vision-bridge

Route image analysis requests from text-only models to vision backends for structured reports.

6|1|Updated May 11, 2026
One-click install
npx skills add https://github.com/Guavafsl/vision-bridge-skills --skill vision-bridge
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: vision-bridge
Source: https://github.com/Guavafsl/vision-bridge-skills/tree/main
Command: npx skills add https://github.com/Guavafsl/vision-bridge-skills --skill vision-bridge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Vision Bridge delegates image analysis to an external vision model when the primary text model cannot view images, returning a structured report for the text model to review and act on.

Core Features & Use Cases

  • Two-stage workflow: vision analysis yields a structured text report, then the text model reviews and executes.
  • Domain and prompt context: supports optional domain context prepended to prompts and neutral system context for the vision model.
  • Flexible interaction: supports deep analysis, brief descriptions, custom questions, and verbose thinking traces.

Quick Start

Run python scripts/vision_bridge.py <image_path> to analyze an image and receive a structured visual report.

Frequently Asked Questions about vision-bridge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze images using a text-only model?

Image analysis for text-only models is handled by routing image requests to an external vision model, which returns a structured visual report for the text model to review and act on.

What environment variables do I need to configure for vision model routing?

Vision model routing requires configuring the VISION_API_KEY environment variable, alongside optional VISION_BASE_URL and VISION_MODEL settings to specify the backend and region.

Can I customize the image analysis prompt with specific domain context?

Domain context customization is fully supported, allowing you to prepend specific domain information and neutral system context to tailor the vision model's analysis prompts.

What is the two-stage workflow for multimodal image analysis?

The two-stage workflow first generates a structured text report from the vision analysis, then the primary text model reviews this report to execute subsequent actions or deep analysis.

How do I run an image analysis script from the command line?

Run python scripts/vision_bridge.py followed by the image path to execute the image analysis and receive a structured visual report directly in your terminal.

Does the vision bridge support flexible interaction styles for image analysis?

Flexible interaction supports deep analysis, brief descriptions, custom questions, and verbose thinking traces to provide varied levels of visual insight from the vision backend.