ScienceClaw is an agent system for scientific work that completes tasks as typed, executable workflows and turns replay-verified improvements into reusable skills and operators. It is intended for AI-assisted research across natural and social science disciplines. The catalogue entries are its skills and instruction for use with the system.
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 beita6969/ScienceClaw --skill rdkit-chemistrygit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/rdkit-chemistry)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/rdkit-chemistry"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/rdkit-chemistry/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/beita6969/scienceclaw/rdkit-chemistry"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/rdkit-chemistry.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 | $0.00042 | $0.01148 |
| Opus 5.5 | $0.00017 | $0.00459 |
| Sonnet 5.5 | $0.00008 | $0.00230 |
| Haiku 4.5 | $0.00004 | $0.00115 |
Grade A, and why
rdkit-chemistry 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RDKit Chemistry
Molecular chemistry operations using RDKit.
When to Use
- Molecular structures, SMILES parsing, or validation
- Molecular properties (MW, logP, TPSA, HBD/HBA)
- Substructure searching or molecular filtering
- Fingerprints (Morgan, MACCS) and similarity calculations
- 2D depiction or molecular image generation
When NOT to Use
- Reaction databases or retrosynthesis planning
- Wet lab protocols or experimental procedures
- Protein structure analysis (use biopython-bio)
- Quantum chemistry or DFT calculations
SMILES Parsing and Properties
from rdkit import Chem
from rdkit.Chem import Descriptors, rdMolDescriptors
mol = Chem.MolFromSmiles('CC(=O)Oc1ccccc1C(=O)O') # Aspirin
if mol is None:
print("Invalid SMILES")
canonical = Chem.MolToSmiles(mol) # Canonical SMILES
mw = Descriptors.MolWt(mol) # Molecular weight
logp = Descriptors.MolLogP(mol) # Partition coefficient
tpsa = Descriptors.TPSA(mol) # Topological polar surface area
hbd = rdMolDescriptors.CalcNumHBD(mol) # H-bond donors
hba = rdMolDescriptors.CalcNumHBA(mol) # H-bond acceptors
rotatable = rdMolDescriptors.CalcNumRotatableBonds(mol)
# SMARTS substructure match
pattern = Chem.MolFromSmarts('[OH]')
has_oh = mol.HasSubstructMatch(pattern)
Lipinski Rule of Five
def lipinski(smi):
mol = Chem.MolFromSmiles(smi)
return {
'MW <= 500': Descriptors.MolWt(mol) <= 500,
'LogP <= 5': Descriptors.MolLogP(mol) <= 5,
'HBD <= 5': rdMolDescriptors.CalcNumHBD(mol) <= 5,
'HBA <= 10': rdMolDescriptors.CalcNumHBA(mol) <= 10,
}
Substructure Search
molecules = [Chem.MolFromSmiles(s) for s in ['CCO', 'CC(=O)O', 'c1ccccc1', 'c1ccccc1O']]
pattern = Chem.MolFromSmarts('c1ccccc1') # Benzene ring
hits = [m for m in molecules if m.HasSubstructMatch(pattern)]
Fingerprints and Similarity
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 · 121 lines · 42 tokens per session scan A fe2731553904
rdkit-chemistry is a skill published in the GitHub repository beita6969/ScienceClaw (1,019 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 1,148 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
jupyter-notebook
Iterative Python via live Jupyter kernel (hamelnb).
astropy
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
bioservices
Provides a Python interface to bioinformatics services including UniProt, KEGG, ChEMBL, Reactome, QuickGO, and UniChem. Used for cross-database protein annotation, pathway retrieval, chemical identifier mapping, and integrated biological data workflows with BioServices.
cobrapy
Performs constraint-based metabolic modeling with COBRApy, including FBA, pFBA, FVA, gene knockouts, flux sampling, growth media, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.