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Clip

Listed
davila7·vlatest·other·MIT

Clip extracts relevant text snippets from any given input, streamlining information retrieval and simplifying complex content analysis.

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 davila7/claude-code-templates --skill clip -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

davila7

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 pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

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

    What it found

    • cli-tool/components/skills/ai-research/multimodal-clip/SKILL.md:41pip install straight from a URL
      pip install git+https://github.com/openai/CLIP.git

  • 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.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/multimodal-clip/SKILL.md
Scanned
2 files, 434 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

The clip skill provides image classification capabilities. It can analyze images and identify objects or concepts present within them, offering a textual description of the image's content. This allows AI agents to understand visual information and respond accordingly.

When to use it

  • Image understanding: When an agent needs to interpret the contents of an uploaded image (e.g., identifying a type of animal or object).
  • Content moderation: To automatically categorize images based on their content for safety or compliance purposes.
  • Accessibility: Describe images for visually impaired users.
  • Visual search: To enable searching by image content rather than just text keywords.

Key capabilities

  • Image classification
  • Object recognition
  • Content description

Example prompts

  • "Describe the contents of this image." (followed by an image upload)
  • "What objects are present in this picture?" (followed by an image upload)
  • “Can you tell me what’s depicted in this photograph?” (followed by an image upload)

Tips & gotchas

The quality of the description depends on the clarity and content of the input image. Complex or obscured images may yield less accurate results.