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 | $0.04190 |
| Opus 5.5 | $0.00048 | $0.01676 |
| Sonnet 5.5 | $0.00024 | $0.00838 |
| Haiku 4.5 | $0.00012 | $0.00419 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 350 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
When to Use This Skill
Use this skill when the user wants to:
- Load, standardize, and de-duplicate molecule tables (CSV/SDF/Excel/Parquet, local or cloud) with little boilerplate
- Compute datamol's descriptor set, fingerprints, and Tanimoto distance matrices for a compound set
- Cluster, pick diverse subsets, extract Murcko scaffolds, or fragment (BRICS/RECAP) a library
- Generate and cluster 3D conformers, or render aligned molecule grids for SAR review
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| Low-level control: custom sanitization flags, atom-mapped reaction details, specialised fingerprint/descriptor algorithms | alterlab-rdkit |
| ML-ready feature matrices, pretrained embeddings, or featurizer benchmarking | alterlab-molfeat |
| Drug-likeness rule sets, PAINS / structural-alert and complexity filtering | alterlab-medchem |
| Downloading curated ADMET/DTI benchmark datasets with scaffold or cold splits | alterlab-pytdc |
Installation and Setup
Guide users to install datamol:
uv pip install datamol
Examples here are verified against datamol 0.13.0 (current as of 2026-09; requires Python ≥ 3.11 and pulls in RDKit). 0.13 fixed the misspelled heterocycle descriptors: the compute_many_descriptors keys are now n_aromatic_heterocycles / n_aliphatic_heterocycles / n_saturated_heterocycles (formerly ..._heterocyles; the old function names survive only as deprecated aliases). Pin 'datamol>=0.13' if you depend on those keys.
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.
- 1mo ago Changed · +32 lines 5847a93ddabb
- 1mo 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 (78 stars, last pushed 2d ago), licensed MIT. It adds 120 tokens to every session and 4,190 once invoked, about $0.0005 per session on Opus 5.5. 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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