google-gemini-embeddings
| 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, integrati
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Installation
View all platforms →skilz install jezweb_claude-skills/google-gemini-embeddings skilz install jezweb_claude-skills/google-gemini-embeddings --agent opencode skilz install jezweb_claude-skills/google-gemini-embeddings --agent codex skilz install jezweb_claude-skills/google-gemini-embeddings --agent gemini
First time? Install Skilz: pip install skilz
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Extract and copy to ~/.claude/skills/ then restart Claude Desktop
git clone https://github.com/jezweb/claude-skills cp -r claude-skills/skills/google-gemini-embeddings ~/.claude/skills/ Need detailed installation help? Check our platform-specific guides:
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Details
- Owner
- jezweb
- Repository
- claude-skills
- Stars
- 99
- Forks
- 14
- Type
- Technical
- Meta-Domain
- data ai
- Primary Domain
- database
- Sub-Domain
- search vector
- Skill Size
- 122.8 KB
- Files
- 17
- Quality Score
- 68.6
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