pytdc
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
67
52%
Does it follow best practices?
Impact
91%
1.16xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/pytdc/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: 0.80). The skill's primary workflows and scripts (SKILL.md, scripts/load_and_split_data.py, scripts/molecular_generation.py, etc.) explicitly call data.get_data/get_split and utilities like cid2smiles and uniprot2seq and load public datasets (ChEMBL, BindingDB, ZINC, PubChem/UniProt lookups) — i.e., it ingests open/public third‑party data which the agent reads and uses to drive model training, oracle evaluation, and generation decisions, so external content can materially influence actions.
- 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.