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 smiles-smarts-workflowgit 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/smiles-smarts-workflow)<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/smiles-smarts-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/smiles-smarts-workflow/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/smiles-smarts-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/smiles-smarts-workflow.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 | $0.00006 | $0.06935 |
| Opus 5 | $0.00003 | $0.03467 |
| Sonnet 5 | $0.00001 | $0.01387 |
| Haiku 4.5 | $0.00001 | $0.00694 |
Grade A, and why
smiles-smarts-workflow 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 — 465 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SMILES and SMARTS Workflow
Description
This skill covers molecular line notations for cheminformatics and molecular machine learning: parsing and validating SMILES; canonical, non-canonical, and isomeric SMILES; stereochemistry handling; aromaticity perception; RDKit sanitization; molecule standardization, salt/fragment handling, neutralization, and tautomer policies; SMARTS substructure queries and recursive SMARTS; reaction SMARTS with atom mapping; duplicate detection; and interoperable export with provenance. Invoke this skill before ingesting molecules into a database or ML pipeline, when building or debugging substructure filters, or when defining the standardization policy that determines molecular identity for a project.
Domain Context
A SMILES string is a serialization of a molecular graph, not a chemical measurement. It encodes atoms, bonds, connectivity, formal charges, isotopes, and — if written as isomeric SMILES — stereochemistry. It does not encode a conformer ensemble, a protonation microstate at a given pH, a solvent, a counterion's crystallographic role, or a measurement context. The first scientific decision when handling line notations is therefore what molecular identity means for the task: whether a salt and its parent, two tautomers, two protonation states, or two resonance forms should be treated as the same entity.
Representation identity is not the same as structure identity. Two different SMILES strings can encode the same molecule, and string equality between SMILES is neither necessary nor sufficient for chemical identity. Canonicalization produces a single deterministic SMILES for a graph within one toolkit and one version, which makes canonical SMILES useful as a deduplication key inside a controlled pipeline. It is not a portable universal identifier: canonical SMILES from RDKit and from Open Babel need not agree, and canonical algorithms can change across releases. For cross-toolkit or archival identity, standardized InChI/InChIKey is a safer key, though InChI has its own normalization rules for tautomers, charges, and stereochemistry. [EXPERT REVIEW NEEDED]
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 · 465 lines · 6 tokens per session scan A f10d80cacacc
smiles-smarts-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 6 tokens to every session and 6,935 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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