torch-geometric
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
82
77%
Does it follow best practices?
Impact
98%
1.19xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/torch_geometric/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 primary workflow (SKILL.md and references/datasets_reference.md) includes loading public, user-generated datasets (e.g., Planetoid/Reddit, Twitch, Facebook, GitHub via torch_geometric.datasets) which the agent downloads and ingests as part of training/analysis, so untrusted third‑party content can influence model behavior and downstream actions.
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b271271
- 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.