Blip 2 Vision Language
This skill uses Blip 2's image understanding to generate detailed captions and descriptions from images, boosting accessibility and content creation.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add blip-2-vision-language npx -- -y @trustedskills/blip-2-vision-language
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"blip-2-vision-language": {
"command": "npx",
"args": [
"-y",
"@trustedskills/blip-2-vision-language"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
The blip-2-vision-language skill integrates the BLIP-2 model to enable AI agents to perform advanced visual question answering and image captioning tasks. It allows agents to interpret complex visual data by combining a vision encoder with a large language model for precise, context-aware responses.
When to use it
- Analyze screenshots of software interfaces to identify UI elements or diagnose errors visually.
- Interpret diagrams, charts, or scientific illustrations to extract trends and explain underlying concepts.
- Generate descriptive captions for uploaded images to assist in accessibility or content tagging workflows.
- Solve visual puzzles or logic problems presented entirely through graphical representations.
Key capabilities
- Visual Question Answering: Answers specific questions about the content within an image.
- Image Captioning: Generates natural language descriptions summarizing the main subjects and actions in a picture.
- Multimodal Reasoning: Connects visual evidence with textual queries to derive accurate conclusions.
Example prompts
- "What is the value of the variable highlighted in red in this code screenshot?"
- "Describe the process flow shown in this diagram step-by-step."
- "Identify any safety hazards present in this photograph of a construction site."
Tips & gotchas
Ensure images are high-resolution to maximize the model's ability to discern fine details and text. Complex charts with dense data may require simplified prompts to get accurate summaries rather than raw data extraction.
Tags
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