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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-datamolgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/alterlab-datamol)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-datamol"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-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/alterlab-ieu/alterlab-academic-skills/alterlab-datamol"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-datamol.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00120 | $0.03482 |
| Opus 5 | $0.00060 | $0.01741 |
| Sonnet 5 | $0.00024 | $0.00696 |
| Haiku 4.5 | $0.00012 | $0.00348 |
Grade A, and why
alterlab-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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 318 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.
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
Examples here are verified against datamol 0.12.x (pulls in RDKit automatically). The descriptor key names below are stable in this line; pin if you depend on them: uv pip install 'datamol>=0.12,<0.13'.
Import convention:
import datamol as dm
Core Workflows
Each subsection below shows the primary call pattern. Full API signatures, parameters, and secondary examples live in the per-module reference files cited under each; complete multi-step pipelines live in references/workflow_recipes.md.
1. Basic Molecule Handling
import datamol as dm
# Parse SMILES (returns None on failure)
mol = dm.to_mol("CCO") # Ethanol
mols = [dm.to_mol(smi) for smi in ["CCO", "c1ccccc1", "CC(=O)O"]]
if dm.to_mol("invalid_smiles") is None:
print("Failed to parse SMILES")
# Export to common formats (canonical + isomeric by default)
smiles = dm.to_smiles(mol) # keeps stereochemistry
flat = dm.to_smiles(mol, isomeric=False) # drops stereochemistry
inchi = dm.to_inchi(mol)
inchikey = dm.to_inchikey(mol)
selfies = dm.to_selfies(mol)
# Standardize user-provided molecules (recommended for datasets)
mol = dm.sanitize_mol(mol)
mol = dm.standardize_mol(mol, disconnect_metals=True, normalize=True, reionize=True)
clean_smiles = dm.standardize_smiles(smiles)
What ships with it
8 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.
- 10d ago First seen · 318 lines · 120 tokens per session scan A b638449b51c7
alterlab-datamol is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 5d ago), licensed MIT. It adds 120 tokens to every session and 3,482 once invoked, about $0.0006 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 2026-08-30.
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