Qwen-VL Image Recognition

Problem

Many agents rely on text-only large language models as their reasoning engine. When users send screenshots, document photos, charts, QR codes, or tables, the model cannot directly read the image content and can only respond that it cannot see the image. Replacing the main model with a multimodal system may introduce higher cost, deployment overhead, and compatibility issues.

How it works

This skill adds a visual preprocessing layer between the agent and the text-only reasoning engine. When a user sends png, jpg, jpeg, webp, or bmp images and asks to inspect, analyze, summarize, or extract information, the skill calls Qwen-VL and converts the visual understanding result into structured Markdown, which the text model can then use for continued reasoning. It fits scenarios such as screenshot analysis, document recognition, chart interpretation, photo description, information extraction, and table extraction. It can switch between qwen-vl-plus and qwen-vl-max, and supports Chinese OCR, English OCR, chart understanding, and UI screenshot analysis.

Boundaries and notes

The skill requires network access and a valid DASHSCOPE_API_KEY, and only supports png/jpg/jpeg/webp/bmp formats. Missing files, unsupported formats, missing keys, network failures, timeouts, or corrupted images are reported with explicit error messages. It is suitable for lightweight deployment and users already using Bailian; if object detection, more fine-grained task modes, or other platform capabilities are needed, a more specialized Qwen-VL skill may be required. It is not suitable for fully offline environments or setups without an API key.