Build RAG systems, semantic search, and document clustering with Gemini embeddings API (gemini-embedding-001). Generate 768-3072 dimension embeddings for vector search, integrate with Cloudflare Vectorize, and use 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY) for optimized retrieval. Use when: implementing vector search with Google embeddings, building retrieval-augmented generation systems, creating semantic search features, clustering documents by meaning, integrating
/plugin marketplace add jezweb/claude-skills/plugin install jezweb-google-gemini-embeddings-skills-google-gemini-embeddings@jezweb/claude-skillsMigrate your code and prompts from Sonnet 4.x and Opus 4.1 to Opus 4.5.
Frontend design skill for UI/UX implementation
Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
Easily create hooks to prevent unwanted behaviors by analyzing conversation patterns