synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill cheminformatics-definitionsgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/cheminformatics-definitions)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/cheminformatics-definitions"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/cheminformatics-definitions/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/synthetic-sciences/openscience/cheminformatics-definitions"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/cheminformatics-definitions.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 | $0.00155 | $0.02114 |
| Opus 5.5 | $0.00062 | $0.00846 |
| Sonnet 5.5 | $0.00031 | $0.00423 |
| Haiku 4.5 | $0.00015 | $0.00211 |
Grade A, and why
cheminformatics-definitions 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cheminformatics definitions
The same molecule has several hydrogen-bond acceptor counts, several polar surface areas and several canonical SMILES, all correct under some definition. RDKit implements more than one of each. Compute with the definition the question uses, and print the definition next to the number; a bare "HBA = 7" is not reproducible.
Rules
- Lipinski counts. The original rule of five counts donors as the number of N-H and
O-H bonds and acceptors as the number of N and O atoms. In RDKit these are
rdMolDescriptors.CalcNumLipinskiHBDandCalcNumLipinskiHBA(also exposed asLipinski.NHOHCountandLipinski.NOCount).Lipinski.NumHDonorsandLipinski.NumHAcceptors(same asrdMolDescriptors.CalcNumHBDandCalcNumHBA) use SMARTS-based pharmacophore definitions that exclude, for example, amide nitrogens and include some sulfur; the two acceptor counts often differ by two or three. The rule's thresholds are MW <= 500, cLogP <= 5, HBD <= 5, HBA <= 10, with "at most one violation" in the original formulation. cLogP in RDKit is Wildman-Crippen (Crippen.MolLogP), which differs from XLogP3 or other predictors by up to a log unit; name the method. - Molecular weight.
Descriptors.MolWtis the average isotopic mass,Descriptors.ExactMolWtthe monoisotopic mass; both depend on whether the counterion was stripped first. - TPSA. Ertl's fragment method. RDKit's default (
Descriptors.TPSA(mol)orrdMolDescriptors.CalcTPSA(mol)) sums N and O contributions only, matching the reference values in the original paper;includeSandP=Trueadds S and P. Sulfonamides and phosphates shift by roughly 10 square angstroms between the two. - QED.
QED.qed(mol)uses Bickerton's mean weights by default;QED.weights_maxandQED.weights_nonegive different values, andQED.properties(mol)exposes the eight underlying components (which use RDKit's own HBA, HBD and alert definitions). Report "QED, RDKit, mean weights". - Rotatable bonds.
rdMolDescriptors.CalcNumRotatableBonds(mol, strict)hasNumRotatableBondsOptions.NonStrict,Strict(excludes amide C-N and similar) andStrictLinkages; Veber-style filters were derived with a particular definition, so state which was used. - InChI and InChIKey.
Chem.MolToInchi(mol)with no options yields standard InChI (InChI=1S/); any option such as/FixedHyields a non-standard InChI (InChI=1/) whose key is not comparable to standard keys.Chem.MolToInchiKey(mol)gives the 27-character key: a 14-character skeleton block, an 8-character block for stereo and isotopes, a version and standard flag, then a protonation character. Stereoisomers share the first block, so matching on it is skeleton matching. Standard InChI normalizes mobile hydrogens, so some tautomers collapse to one key and others do not. - SMILES.
Chem.MolToSmiles(mol)is canonical and isomeric by default (isomericSmiles=Truekeeps stereo and isotopes;Falsedrops them;kekuleSmiles=TrueafterChem.Kekulizewrites explicit bond orders). Canonical means canonical within one toolkit and version; never join tables from different toolkits on SMILES text. Recanonicalize everything through the same RDKit build or join on InChIKey. - Standardization order.
rdMolStandardize.Cleanup(sanitize, disconnect metals, normalize functional groups, reionize),FragmentParent(largest organic fragment, which is the usual salt stripping),ChargeParent(uncharged fragment parent, orUncharger().uncharge),TautomerEnumerator().Canonicalize(orTautomerParent).Chem.SaltRemover.SaltRemoverstrips a fixed list of counterions instead and behaves differently for solvates and mixed salts. Uncharging is not protonation at pH 7.4; a pKa-based protonation step is a different, separately named operation. Descriptors on the parent differ from descriptors on the drawn salt form; say which was profiled. - Aromaticity. RDKit's default model differs from the MDL model
(
Chem.SetAromaticity(mol, Chem.AromaticityModel.AROMATICITY_MDL)afterChem.Kekulize(mol, clearAromaticFlags=True)) and from OpenBabel or Daylight perception. Aromatic ring counts, aromatic-atom fractions and lowercase SMARTS matches all change with the model. Inputs with unusual valences may needChem.MolFromSmiles(smi, sanitize=False)followed byChem.SanitizeMolwith explicit flags; record any molecule that failed to parse rather than dropping it silently. - Hydrogens and stereo. Descriptors that count hydrogens need the implicit-H graph,
3D work needs
Chem.AddHs; write canonical SMILES afterChem.RemoveHs.Chem.FindMolChiralCenters(mol, includeUnassigned=True, useLegacyImplementation=False)reveals unspecified centers that will make "same molecule" comparisons ambiguous. - Always state
rdkit.__version__, the function and arguments, the standardization steps and their order, and whether values are for the parent or the drawn form. Put a definitions footnote under every property table.
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 First seen · 116 lines · 155 tokens per session scan A 195318cab291
cheminformatics-definitions is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 155 tokens to every session and 2,114 once invoked, about $0.0006 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 2026-09-15.
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