ETKDGv3, default 10), optimizes each with a force field (MMFF94s/UFF), keeps the lowest-energy conformer, automatically falls back to 2D layout on total embedding failure with a printed warning, and writes results to SDF or XYZ format. USE WHEN you need to generate 3D (or 2D fallback) molecular geometries from SMILES datasets (.csv/.smi) for downstream tasks such as docking, visualization, or 3D-descriptor computation.Data★ 154
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in