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 agentmods add skills/zaoqu-liu/scienceclaw/rdkitnpx skills add Zaoqu-Liu/ScienceClaw --skill rdkitgit clone --depth 1 https://github.com/Zaoqu-Liu/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/zaoqu-liu/scienceclaw/rdkit)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/rdkit"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/rdkit.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.05407 |
| Opus 5 | $0.00040 | $0.02704 |
| Sonnet 5 | $0.00016 | $0.01081 |
| Haiku 4.5 | $0.00008 | $0.00541 |
Grade A, and why
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 2d 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.
This is a copy
100% identical to rdkit — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 769 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RDKit Cheminformatics Toolkit
Overview
RDKit is a comprehensive cheminformatics library providing Python APIs for molecular analysis and manipulation. This skill provides guidance for reading/writing molecular structures, calculating descriptors, fingerprinting, substructure searching, chemical reactions, 2D/3D coordinate generation, and molecular visualization. Use this skill for drug discovery, computational chemistry, and cheminformatics research tasks.
Core Capabilities
1. Molecular I/O and Creation
Reading Molecules:
Read molecular structures from various formats:
from rdkit import Chem
# From SMILES strings
mol = Chem.MolFromSmiles('Cc1ccccc1') # Returns Mol object or None
# From MOL files
mol = Chem.MolFromMolFile('path/to/file.mol')
# From MOL blocks (string data)
mol = Chem.MolFromMolBlock(mol_block_string)
# From InChI
mol = Chem.MolFromInchi('InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H')
Writing Molecules:
Convert molecules to text representations:
# To canonical SMILES
smiles = Chem.MolToSmiles(mol)
# To MOL block
mol_block = Chem.MolToMolBlock(mol)
# To InChI
inchi = Chem.MolToInchi(mol)
Batch Processing:
For processing multiple molecules, use Supplier/Writer objects:
# Read SDF files
suppl = Chem.SDMolSupplier('molecules.sdf')
for mol in suppl:
if mol is not None: # Check for parsing errors
# Process molecule
pass
# Read SMILES files
suppl = Chem.SmilesMolSupplier('molecules.smi', titleLine=False)
# For large files or compressed data
with gzip.open('molecules.sdf.gz') as f:
suppl = Chem.ForwardSDMolSupplier(f)
for mol in suppl:
# Process molecule
pass
# Multithreaded processing for large datasets
suppl = Chem.MultithreadedSDMolSupplier('molecules.sdf')
# Write molecules to SDF
writer = Chem.SDWriter('output.sdf')
for mol in molecules:
writer.write(mol)
writer.close()
Important Notes:
- All
MolFrom*functions returnNoneon failure with error messages - Always check for
Nonebefore processing molecules - Molecules are automatically sanitized on import (validates valence, perceives aromaticity)
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.
- 2d ago First seen · 769 lines · 80 tokens per session scan A 0f0a94a8e952
rdkit is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 80 tokens to every session and 5,407 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to rdkit, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
jupyter-notebook
Iterative Python via live Jupyter kernel (hamelnb).
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
bioservices
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use…
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
biology-biopython
Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.