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/kdevos12/alkyl/deepchemnpx skills add Kdevos12/ALKYL --skill deepchemgit clone --depth 1 https://github.com/Kdevos12/ALKYLWhat 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 | $0.00047 | $0.00652 |
| Opus 5 | $0.00023 | $0.00326 |
| Sonnet 5 | $0.00009 | $0.00130 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
deepchem 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepChem
Deep learning for the life sciences: drug discovery, quantum chemistry, materials science, bioinformatics.
When to Use This Skill
- Building ML models on molecular datasets (SMILES, graphs, fingerprints)
- Working with MoleculeNet benchmark datasets
- Predicting molecular properties (solubility, toxicity, binding affinity)
- Protein-ligand interaction modeling
- Quantum chemistry property prediction (QM9, GDB datasets)
- Featurizing molecules for downstream ML tasks
- Virtual screening and drug discovery pipelines
Quick Start — Standard Workflow
import deepchem as dc
# 1. Load dataset with featurizer
tasks, datasets, transformers = dc.molnet.load_delaney(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = datasets
# 2. Create model
model = dc.models.GraphConvModel(n_tasks=1, mode='regression', dropout=0.2)
# 3. Train
model.fit(train_dataset, nb_epoch=100)
# 4. Evaluate
metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)
train_score = model.evaluate(train_dataset, [metric], transformers)
test_score = model.evaluate(test_dataset, [metric], transformers)
# 5. Predict
predictions = model.predict_on_batch(test_dataset.X[:10])
Router — What to Read
| Task | Reference |
|---|---|
| Dataset creation, access, splitters | references/core-concepts.md |
| Training workflow, metrics, hyperopt, multitask | references/model-training.md |
| Fingerprints, GCN, ChemBERTa, graph models | references/mol-machine-learning.md |
| MoleculeNet, protein-ligand, virtual screening | references/drug-discovery.md |
| QM9, DeepQMC, materials science | references/quantum-materials.md |
Installation
pip install --pre deepchem # with TensorFlow
pip install --pre deepchem[torch] # with PyTorch
pip install --pre deepchem[jax] # with JAX
import deepchem as dc
dc.__version__ # verify installation
Key Submodules
| Submodule | Role |
|---|---|
dc.molnet |
MoleculeNet dataset loaders |
dc.models |
All model classes |
dc.feat |
Featurizers |
dc.metrics |
Evaluation metrics |
dc.splits |
Dataset splitters |
dc.data |
Dataset classes |
dc.trans |
Transformers (normalization, etc.) |
What ships with it
5 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.
- 2d ago First seen · 83 lines · 47 tokens per session scan A 4533e272a41f
deepchem is a skill published in the GitHub repository Kdevos12/ALKYL (6 stars, last pushed 5mo ago), licensed MIT. It adds 47 tokens to every session and 652 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 2026-08-31.
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