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 LeonChaoX/qinyan-academic-skills --skill rdkitgit clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-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/leonchaox/qinyan-academic-skills/rdkit)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/rdkit"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/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/leonchaox/qinyan-academic-skills/rdkit"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/rdkit.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 | $0.00080 | $0.05342 |
| Opus 5 | $0.00040 | $0.02671 |
| Sonnet 5 | $0.00016 | $0.01068 |
| Haiku 4.5 | $0.00008 | $0.00534 |
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 9d 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
92% identical to rdkit — 7 lines 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 — 779 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 ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 779 lines · 80 tokens per session scan A 17b2e0f675ef
rdkit is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (884 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 5,342 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to rdkit, differing in 7 lines, and is treated as a copy.
Other skills, from other repositories
academic-research
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clean-data
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model-scaffold
Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, image-to-image synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone (transfer learning) — the missing middle link between choosing an architecture and validating a…
model-sourcing
Vet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family. Records a model dossier (source and version pin, licence and the file it was read from, intended use, pretrained-weight provenance, model task vs study task, reported validation…
preprocess-imaging
Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling and intensity normalisation, and the augmentation plan — so the pipeline is leakage-safe before model-scaffold builds the training repo. Emits a declarative preprocessing manifest and a deterministic data-stage…
radiomics-ml
Produce or audit a radiomics / tabular clinical-ML study — imaging or clinical features → any classical learner (penalised logistic [LASSO / ridge / elastic-net], SVM, k-NN, naive Bayes, LDA/QDA, decision tree, random forest, gradient boosting [XGBoost / LightGBM / CatBoost], shallow MLP, stacked ensembles) → a…