Process mixed text-and-image prompts into streamed multimodal model output.
npx skills add https://github.com/qq5855144/GitHubM --skill multimodal-llm-qq5855144
Please help me install this Agent Skill. Skill: multimodal-llm Source: https://github.com/qq5855144/GitHubM/tree/main/.skills/multimodal-llm Command: npx skills add https://github.com/qq5855144/GitHubM --skill multimodal-llm-qq5855144
This Skill removes the complexity of building multimodal AI integrations by providing a ready-to-use workflow for sending text and images to a large model and receiving streamed results.
Use the multimodal-llm skill to analyze the attached image with the user’s question and stream back the answer.
High-intent search queries and answers about installing and using this skill.
To stream multimodal model responses, you send mixed text-and-image prompts to a large model and parse the incrementally generated content over SSE. This approach provides responsive interfaces for visual question answering and assistant-driven content generation.
SSE streaming for image analysis supports web, mini program, and app environments. You must use platform-specific fetch clients to maintain stream handling and ensure secure server-side injection of the integration key across these workflows.
Handling base64 images involves encoding image data and sending it alongside natural language prompts to the multimodal model. This allows the model to perform visual analysis and instruction following while returning generated content incrementally.
Secure integration requires server-side injection of the integration key. By keeping the key on the server and routing mixed text-and-image prompts through a backend proxy, you protect credentials while delivering streamed SSE output to the client.
Multimodal LLM streaming works with screenshots by inspecting the attached image with the user's question and returning answers as they are generated. This enables support assistants to analyze visual content and stream responses back instantly.
SSE streams break when platform-specific fetch clients are not configured correctly. Web, mini program, and app environments require distinct client patterns to maintain the connection and properly receive the streamed multimodal model output.
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