xyva-yuangui Vision Reader
图片识别助手。触发后必须先询问用户确认，再执行分析。
| 触发场景 | 询问确认内容 |
|---|---|
| OCR 提取 | 确认图片路径、识别语言 (中文/英文)、输出格式 |
| 图片分析 | 确认分析深度 (简述/详细)、关注重点 |
| 截图理解 | 确认截图来源、需要提取的信息类型 |
| 文档识别 | 确认文档类型、是否保留格式 |
执行流程: 触发 → 询问用户 → 用户确认 → 执行识别 → 返回结果
Full docs: read SKILL-REFERENCE.md
Analyze images using AI vision models or local OCR.
Capabilities
- AI Vision Analysis — send image to vision model (qwen3.5-plus, qwen3-max) for understanding
- OCR Text Extraction — extract text from images using macOS Vision framework
- Screenshot Analysis — combine with screenshot-sender for screen understanding
- Document Reading — extract text from scanned documents, receipts, etc.
Method 1: AI Vision Model (Recommended)
Use vision-capable models directly. These models accept image input:
bailian/qwen3.5-plus— 1M context, supports["text", "image"]bailian/qwen3-max-2026-01-23— 262K context, supports["text", "image"]qwen-portal/vision-model— 128K context, supports["text", "image"]
When analyzing images, switch to one of these models if the current model doesn't support image input.
Workflow
- User provides image path or URL
- Agent reads the image file
- Agent sends to vision model with analysis prompt
- Return structured analysis
Method 2: Local OCR (macOS Vision Framework)
For pure text extraction without needing an AI model:
python3 {baseDir}/scripts/ocr_extract.py <image_path> [--lang zh-Hans,en] [--json]
Parameters
| Flag | Description | Default |
|---|---|---|
<image_path> |
Path to image file (PNG, JPG, etc.) | required |
--lang |
OCR languages (comma-separated) | zh-Hans,en |
--json |
Output as JSON with bounding boxes | plain text |
--confidence |
Min confidence threshold (0-1) | 0.5 |
Output (plain text mode)
第一行文字
第二行文字
...
Output (JSON mode)
{
"text": "完整提取文本",
"blocks": [
{"text": "第一行", "confidence": 0.98, "bbox": [x, y, w, h]},
...
],
"language": "zh-Hans"
}
Method 3: Tesseract OCR (Fallback)
If macOS Vision Framework is unavailable:
brew install tesseract tesseract-lang
tesseract <image_path> stdout -l chi_sim+eng
Workflow Examples
User says: "帮我看看这张图片上写了什么"
- Get image path
- Run OCR:
python3 {baseDir}/scripts/ocr_extract.py /path/to/image.png - Return extracted text
User says: "分析一下这个截图的内容"
- Get screenshot path (or take new one with screenshot-sender)
- Send to vision model (qwen3.5-plus) with prompt: "请详细描述这张截图的内容"
- Return model analysis
User says: "这个价格截图里哪个最便宜？"
- Run OCR to extract prices
- Send to vision model for comparison analysis
- Return structured comparison
Notes
- macOS Vision Framework requires macOS 10.15+
- For Chinese text, always include
zh-Hansin language list - Vision models are more accurate for complex analysis; OCR is faster for pure text
- Large images may be resized before sending to vision models