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-datasetsgit 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-datasets)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/molecular-datasets"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/molecular-datasets/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-datasets"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/molecular-datasets.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 | $0.00004 | $0.07110 |
| Opus 5 | $0.00002 | $0.03555 |
| Sonnet 5 | $0.00001 | $0.01422 |
| Haiku 4.5 | $0.00000 | $0.00711 |
Grade A, and why
molecular-datasets 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 — 575 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular Datasets
Description
This skill covers molecular datasets for chemistry and molecular machine learning: selecting appropriate sources, preserving identifiers and metadata, standardizing molecular records, deduplicating structures, harmonizing units and labels, creating leakage-aware splits, auditing provenance and licenses, and preparing curated datasets for molecular featurization, molecular GNNs, small-data ML, and surrogate validation. Invoke this skill before training or comparing molecular property models, especially when combining public benchmarks, database exports, quantum-chemistry datasets, or internal experimental measurements.
Domain Context
A molecular dataset is more than a table of SMILES and labels. Each row carries assumptions about molecular identity, measurement protocol, target definition, and data provenance. The same compound may appear as a salt, neutral parent, tautomer, stereoisomer, InChIKey duplicate, SDF entry, or assay-specific record. Two rows with the same canonical SMILES may have different labels because they were measured at different pH, temperature, solvent, assay target, experimental protocol, or quantum-chemistry level.
Dataset source defines what claims a model can support. QM9 is a small-molecule quantum-chemistry dataset for equilibrium gas-phase organic molecules. MoleculeNet aggregates many benchmark tasks with different label types and split conventions. Tox21, ESOL, FreeSolv, and Lipophilicity are useful molecular-property benchmarks but are small and can be overfit by repeated leaderboard tuning. ChEMBL, BindingDB, PubChem, ZINC, and internal screening campaigns contain richer chemistry but require heavier curation, metadata harmonization, and licensing checks. OC20/OC22 are primarily catalyst/surface datasets rather than ordinary isolated-molecule datasets, but they are relevant when molecules are adsorbates on materials surfaces.
The core risk is accidental incompatibility. Mixing pIC50, Ki, Kd, percent inhibition, and qualitative activity labels as if they are one target creates a model of assay artifacts. Mixing gas-phase DFT barriers with solvent-corrected experimental rates creates a multi-fidelity target that must be labeled as such. Combining public and internal data without source tags makes it impossible to diagnose whether a model learned chemistry or source bias.
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 · 575 lines · 4 tokens per session scan A 45530e04ca3e
molecular-datasets is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 4 tokens to every session and 7,110 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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