torchdrug
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
90
71%
Does it follow best practices?
Impact
94%
1.18xAverage score across 9 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/torchdrug/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.md workflows explicitly instruct loading public datasets (e.g., datasets.BBBP(), datasets.Hetionet(), datasets.USPTO50k(), AlphaFoldDB/ChEMBL/ZINC references) and note "automatic downloading", so the skill will fetch and ingest external, public third‑party data that the agent reads and that can materially influence model decisions and downstream actions.
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
25e1c0f
- Audited
- Security analysis
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.