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Multimodal Llm

Checked
yonatangross·vlatest·ai-ml·MIT

Provides LLMs guidance and assistance for building AI and machine learning applications.

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 yonatangross/orchestkit --skill multimodal-llm -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

yonatangross

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 yonatangross/orchestkit at 7784b90c05.

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

Commit
7784b90c05
Manifest
src/skills/multimodal-llm/SKILL.md
Scanned
2 files, 374 lines
Copies in repo
2 — all of them scanned
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 integrates vision, audio, and video generation capabilities into AI agents using leading multimodal models. It enables tasks like image captioning and visual question answering, document understanding (including OCR and table extraction), speech-to-text transcription, text-to-speech synthesis, and AI video generation using providers such as Kling, Sora, Veo, and Runway. The skill leverages specific model IDs from Anthropic, OpenAI, Google, and Veo for these functionalities.

When to use it

  • Image Analysis: You need an agent to analyze images, identify objects, or answer questions about visual content.
  • Document Processing: Your workflow requires extracting data from PDFs, charts, or diagrams.
  • Audio Transcription: You want to automatically transcribe audio recordings into text.
  • Video Generation: You require assistance in generating videos using AI models like Kling, Sora, Veo, or Runway.
  • Voice Agent Development: Building a voice agent that requires both speech-to-text and text-to-speech capabilities.

Key capabilities

  • Image captioning and visual question answering (VQA)
  • Document Optical Character Recognition (OCR) and table extraction
  • Speech-to-Text transcription with speaker diarization
  • Text-to-Speech voice synthesis
  • AI video generation using Kling v3, Sora 2, Veo 3.1 (std/lite/fast tiers), and Runway Gen-4.5 (gen4_turbo)
  • Model selection based on cost optimization and image size limits

Example prompts

  • "Analyze this image [base64 encoded image data] and describe the objects present."
  • "Transcribe this audio file [audio file path/URL]."
  • "Generate a short video of [video description] using Veo in fast mode."
  • “Extract all tables from this PDF document [PDF file path].”

Tips & gotchas

  • Image Encoding: When sending images for analysis, ensure they are base64 encoded.
  • Image Resizing: Resize images before encoding to optimize performance and adhere to model limitations.
  • Model Selection: Carefully consider the provider based on accuracy requirements, cost constraints, and context window needs.