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Agent skill

Deepchem

by davila7 in davila7/claude-code-templates

Molecular machine learning toolkit. An agent skill from davila7/claude-code-templates.

MITAuto-check passedResearch & Science

Install Deepchem

skills CLI
$ npx skills add davila7/claude-code-templates --skill deepchem -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates deepchem --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/deepchem .claude/skills/deepchem && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deepchem
GitHub stars
32k
Used in
11 other repos
Token cost
~4.4k tokens
SKILL.md length
888 words
Files
6 (incl. scripts, references)
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Molecular machine learning toolkit. An agent skill from davila7/claude-code-templates.

  • Works in 11 steps: Molecular Data Loading and Processing → Molecular Featurization → Data Splitting → …
  • Tasks that involve Drug discovery and cheminformatics
  • SKILL.md covers Overview, When to Use This Skill, Core Capabilities and Typical Workflows, plus 3 more sections
  • Runs Python scripts from its folder; calls python and uv

What it does

Deepchem is an agent skill from davila7/claude-code-templates. Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/api_reference.md`, `references/workflows.md` and `scripts/graph_neural_network.py`).

It sits in Research & Science, covering Drug discovery and cheminformatics, Machine learning and Deep learning. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics
  • Tasks that involve Machine learning
  • Tasks that involve Deep learning

Example prompts

  • “/deepchem”

Requirements

  • Python 3

Workflow steps

11 steps, taken from the step headings in SKILL.md.

  1. Molecular Data Loading and Processing
  2. Molecular Featurization
  3. Data Splitting
  4. Model Selection and Training
  5. MoleculeNet Benchmarks
  6. Transfer Learning
  7. Model Evaluation
  8. Making Predictions
  9. predict_solubility.py
  10. graph_neural_network.py
  11. transfer_learning.py

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • deepchem.readthedocs.io
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deepchem loads about 4.4k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 888 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 14680ec, republished under its MIT licence (© davila7). 888 words, ~4,352 tokens.

Download SKILL.mdSave it as .claude/skills/deepchem/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
deepchem
description
Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

DeepChem

Overview

DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models.

When to Use This Skill

This skill should be used when:

  • Loading and processing molecular data (SMILES strings, SDF files, protein sequences)
  • Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties)
  • Training models on chemical/biological datasets
  • Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.)
  • Converting molecules to ML-ready features (fingerprints, graph representations, descriptors)
  • Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP)
  • Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer)
  • Predicting crystal/materials properties (bandgap, formation energy)
  • Analyzing protein or DNA sequences

Core Capabilities

1. Molecular Data Loading and Processing

DeepChem provides specialized loaders for various chemical data formats:

python
import deepchem as dc

# Load CSV with SMILES
featurizer = dc.feat.CircularFingerprint(radius=2, size=2048)
loader = dc.data.CSVLoader(
    tasks=['solubility', 'toxicity'],
    feature_field='smiles',
    featurizer=featurizer
)
dataset = loader.create_dataset('molecules.csv')

# Load SDF files
loader = dc.data.SDFLoader(tasks=['activity'], featurizer=featurizer)
dataset = loader.create_dataset('compounds.sdf')

# Load protein sequences
loader = dc.data.FASTALoader()
dataset = loader.create_dataset('proteins.fasta')

Key Loaders:

  • CSVLoader: Tabular data with molecular identifiers
  • SDFLoader: Molecular structure files
  • FASTALoader: Protein/DNA sequences
  • ImageLoader: Molecular images
  • JsonLoader: JSON-formatted datasets
2. Molecular Featurization

Convert molecules into numerical representations for ML models.

Decision Tree for Featurizer Selection
Is the model a graph neural network?
├─ YES → Use graph featurizers
│   ├─ Standard GNN → MolGraphConvFeaturizer
│   ├─ Message passing → DMPNNFeaturizer
│   └─ Pretrained → GroverFeaturizer
│
└─ NO → What type of model?
    ├─ Traditional ML (RF, XGBoost, SVM)
    │   ├─ Fast baseline → CircularFingerprint (ECFP)
    │   ├─ Interpretable → RDKitDescriptors
    │   └─ Maximum coverage → MordredDescriptors
    │
    ├─ Deep learning (non-graph)
    │   ├─ Dense networks → CircularFingerprint
    │   └─ CNN → SmilesToImage
    │
    ├─ Sequence models (LSTM, Transformer)
    │   └─ SmilesToSeq
    │
    └─ 3D structure analysis
        └─ CoulombMatrix
Example Featurization
python
# Fingerprints (for traditional ML)
fp = dc.feat.CircularFingerprint(radius=2, size=2048)

# Descriptors (for interpretable models)
desc = dc.feat.RDKitDescriptors()

# Graph features (for GNNs)
graph_feat = dc.feat.MolGraphConvFeaturizer()

# Apply featurization
features = fp.featurize(['CCO', 'c1ccccc1'])

Selection Guide:

  • Small datasets (<1K): CircularFingerprint or RDKitDescriptors
  • Medium datasets (1K-100K): CircularFingerprint or graph featurizers
  • Large datasets (>100K): Graph featurizers (MolGraphConvFeaturizer, DMPNNFeaturizer)
  • Transfer learning: Pretrained model featurizers (GroverFeaturizer)

See references/api_reference.md for complete featurizer documentation.

3. Data Splitting

Critical: For drug discovery tasks, use ScaffoldSplitter to prevent data leakage from similar molecular structures appearing in both training and test sets.

python
# Scaffold splitting (recommended for molecules)
splitter = dc.splits.ScaffoldSplitter()
train, valid, test = splitter.train_valid_test_split(
    dataset,
    frac_train=0.8,
    frac_valid=0.1,
    frac_test=0.1
)

# Random splitting (for non-molecular data)
splitter = dc.splits.RandomSplitter()
train, test = splitter.train_test_split(dataset)

# Stratified splitting (for imbalanced classification)
splitter = dc.splits.RandomStratifiedSplitter()
train, test = splitter.train_test_split(dataset)

Available Splitters:

  • ScaffoldSplitter: Split by molecular scaffolds (prevents leakage)
  • ButinaSplitter: Clustering-based molecular splitting
  • MaxMinSplitter: Maximize diversity between sets
  • RandomSplitter: Random splitting
  • RandomStratifiedSplitter: Preserves class distributions
4. Model Selection and Training
Quick Model Selection Guide
Dataset SizeTaskRecommended ModelFeaturizer
< 1K samplesAnySklearnModel (RandomForest)CircularFingerprint
1K-100KClassification/RegressionGBDTModel or MultitaskRegressorCircularFingerprint
> 100KMolecular propertiesGCNModel, AttentiveFPModel, DMPNNModelMolGraphConvFeaturizer
Any (small preferred)Transfer learningChemBERTa, GROVER, MolFormerModel-specific
Crystal structuresMaterials propertiesCGCNNModel, MEGNetModelStructure-based
Protein sequencesProtein propertiesProtBERTSequence-based
Example: Traditional ML
python
from sklearn.ensemble import RandomForestRegressor

# Wrap scikit-learn model
sklearn_model = RandomForestRegressor(n_estimators=100)
model = dc.models.SklearnModel(model=sklearn_model)
model.fit(train)
Example: Deep Learning
python
# Multitask regressor (for fingerprints)
model = dc.models.MultitaskRegressor(
    n_tasks=2,
    n_features=2048,
    layer_sizes=[1000, 500],
    dropouts=0.25,
    learning_rate=0.001
)
model.fit(train, nb_epoch=50)
Example: Graph Neural Networks
python
# Graph Convolutional Network
model = dc.models.GCNModel(
    n_tasks=1,
    mode='regression',
    batch_size=128,
    learning_rate=0.001
)
model.fit(train, nb_epoch=50)

# Graph Attention Network
model = dc.models.GATModel(n_tasks=1, mode='classification')
model.fit(train, nb_epoch=50)

# Attentive Fingerprint
model = dc.models.AttentiveFPModel(n_tasks=1, mode='regression')
model.fit(train, nb_epoch=50)
5. MoleculeNet Benchmarks

Quick access to 30+ curated benchmark datasets with standardized train/valid/test splits:

python
# Load benchmark dataset
tasks, datasets, transformers = dc.molnet.load_tox21(
    featurizer='GraphConv',  # or 'ECFP', 'Weave', 'Raw'
    splitter='scaffold',     # or 'random', 'stratified'
    reload=False
)
train, valid, test = datasets

# Train and evaluate
model = dc.models.GCNModel(n_tasks=len(tasks), mode='classification')
model.fit(train, nb_epoch=50)

metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
test_score = model.evaluate(test, [metric])

Common Datasets:

  • Classification: load_tox21(), load_bbbp(), load_hiv(), load_clintox()
  • Regression: load_delaney(), load_freesolv(), load_lipo()
  • Quantum properties: load_qm7(), load_qm8(), load_qm9()
  • Materials: load_perovskite(), load_bandgap(), load_mp_formation_energy()

See references/api_reference.md for complete dataset list.

6. Transfer Learning

Leverage pretrained models for improved performance, especially on small datasets:

python
# ChemBERTa (BERT pretrained on 77M molecules)
model = dc.models.HuggingFaceModel(
    model='seyonec/ChemBERTa-zinc-base-v1',
    task='classification',
    n_tasks=1,
    learning_rate=2e-5  # Lower LR for fine-tuning
)
model.fit(train, nb_epoch=10)

# GROVER (graph transformer pretrained on 10M molecules)
model = dc.models.GroverModel(
    task='regression',
    n_tasks=1
)
model.fit(train, nb_epoch=20)

When to use transfer learning:

  • Small datasets (< 1000 samples)
  • Novel molecular scaffolds
  • Limited computational resources
  • Need for rapid prototyping

Use the scripts/transfer_learning.py script for guided transfer learning workflows.

7. Model Evaluation
python
# Define metrics
classification_metrics = [
    dc.metrics.Metric(dc.metrics.roc_auc_score, name='ROC-AUC'),
    dc.metrics.Metric(dc.metrics.accuracy_score, name='Accuracy'),
    dc.metrics.Metric(dc.metrics.f1_score, name='F1')
]

regression_metrics = [
    dc.metrics.Metric(dc.metrics.r2_score, name='R²'),
    dc.metrics.Metric(dc.metrics.mean_absolute_error, name='MAE'),
    dc.metrics.Metric(dc.metrics.root_mean_squared_error, name='RMSE')
]

# Evaluate
train_scores = model.evaluate(train, classification_metrics)
test_scores = model.evaluate(test, classification_metrics)
8. Making Predictions
python
# Predict on test set
predictions = model.predict(test)

# Predict on new molecules
new_smiles = ['CCO', 'c1ccccc1', 'CC(C)O']
new_features = featurizer.featurize(new_smiles)
new_dataset = dc.data.NumpyDataset(X=new_features)

# Apply same transformations as training
for transformer in transformers:
    new_dataset = transformer.transform(new_dataset)

predictions = model.predict(new_dataset)

Typical Workflows

Workflow A: Quick Benchmark Evaluation

For evaluating a model on standard benchmarks:

python
import deepchem as dc

# 1. Load benchmark
tasks, datasets, _ = dc.molnet.load_bbbp(
    featurizer='GraphConv',
    splitter='scaffold'
)
train, valid, test = datasets

# 2. Train model
model = dc.models.GCNModel(n_tasks=len(tasks), mode='classification')
model.fit(train, nb_epoch=50)

# 3. Evaluate
metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
test_score = model.evaluate(test, [metric])
print(f"Test ROC-AUC: {test_score}")
Workflow B: Custom Data Prediction

For training on custom molecular datasets:

python
import deepchem as dc

# 1. Load and featurize data
featurizer = dc.feat.CircularFingerprint(radius=2, size=2048)
loader = dc.data.CSVLoader(
    tasks=['activity'],
    feature_field='smiles',
    featurizer=featurizer
)
dataset = loader.create_dataset('my_molecules.csv')

# 2. Split data (use ScaffoldSplitter for molecules!)
splitter = dc.splits.ScaffoldSplitter()
train, valid, test = splitter.train_valid_test_split(dataset)

# 3. Normalize (optional but recommended)
transformers = [dc.trans.NormalizationTransformer(
    transform_y=True, dataset=train
)]
for transformer in transformers:
    train = transformer.transform(train)
    valid = transformer.transform(valid)
    test = transformer.transform(test)

# 4. Train model
model = dc.models.MultitaskRegressor(
    n_tasks=1,
    n_features=2048,
    layer_sizes=[1000, 500],
    dropouts=0.25
)
model.fit(train, nb_epoch=50)

# 5. Evaluate
metric = dc.metrics.Metric(dc.metrics.r2_score)
test_score = model.evaluate(test, [metric])
Workflow C: Transfer Learning on Small Dataset

For leveraging pretrained models:

python
import deepchem as dc

# 1. Load data (pretrained models often need raw SMILES)
loader = dc.data.CSVLoader(
    tasks=['activity'],
    feature_field='smiles',
    featurizer=dc.feat.DummyFeaturizer()  # Model handles featurization
)
dataset = loader.create_dataset('small_dataset.csv')

# 2. Split data
splitter = dc.splits.ScaffoldSplitter()
train, test = splitter.train_test_split(dataset)

# 3. Load pretrained model
model = dc.models.HuggingFaceModel(
    model='seyonec/ChemBERTa-zinc-base-v1',
    task='classification',
    n_tasks=1,
    learning_rate=2e-5
)

# 4. Fine-tune
model.fit(train, nb_epoch=10)

# 5. Evaluate
predictions = model.predict(test)

See references/workflows.md for 8 detailed workflow examples covering molecular generation, materials science, protein analysis, and more.

Example Scripts

This skill includes three production-ready scripts in the scripts/ directory:

1. predict_solubility.py

Train and evaluate solubility prediction models. Works with Delaney benchmark or custom CSV data.

bash
# Use Delaney benchmark
python scripts/predict_solubility.py

# Use custom data
python scripts/predict_solubility.py \
    --data my_data.csv \
    --smiles-col smiles \
    --target-col solubility \
    --predict "CCO" "c1ccccc1"
2. graph_neural_network.py

Train various graph neural network architectures on molecular data.

bash
# Train GCN on Tox21
python scripts/graph_neural_network.py --model gcn --dataset tox21

# Train AttentiveFP on custom data
python scripts/graph_neural_network.py \
    --model attentivefp \
    --data molecules.csv \
    --task-type regression \
    --targets activity \
    --epochs 100
3. transfer_learning.py

Fine-tune pretrained models (ChemBERTa, GROVER) on molecular property prediction tasks.

bash
# Fine-tune ChemBERTa on BBBP
python scripts/transfer_learning.py --model chemberta --dataset bbbp

# Fine-tune GROVER on custom data
python scripts/transfer_learning.py \
    --model grover \
    --data small_dataset.csv \
    --target activity \
    --task-type classification \
    --epochs 20

Common Patterns and Best Practices

Pattern 1: Always Use Scaffold Splitting for Molecules
python
# GOOD: Prevents data leakage
splitter = dc.splits.ScaffoldSplitter()
train, test = splitter.train_test_split(dataset)

# BAD: Similar molecules in train and test
splitter = dc.splits.RandomSplitter()
train, test = splitter.train_test_split(dataset)
Show full SKILL.md (357 more words)Show less
Pattern 2: Normalize Features and Targets
python
transformers = [
    dc.trans.NormalizationTransformer(
        transform_y=True,  # Also normalize target values
        dataset=train
    )
]
for transformer in transformers:
    train = transformer.transform(train)
    test = transformer.transform(test)
Pattern 3: Start Simple, Then Scale
  1. Start with Random Forest + CircularFingerprint (fast baseline)
  2. Try XGBoost/LightGBM if RF works well
  3. Move to deep learning (MultitaskRegressor) if you have >5K samples
  4. Try GNNs if you have >10K samples
  5. Use transfer learning for small datasets or novel scaffolds
Pattern 4: Handle Imbalanced Data
python
# Option 1: Balancing transformer
transformer = dc.trans.BalancingTransformer(dataset=train)
train = transformer.transform(train)

# Option 2: Use balanced metrics
metric = dc.metrics.Metric(dc.metrics.balanced_accuracy_score)
Pattern 5: Avoid Memory Issues
python
# Use DiskDataset for large datasets
dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids)

# Use smaller batch sizes
model = dc.models.GCNModel(batch_size=32)  # Instead of 128

Common Pitfalls

Issue 1: Data Leakage in Drug Discovery

Problem: Using random splitting allows similar molecules in train/test sets. Solution: Always use ScaffoldSplitter for molecular datasets.

Issue 2: GNN Underperforming vs Fingerprints

Problem: Graph neural networks perform worse than simple fingerprints. Solutions:

  • Ensure dataset is large enough (>10K samples typically)
  • Increase training epochs (50-100)
  • Try different architectures (AttentiveFP, DMPNN instead of GCN)
  • Use pretrained models (GROVER)
Issue 3: Overfitting on Small Datasets

Problem: Model memorizes training data. Solutions:

  • Use stronger regularization (increase dropout to 0.5)
  • Use simpler models (Random Forest instead of deep learning)
  • Apply transfer learning (ChemBERTa, GROVER)
  • Collect more data
Issue 4: Import Errors

Problem: Module not found errors. Solution: Ensure DeepChem is installed with required dependencies:

bash
uv pip install deepchem
# For PyTorch models
uv pip install deepchem[torch]
# For all features
uv pip install deepchem[all]

Reference Documentation

This skill includes comprehensive reference documentation:

references/api_reference.md

Complete API documentation including:

  • All data loaders and their use cases
  • Dataset classes and when to use each
  • Complete featurizer catalog with selection guide
  • Model catalog organized by category (50+ models)
  • MoleculeNet dataset descriptions
  • Metrics and evaluation functions
  • Common code patterns

When to reference: Search this file when you need specific API details, parameter names, or want to explore available options.

references/workflows.md

Eight detailed end-to-end workflows:

  1. Molecular property prediction from SMILES
  2. Using MoleculeNet benchmarks
  3. Hyperparameter optimization
  4. Transfer learning with pretrained models
  5. Molecular generation with GANs
  6. Materials property prediction
  7. Protein sequence analysis
  8. Custom model integration

When to reference: Use these workflows as templates for implementing complete solutions.

Installation Notes

Basic installation:

bash
uv pip install deepchem

For PyTorch models (GCN, GAT, etc.):

bash
uv pip install deepchem[torch]

For all features:

bash
uv pip install deepchem[all]

If import errors occur, the user may need specific dependencies. Check the DeepChem documentation for detailed installation instructions.

Additional Resources

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in cli-tool/components/skills/scientific/deepchem of davila7/claude-code-templates.

  • SKILL.md
  • references/api_reference.md
  • references/workflows.md
  • scripts/graph_neural_network.py
  • scripts/predict_solubility.py
  • scripts/transfer_learning.py

Open the folder on GitHubat commit 14680ec

Used in 11 other repositories

We found 43 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 11 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Deepchem next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Deepchem compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Bio Molecular DescriptorsFreedomIntelligence/OpenClaw-Medical-Skills3.1k—~1.6kAutomated safety check: PassNone
Drug Target Interactionwentorai/research-plugins2981 repos~2.2kAutomated safety check: PassMIT
tangermeme Genomic Model Analysisjmschrei/tangermeme311—~1.6kAutomated safety check: PassMIT

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    davila7/claude-code-templates

    Senior FDA consultant and specialist for medical device companies including HIPAA compliance and requirement management.

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Questions about Deepchem

What does Deepchem do?

Molecular machine learning toolkit. An agent skill from davila7/claude-code-templates. Deepchem is an agent skill from davila7/claude-code-templates. Molecular machine learning toolkit.

When should I use Deepchem?

Deepchem fits situations like: tasks that involve Drug discovery and cheminformatics; tasks that involve Machine learning; tasks that involve Deep learning.

How do I install Deepchem in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill deepchem -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/deepchem in davila7/claude-code-templates) into .claude/skills/deepchem in your project. Claude Code loads it when a task matches its description.

How do I install Deepchem in Codex?

Run `npx skills add davila7/claude-code-templates --skill deepchem -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/deepchem in davila7/claude-code-templates) into .agents/skills/deepchem in your project. Codex loads it when a task matches its description.

Can I use Deepchem in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill deepchem -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepchem, .gemini/skills/deepchem, .github/skills/deepchem and .opencode/skills/deepchem in your project.

What does Deepchem need to run?

Going by SKILL.md and its folder, Deepchem needs Python for the scripts in its folder and the command-line tools its instructions call (python and uv). Our summary lists: Python 3.

Does Deepchem access the network?

SKILL.md names 2 domains. As links in the text: deepchem.readthedocs.io and github.com. This is read from the text; nothing was executed.

Is Deepchem safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Deepchem use?

Deepchem is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deepchem use?

About 4.4k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.8k tokens, read only when the agent opens those files.

What are the alternatives to Deepchem?

Skills that share tags, products or a category with Deepchem: Mq Variational Training (mindspore-ai/mindquantum, 101 stars), Molfeat (lamm-mit/scienceclaw, 244 stars), Bio Molecular Descriptors (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Drug Target Interaction (wentorai/research-plugins, 298 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepchem?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.