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Together Embeddings

Listed
zainhas·vlatest·ai-ml·MIT

Together Embeddings generates high-quality vector representations of text data for semantic search and AI applications, boosting performance & understanding.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add zainhas/skills_draft --skill together-embeddings -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

zainhas

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: api.together.xyz, docs.together.ai.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    api.together.xyzundeclareddocs.together.aiundeclared

    What it found

    • skills/together-embeddings/references/api-reference.md:46contacts api.together.xyz
      curl -X POST "https://api.together.xyz/v1/embeddings" \
    • skills/together-embeddings/scripts/embed_and_rerank.py:9contacts docs.together.ai
      https://docs.together.ai/docs/rerank-overview for setup instructions.
    • skills/together-embeddings/scripts/rag_pipeline.py:12contacts docs.together.ai
      pipeline. See https://docs.together.ai/docs/rerank-overview for details.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned zainhas/togetherai-skills at 0995dd3117.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
0995dd3117
Manifest
skills/together-embeddings/SKILL.md
Scanned
8 files, 878 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides access to Together AI's embedding models. Embeddings are numerical representations of text that capture semantic meaning, allowing for tasks like similarity search and clustering. You can use this skill to generate embeddings for text input, enabling your agent to understand relationships between different pieces of content.

When to use it

  • Semantic Search: Find documents or passages similar in meaning to a user's query.
  • Content Clustering: Group related articles or topics together based on their semantic similarity.
  • Recommendation Systems: Suggest relevant items (e.g., products, articles) based on the embeddings of previously viewed items.
  • Knowledge Base Indexing: Create an index for a knowledge base that allows for efficient retrieval of information based on meaning rather than keywords.

Key capabilities

  • Generates text embeddings using Together AI models.
  • Supports various input text lengths.
  • Provides numerical vector representations of text.

Example prompts

  • "Generate an embedding for the following sentence: 'The quick brown fox jumps over the lazy dog.'"
  • "Create embeddings for these two sentences and tell me how similar they are: 'I love pizza' and 'Pizza is my favorite food'."
  • “Embed this product description so I can find similar products.”

Tips & gotchas

  • The quality of the embeddings depends on the chosen Together AI model. Experiment with different models to find the best fit for your task.