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Embedding Comparison

Checked
mindmorass·vlatest·ai-ml

This skill compares embeddings to find similar concepts or identify anomalies by measuring semantic similarity.

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 mindmorass/reflex --skill embedding-comparison -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

mindmorass

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 mindmorass/reflex at b87139cce6.

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

Commit
b87139cce6
Manifest
plugins/reflex/skills/embedding-comparison/SKILL.md
Scanned
1 file, 445 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 allows you to compare embeddings generated from different models or sources. It facilitates identifying subtle differences in semantic representation and understanding how various AI systems interpret information. The tool helps evaluate model performance, debug unexpected behavior, and select the most appropriate embedding strategy for a given task. Ultimately, this leads to more informed decisions about your AI agent's architecture.

When to use it

  • Model Selection: Compare embeddings from different language models (e.g., OpenAI’s text-embedding-ada-002 vs. Cohere’s embed-multilingual-v3) for a specific dataset to determine which produces more meaningful representations.
  • Debugging Semantic Drift: Detect if the meaning of similar phrases changes unexpectedly across different versions or deployments of an embedding model.
  • Evaluating Prompt Engineering: Assess how variations in prompts affect the resulting embeddings and their similarity scores.
  • Similarity Search Optimization: Fine-tune your search parameters by comparing embeddings for optimal retrieval accuracy.

Key capabilities

  • Comparison of embeddings from multiple sources.
  • Identification of subtle differences between embedding vectors.
  • Evaluation of model performance based on embedding quality.
  • Debugging unexpected behavior in AI systems.

Example prompts

  • "Compare the embeddings generated by OpenAI and Cohere for the phrases 'happy customer' and 'satisfied client'."
  • “Show me a visual comparison of the embeddings produced by Model A and Model B when given the prompt, 'Summarize this article about climate change.'"
  • "Which embedding model produces the closest vector to 'artificial intelligence' from the following list: 'machine learning', 'deep learning', 'neural networks'?"

Tips & gotchas

This skill requires access to multiple embedding models or APIs. Ensure you have the necessary credentials and API keys configured before use.