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 SFETNI/Deep-Matter-Chem-Skills --skill molecular-featurizationgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-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/sfetni/deep-matter-chem-skills/molecular-featurization)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/molecular-featurization"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/molecular-featurization/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/sfetni/deep-matter-chem-skills/molecular-featurization"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/molecular-featurization.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 | $0.00005 | $0.07438 |
| Opus 5 | $0.00003 | $0.03719 |
| Sonnet 5 | $0.00001 | $0.01488 |
| Haiku 4.5 | $0.00001 | $0.00744 |
Grade A, and why
molecular-featurization 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 12d 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 — 605 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular Featurization
Description
This skill covers molecular featurization for chemistry and molecular machine learning: parsing SMILES, SMARTS, InChI, SDF, and MOL2-like records; RDKit sanitization and standardization; fingerprints such as Morgan/ECFP, MACCS, atom-pair, torsion, pharmacophore, and topological fingerprints; physicochemical descriptors; 2D versus 3D descriptors; conformer-dependent features; descriptor scaling, missing values, and leakage-aware feature selection. Invoke this skill before training molecular property models, molecular GNNs, small-data surrogates, or screening workflows that depend on chemically meaningful molecular inputs.
Domain Context
Molecular featurization converts a chemical representation into numerical inputs for machine learning. That conversion is not neutral. A SMILES string encodes molecular graph connectivity and stereochemistry if specified, but not a conformer ensemble, protonation microstate, solvent, assay conditions, or experimental uncertainty. A 3D SDF file includes coordinates, but those coordinates may be generated, force-field-minimized, crystallographic, docked, or experimentally derived. Different coordinate sources can produce different descriptors for the same molecule.
The first scientific decision is molecular identity. Salts, mixtures, counterions, tautomers, zwitterions, protonation states, isotopes, stereoisomers, and resonance forms may all map to different graphs or descriptors. Standardization choices must match the target property. For a pH-dependent solubility or binding assay, neutralizing every molecule may remove signal; for a gas-phase quantum-chemistry target, keeping a counterion may be wrong. [EXPERT REVIEW NEEDED]
Fingerprints and descriptors encode different assumptions. Morgan/ECFP fingerprints capture circular atom neighborhoods and are strong baselines for QSAR and small molecular datasets. MACCS keys encode a fixed set of substructure patterns. Atom-pair and topological torsion fingerprints encode longer graph-distance relationships. Physicochemical descriptors such as logP, TPSA, HBD/HBA counts, rotatable bonds, molecular weight, and formal charge are interpretable but lower-dimensional. 3D descriptors capture shape, conformer geometry, pharmacophores, Coulomb interactions, or surface properties, but depend strongly on conformer generation and alignment.
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.
- 12d ago First seen · 605 lines · 5 tokens per session scan A 66cc7fa3b7cb
molecular-featurization is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 7,438 once invoked, about $0.0000 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 2026-08-31.
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