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.
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Low
Low-risk findings worth noting
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Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
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
At runtime, the required workflow reads outsider-authored free text from user-supplied CSV files (e.g., molecules’ SMILES strings) via `dc.data.CSVLoader(...).create_dataset(data_path)` in `scripts/graph_neural_network.py` (and similarly `predict_solubility.py` / `transfer_learning.py`), without selecting a specific item beforehand.
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- K-Dense-AI/scientific-agent-skills
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b2a92ba
- Audited
- Security analysis
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