Gemini Embedding 2 — Google's natively multimodal embedding model (GA April 2026). Maps text, images, video, audio, and PDFs into a unified 3072-dim vector space with MRL support (truncate to 128-3072). Use when: embeddings, semantic search, RAG, clustering, classification, multimodal retrieval, vector database, MRL truncation, cosine similarity, narrative memory, code retrieval, cross-modal search, agentic RAG.
How to install gemini-embedding-2?
fandry96/k3-agentic-skills/gemini-embedding-2
$npx -y skills add fandry96/k3-agentic-skills --skill gemini-embedding-2Installs into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/fandry96/k3-agentic-skills" --skill "fandry96/k3-agentic-skills/gemini-embedding-2"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/fandry96/k3-agentic-skills" that are relevant to the current task. Run `npx skills add "https://github.com/fandry96/k3-agentic-skills"` and select the relevant skills, then follow their instructions.
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