google-gemini-embeddings
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
90
92%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Security
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: 0.90). The skill's RAG workflow (templates/rag-with-vectorize.ts and related examples) ingests arbitrary user-provided/public documents via the POST /ingest endpoint and then uses the stored Vectorize results' metadata.text as context for LLM generation—so untrusted third‑party content can directly influence generation and subsequent tool actions.
- Repository
- jezweb/claude-skills
- Commit
fa91c34
- Audited
- Security analysis
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