datamol

datamol is a skill for Claude Code, Codex from Microck/ordinary-claude-skills. It costs 67 tokens per session (5,102 once invoked), scanned A, original, no licence file.

A Python interface for RDKit, a chemistry toolkit used to represent and analyse molecules. It simplifies tasks such as reading SMILES, calculating descriptors, comparing structures, and generating 3D conformers.

In plain words
What is it for?
Use it to standardise molecules, calculate fingerprints and descriptors, cluster compounds, create 3D structures, and process molecules in parallel.
Why use it?
It reduces the amount of chemistry-specific code needed for standard drug-discovery workflows.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to standardise molecules, calculate fingerprints and descriptors, cluster compounds, create 3D structures, and process molecules in parallel.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/microck/ordinary-claude-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 Microck/ordinary-claude-skills --skill datamol
Clone the repo
git clone --depth 1 https://github.com/Microck/ordinary-claude-skills

Made for: Claude Code, Codex.

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/microck/ordinary-claude-skills/datamol/github.svg)](https://agentmods.dev/skills/microck/ordinary-claude-skills/datamol)
Your own site
<a href="https://agentmods.dev/skills/microck/ordinary-claude-skills/datamol"><img src="https://agentmods.dev/badge/skills/microck/ordinary-claude-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/microck/ordinary-claude-skills/datamol"><img src="https://agentmods.dev/badge/skills/microck/ordinary-claude-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 5,102 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 unknown No closer match found 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 $0.05102
Opus 5 $0.00034 $0.02551
Sonnet 5 $0.00013 $0.01020
Haiku 4.5 $0.00007 $0.00510

Measured 9d ago against content hash 521f036df70f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, 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 9d 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.

skills_all/claude-scientific-skills/scientific-skills/datamol/SKILL.md · 701 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

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. 9d ago First seen · 701 lines · 67 tokens per session scan A 521f036df70f

Subscribe to this mod's changes

datamol is a skill published in the GitHub repository Microck/ordinary-claude-skills (394 stars, last pushed 6d ago), with no licence file. It adds 67 tokens to every session and 5,102 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Related

Other skills, from other repositories

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

K-Dense-AI/scientific-agent-skills · 56 tokens

cobrapy

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

K-Dense-AI/scientific-agent-skills · 38 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use…

K-Dense-AI/scientific-agent-skills · 73 tokens

pennylane

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…

K-Dense-AI/scientific-agent-skills · 98 tokens

python-to-dafny-translator

Translate Python programs into equivalent Dafny code, preserving program semantics and ensuring the generated code is well-typed, executable, and verifiable. Use when the user asks to convert Python code to Dafny, port Python programs to Dafny, add formal verification to Python code, or create Dafny versions of Python…

ArabelaTso/Skills-4-SE · 74 tokens