AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.
Borrowing it
Nothing to install: this file belongs to aiming-lab/AutoResearchClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aiming-lab/AutoResearchClaw/main/.claude/skills/chemistry-rdkit/SKILL.mdgit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWrote 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/aiming-lab/autoresearchclaw/chemistry-rdkit)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/chemistry-rdkit"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/chemistry-rdkit/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/aiming-lab/autoresearchclaw/chemistry-rdkit"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/chemistry-rdkit.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 | $0.00041 | $0.00812 |
| Opus 5 | $0.00020 | $0.00406 |
| Sonnet 5 | $0.00008 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
chemistry-rdkit 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 11d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RDKit Cheminformatics Best Practice
Molecular I/O
- Create molecules from SMILES:
mol = Chem.MolFromSmiles('CCO') - Always check for None:
MolFromSmilesreturns None on invalid input - Convert to canonical SMILES:
Chem.MolToSmiles(mol) - Read SDF files:
suppl = Chem.SDMolSupplier('file.sdf') - Read SMILES files:
suppl = Chem.SmilesMolSupplier('file.smi') - Write molecules:
writer = Chem.SDWriter('output.sdf')
Molecular Descriptors
- Molecular weight:
Descriptors.MolWt(mol) - LogP (lipophilicity):
Descriptors.MolLogP(mol) - TPSA (polar surface area):
Descriptors.TPSA(mol) - H-bond donors/acceptors:
Descriptors.NumHDonors(mol),Descriptors.NumHAcceptors(mol) - Rotatable bonds:
Descriptors.NumRotatableBonds(mol) - Lipinski Rule of 5: MW <= 500, LogP <= 5, HBD <= 5, HBA <= 10
Fingerprints and Similarity
- Morgan (circular) fingerprints:
AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048) - RDKit fingerprints:
Chem.RDKFingerprint(mol) - MACCS keys:
MACCSkeys.GenMACCSKeys(mol) - Tanimoto similarity:
DataStructs.TanimotoSimilarity(fp1, fp2) - Use radius=2 (ECFP4 equivalent) as default for most applications
- For virtual screening, Tanimoto > 0.7 suggests structural similarity
Substructure Search
- SMARTS patterns:
pattern = Chem.MolFromSmarts('[OH]') - Check match:
mol.HasSubstructMatch(pattern) - Get all matches:
mol.GetSubstructMatches(pattern) - Common SMARTS:
[#6](=O)[OH](carboxylic acid),[NH2](primary amine) - Filter compound libraries by functional group presence
Property Calculation Patterns
- Batch processing: iterate over SDMolSupplier, skip None entries
- Use
Chem.Descriptors.descListfor all available descriptors - For ADMET filtering, calculate Lipinski, Veber, and PAINS filters
- Generate 3D coordinates:
AllChem.EmbedMolecule(mol, AllChem.ETKDG()) - Minimize energy:
AllChem.MMFFOptimizeMolecule(mol)
Common Pitfalls
- Always sanitize molecules (default behavior) — disable only when needed
- Add hydrogens explicitly for 3D work:
Chem.AddHs(mol) - Handle stereochemistry: use
Chem.AssignStereochemistry(mol) - Large SDF files: use
ForwardSDMolSupplierfor memory efficiency - Kekulization errors usually indicate invalid SMILES input
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.
- 11d ago First seen · 60 lines · 41 tokens per session scan A 7cae29200713
chemistry-rdkit is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,389 stars, last pushed 22d ago), licensed MIT. It adds 41 tokens to every session and 812 once invoked, about $0.0002 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 2026-08-30.
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