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together-embeddings

Skill

Dense vector embeddings, semantic search, RAG pipelines, and reranking via Together AI. Generate embeddings with open-source models and rerank results behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text gen

0Machine Learning

Install

git clone https://github.com/togethercomputer/skills.git ~/.claude/skills/together-embeddings

What is together-embeddings?

Dense vector embeddings, semantic search, RAG pipelines, and reranking via Together AI. Generate embeddings with open-source models and rerank results behind dedicated endpoints. Reach for it whenever the user needs vector representations or retrieval quality improvements rather than direct text generation.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~77 tokens of context used while enabled, before you invoke anything

Documentation

README · ~2 min read

Together Embeddings & Reranking

Overview

Use this skill for semantic retrieval components:

  • create embeddings
  • batch embeddings
  • build retrieval or RAG pipelines
  • rerank retrieved candidates

This skill is for retrieval plumbing, not for the final language-model response itself.

When This Skill Wins

  • Build vector search or semantic similarity features

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