Skip to content
TrustedSkillsbeta
Back to all skills

Clip Aware Embeddings

Checked
erichowens·vlatest·ai-ml·MIT

This skill generates CLIP-aware embeddings to capture visual context for richer semantic understanding and improved AI model performance.

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 curiositech/some_claude_skills --skill clip-aware-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

erichowens

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 erichowens/some_claude_skills at 6713fc7a64.

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

Commit
6713fc7a64
Manifest
.claude/skills/clip-aware-embeddings/SKILL.md
Scanned
1 file, 304 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 generates embeddings that are aware of visual concepts. It leverages the CLIP model to create vector representations of text and images, allowing for semantic similarity comparisons between them. This enables tasks like image retrieval based on textual descriptions or finding similar images based on a given query.

When to use it

  • Image Search: Retrieve relevant images from a database based on a user's text description (e.g., "find photos of fluffy white cats").
  • Content Moderation: Identify potentially inappropriate content by comparing image embeddings against known harmful concepts.
  • Visual Question Answering: Help an AI agent understand questions about images, linking textual queries to visual elements.
  • Creative Applications: Generate novel combinations of text and images based on semantic similarity.

Key capabilities

  • CLIP-based embedding generation
  • Text-to-image embedding mapping
  • Image-to-text embedding mapping
  • Semantic similarity comparison between text and images

Example prompts

  • "Generate an embedding for the phrase 'a red apple sitting on a table'."
  • "Find images with embeddings similar to the embedding of the word 'sunset'."
  • “What is the most visually similar image to ‘golden retriever puppy’?”

Tips & gotchas

The quality of the embeddings depends heavily on the CLIP model's training data. Results may vary for niche or highly specific visual concepts not well represented in the original CLIP dataset.