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.
npx skills add yanjumlinnb-boop/scientific-agent-skills --skill datamolgit clone --depth 1 https://github.com/yanjumlinnb-boop/scientific-agent-skillsWrote 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.
[](https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/datamol)<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.
<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>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.05597 |
| Opus 5.5 | $0.00027 | $0.02239 |
| Sonnet 5.5 | $0.00013 | $0.01119 |
| Haiku 4.5 | $0.00007 | $0.00560 |
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 1mo 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.
This is a copy
84% identical to datamol — 70 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.
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)
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.
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.
- 1mo ago First seen · 724 lines · 67 tokens per session scan A 66e491e7fb82
datamol is a skill published in the GitHub repository yanjumlinnb-boop/scientific-agent-skills (4 stars, last pushed 4mo ago), licensed MIT. It adds 67 tokens to every session and 5,597 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to datamol, differing in 70 lines, and is treated as a copy.
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