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, integrating embeddings with Cloudflare Vectorize, optimizing dimension sizes (128-3072), or troubleshooting dimension mismatch errors, incorrect task type selections, rate limit issues (100 RPM free tier), vector normalization mistakes, or text truncation errors (2,048 token limit).
Install
mkdir -p .claude/skills/google-gemini-embeddings && curl -L -o skill.zip "https://mcp.directory/api/skills/download/1404" && unzip -o skill.zip -d .claude/skills/google-gemini-embeddings && rm skill.zipInstalls to .claude/skills/google-gemini-embeddings
About google-gemini-embeddings
google-gemini-embeddings is a specialized agent skill created by benchflow-ai that extends AI coding assistants with enhanced capabilities. Agent skills provide context, workflows, and specialized knowledge that help AI assistants perform specific tasks more effectively than general-purpose AI alone.
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 embeddings with Cloudflare Vectorize, optimizing dimension sizes (128-3072), or troubleshooting dimension mismatch errors, incorrect task type selections, rate limit issues (100 RPM free tier), vector normalization mistakes, or text truncation errors (2,048 token limit).
How to use google-gemini-embeddings
Once installed, google-gemini-embeddings becomes available in your AI coding environment automatically. Your AI assistant will use the skill's instructions and knowledge whenever relevant tasks arise in your workflow. You can install it at the project level (available only in a specific project) or globally (available across all your projects).
Use the install panel on this page to copy a one-line command for your preferred AI client. The skill files are downloaded and placed in the appropriate directory — no additional configuration is required.
Compatible AI clients
google-gemini-embeddings works with multiple AI coding assistants including Cursor, Claude Code, Codex, Copilot, and other agents that support the skills format. Each client stores skills in a slightly different directory, but the installation command handles this automatically.
What are agent skills?
Agent skills are reusable instruction sets that give AI coding assistants new capabilities. Unlike MCP servers that provide tools and API connections, skills provide context, workflows, and domain-specific knowledge. Think of skills as specialized training for your AI assistant — they help it understand particular frameworks, coding patterns, or development workflows.
Browse more skills in the skills directory or check out the leaderboard to see the most popular skills.
About the author
benchflow-ai has published 7 skills on MCP.Directory. Browse their full catalog to discover complementary skills for your workflow.
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