{
  "name": "google-gemini-embeddings",
  "owner": {
    "name": "Jeremy Dawes",
    "email": "jeremy@jezweb.net"
  },
  "plugins": [
    {
      "name": "google-gemini-embeddings",
      "description": "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",
      "source": {
        "source": "url",
        "url": "https://git.waymay.us/zhongwei/gh-jezweb-claude-skills-skills-google-gemini-embeddings.git"
      },
      "strict": true
    }
  ]
}