deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
79
75%
Does it follow best practices?
Impact
83%
1.36xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/deepchem/SKILL.mdSecurity
1 medium severity finding. This skill can be installed but you should review these findings before use.
W011: Third-party content exposure detected (indirect prompt injection risk)
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
Third-party content exposure detected (high risk: 0.70). The skill's workflows and scripts explicitly load public third‑party data and models (e.g., dc.molnet.load_* calls in SKILL.md and scripts/graph_neural_network.py / scripts/predict_solubility.py to fetch MoleculeNet benchmarks and dc.models.HuggingFaceModel with model id 'seyonec/ChemBERTa-zinc-base-v1' in scripts/transfer_learning.py), which are untrusted external resources that the agent ingests and uses to train/fine‑tune models and drive downstream predictions, so external content can materially influence agent behavior.
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- Audited
- Security analysis
Is this your skill?
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