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Llava

Checked
davila7·vlatest·ai-ml·MIT

Llava allows you to generate detailed images from natural language prompts, offering a powerful way to visualize your ideas and concepts.

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 llava -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 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 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-llava/SKILL.md
Scanned
2 files, 461 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 llava skill allows AI agents to process and understand images alongside text prompts. It combines a large language model with a vision encoder, enabling agents to answer questions about image content, generate captions, or perform tasks based on visual information. This skill facilitates multimodal interactions, bridging the gap between textual understanding and visual perception.

When to use it

  • Image-based Q&A: When you need an agent to understand the contents of a photo and answer specific questions about what's depicted (e.g., "What kind of dog is in this picture?").
  • Visual Task Assistance: For tasks requiring visual input, such as identifying objects or describing scenes for accessibility purposes.
  • Caption Generation: To automatically generate descriptive captions for images.
  • Content Moderation: To analyze image content and flag potentially inappropriate material based on both visual and contextual cues.

Key capabilities

  • Image understanding
  • Text-image interaction
  • Question answering about images
  • Image captioning

Example prompts

  • "Describe the scene in this image." (followed by an image upload)
  • "What objects are present in this photo?" (followed by an image upload)
  • "Answer the question: 'Is there a cat?' based on this image." (followed by an image upload)

Tips & gotchas

The quality of results depends heavily on the clarity and resolution of the provided images. Ensure that the images are well-lit and in focus for optimal performance.