rdkit

rdkit is a skill for Claude Code, Codex from LeonChaoX/qinyan-academic-skills. It costs 80 tokens per session (5,342 once invoked), scanned A, a copy of rdkit, MIT.

A Python toolkit for working with chemical structures in formats such as SMILES, a text notation for molecules, and SDF files. It can calculate molecular properties, compare structures, search substructures, model reactions, and create 2D or 3D representations.

In plain words
What is it for?
Use it to read and write molecular files, calculate descriptors such as molecular weight and LogP, find similar or matching structures, generate coordinates and images, and analyze chemical reactions.
Why use it?
It gives developers detailed control over chemical data and operations needed in drug discovery and chemistry research. This avoids writing low-level code for parsing, comparing, and transforming molecules.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to read and write molecular files, calculate descriptors such as molecular weight and LogP, find similar or matching structures, generate coordinates and images, and analyze chemical reactions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/leonchaox/qinyan-academic-skills/rdkit
Install

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.

Any agent
npx skills add LeonChaoX/qinyan-academic-skills --skill rdkit
Clone the repo
git clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for rdkit

README.md
[![agentmods](https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/rdkit/github.svg)](https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/rdkit)
Your own site
<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.

agentmods 80×15 button for rdkit

Your own site · 80×15
<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>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,342 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 92% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 17b2e0f675ef, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/molecular_properties.py, scripts/similarity_search.py, scripts/substructure_filter.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

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.

skills/06-化学信息与药物发现/rdkit/SKILL.md · 779 lines

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 return None on failure with error messages
  • Always check for None before processing molecules
  • Molecules are automatically sanitized on import (validates valence, perceives aromaticity)

Read the full file on GitHub · 779 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 779 lines · 80 tokens per session scan A 17b2e0f675ef

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

academic-research

Nested swiss-knife reference for academic literature work — find papers, fetch full-text PDFs, trace citations, write LaTeX manuscripts. First action for any "get me this paper" request: python3 /scripts/fetchpaper.py — walks arXiv → Unpaywall → Europe PMC → CORE → in-house publisher-page extraction…

Lingtai-AI/lingtai · 180 tokens

clean-data

Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher…

Aperivue/medsci-skills · 64 tokens

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…

Aperivue/medsci-skills · 191 tokens

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…

Aperivue/medsci-skills · 169 tokens

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…

Aperivue/medsci-skills · 133 tokens

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…

Aperivue/medsci-skills · 223 tokens