datamol

datamol is a skill for Claude Code from dralkh/seerai. It costs 67 tokens per session (5,597 once invoked), scanned A, a copy of datamol, MIT.

A Python library that provides a simpler interface to RDKit, a toolkit for working with chemical molecules. It supports tasks such as reading molecular formats, standardizing structures, calculating descriptors, generating fingerprints and 3D shapes, clustering, and batch processing.

In plain words
What is it for?
Use it for molecular structure conversion and cleanup, chemical-property calculations, similarity fingerprints, 3D conformers, clustering, scaffold analysis, reaction processing, visualization, and parallel batch work.
Why use it?
It reduces the amount of RDKit-specific code needed for common drug-discovery work while returning RDKit molecule objects for compatibility. Sensible defaults can make routine cheminformatics tasks easier to set up.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/dralkh/seerai/datamol
Any agent
npx skills add dralkh/seerai --skill datamol
Clone the repo
git clone --depth 1 https://github.com/dralkh/seerai

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for datamol

README.md
[![agentmods](https://agentmods.dev/badge/skills/dralkh/seerai/datamol.svg)](https://agentmods.dev/skills/dralkh/seerai/datamol)
Your own site
<a href="https://agentmods.dev/skills/dralkh/seerai/datamol"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/datamol.svg" alt="Measured on agentmods" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,597 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 84% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00067 $0.05597
Opus 5 $0.00034 $0.02799
Sonnet 5 $0.00013 $0.01119
Haiku 4.5 $0.00007 $0.00560

Measured 6d ago against content hash 66e491e7fb82, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

datamol scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

84% identical to datamol — 64 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/datamol/SKILL.md · 724 lines

How it starts

The opening of the file, as written. The whole thing — 724 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Datamol Cheminformatics Skill

Overview

Datamol is a Python library that provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. Simplify complex molecular operations with sensible defaults, efficient parallelization, and modern I/O capabilities. All molecular objects are native rdkit.Chem.Mol instances, ensuring full compatibility with the RDKit ecosystem.

Version note: Examples target datamol 0.12.x (PyPI stable: 0.12.5, June 2024). Since 0.10.0, modules are lazy-loaded by default (set DATAMOL_DISABLE_LAZY_LOADING=1 to disable). Since 0.12.2, RDKit is a direct PyPI dependency of datamol. Fingerprints use RDKit's rdFingerprintGenerator API (0.12.5+).

Key capabilities:

  • Molecular format conversion (SMILES, SELFIES, InChI)
  • Structure standardization and sanitization
  • Molecular descriptors and fingerprints
  • 3D conformer generation and analysis
  • Clustering and diversity selection
  • Scaffold and fragment analysis
  • Chemical reaction application
  • Visualization and alignment
  • Batch processing with parallelization
  • Cloud storage support via fsspec

Installation and Setup

Guide users to install datamol:

uv pip install datamol

RDKit is installed automatically with datamol. For remote file paths (S3, GCS, HTTP), install the matching fsspec backend:

uv pip install s3fs   # AWS S3
uv pip install gcsfs  # Google Cloud Storage

Import convention:

import datamol as dm

Core Workflows

1. Basic Molecule Handling

Creating molecules from SMILES:

import datamol as dm

# Single molecule
mol = dm.to_mol("CCO")  # Ethanol

# From list of SMILES
smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"]
mols = [dm.to_mol(smi) for smi in smiles_list]

# Error handling
mol = dm.to_mol("invalid_smiles")  # Returns None
if mol is None:
    print("Failed to parse SMILES")

Converting molecules to SMILES:

# Canonical SMILES
smiles = dm.to_smiles(mol)

# Isomeric SMILES (includes stereochemistry)
smiles = dm.to_smiles(mol, isomeric=True)

# Other formats
inchi = dm.to_inchi(mol)
inchikey = dm.to_inchikey(mol)
selfies = dm.to_selfies(mol)

Read the full file on GitHub · 724 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 724 lines · 67 tokens per session scan A 66e491e7fb82

Subscribe to this mod's changes

datamol is a skill published in the GitHub repository dralkh/seerai (76 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 5,597 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to datamol, differing in 64 lines, and is treated as a copy.

Related

Other skills, from other repositories

sn-search-academic

用于学术调研、论文精读、相关工作梳理、百科知识查询和引用链追溯。.

OpenSenseNova/SenseNova-Skills · 31 tokens

paper-revision-author

Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.

opensquilla/opensquilla · 24 tokens

imaging-data-commons

Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

synthetic-sciences/openscience · 62 tokens

clinical-decision-support

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading…

synthetic-sciences/openscience · 97 tokens

immunology-assays

Computational analysis of immunology experimental data. ATAC-seq differential accessibility, immune cell tracking from microscopy, ELISA data processing with 4-parameter logistic fitting, immunohistochemistry quantification, antibody titer analysis, and cell cycle phase duration estimation. For flow cytometry use…

synthetic-sciences/openscience · 79 tokens

molecular-cloning

Molecular cloning simulation and design. PCR amplicon prediction, restriction enzyme digestion, Golden Gate and Gibson assembly simulation, primer design, CRISPR sgRNA design, and plasmid annotation. For protein-level sequence analysis use biopython or esm; for database lookups use gene-database or ensembl-database.

synthetic-sciences/openscience · 70 tokens