molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
74
70%
Does it follow best practices?
Impact
78%
1.41xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/molfeat/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 explicitly instructs loading pretrained models and model cards from external hubs (e.g., ModelStore.load, PretrainedMolTransformer("ChemBERTa-77M-MLM") and the "Access to transformer models through HuggingFace hub" note in references/available_featurizers.md and SKILL.md), meaning it fetches untrusted public third-party model artifacts and metadata which are ingested and can materially change downstream featurization/decisions.
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- 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.