datamol

datamol is a skill for Claude Code from yanjumlinnb-boop/scientific-agent-skills. It costs 67 tokens per session (5,597 once invoked), scanned A, a copy of datamol, MIT.

A Python interface for RDKit, a chemistry toolkit used to read, compare, describe, and visualize molecules. It provides simpler defaults while returning RDKit molecule objects for compatibility with that toolkit.

In plain words
What is it for?
Use it to convert molecular formats, clean structures, calculate descriptors and fingerprints, create 3D conformers, group similar molecules, analyze fragments, apply reactions, visualize molecules, and process batches.
Why use it?
It reduces the amount of setup and specialized code needed for common molecular data tasks. This makes routine chemical analysis easier to add to Python workflows.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to convert molecular formats, clean structures, calculate descriptors and fingerprints, create 3D conformers, group similar molecules, analyze fragments, apply reactions, visualize molecules, and process batches.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yanjumlinnb-boop/scientific-agent-skills/datamol
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.

Any agent
npx skills add yanjumlinnb-boop/scientific-agent-skills --skill datamol
Clone the repo
git clone --depth 1 https://github.com/yanjumlinnb-boop/scientific-agent-skills

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/yanjumlinnb-boop/scientific-agent-skills/datamol/github.svg)](https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/datamol)
Your own site
<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/datamol"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/datamol/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for datamol

Your own site · 80×15
<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/datamol"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/datamol.svg" alt="Reviewed on agentmods" width="80" 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. A grade says what 26 rules found in the file — not that it is safe.
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 11d ago against content hash 66e491e7fb82, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, 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 11d 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. 11d 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 yanjumlinnb-boop/scientific-agent-skills (2 stars, last pushed 2mo 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.

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