diff --git a/.gitignore b/.gitignore
index 5668042..78eca49 100644
--- a/.gitignore
+++ b/.gitignore
@@ -76,6 +76,11 @@ memory/L4_raw_sessions/*
 # Keychain
 !memory/keychain.py
 
+# Vision / OCR / UI detection tools
+!memory/ocr_utils.py
+!memory/vision_sop.md
+!memory/ui_detect.py
+
 # Visual Studio
 .vs/
 restore_commit.txt
diff --git a/memory/ocr_utils.py b/memory/ocr_utils.py
new file mode 100644
index 0000000..e296ed4
--- /dev/null
+++ b/memory/ocr_utils.py
@@ -0,0 +1,101 @@
+"""
+本地 OCR 工具
+- OCR引擎: rapidocr-onnxruntime (~1s/次, 中英文准确率高, 带bbox)
+- 坑(rapid): result[i][2] conf 是 str 不是 float
+- 坑(rapid): 无文字时 result 返回 None 而非空列表
+- 坑: enhance 放大+高对比度处理，对清晰文字有害，默认关闭
+- 坑(远程桌面): ImageGrab/mss 在 RDP 断开后截图全黑，用 ocr_window(hwnd) 代替
+"""
+import re
+from PIL import ImageGrab, Image, ImageEnhance
+
+_LANG = 'zh-Hans-CN'
+_rapid_engine = None
+
+def _get_rapid():
+    global _rapid_engine
+    if _rapid_engine is None:
+        from rapidocr_onnxruntime import RapidOCR
+        _rapid_engine = RapidOCR()
+    return _rapid_engine
+
+def _preprocess(img, scale=3, contrast=3.0):
+    img = ImageEnhance.Contrast(img).enhance(contrast)
+    img = img.resize((img.width * scale, img.height * scale))
+    return img
+
+def _strip_cjk_spaces(t):
+    return re.sub(r'(?<=[\u4e00-\u9fff])\s+(?=[\u4e00-\u9fff])', '', t)
+
+def _ocr_rapid(img):
+    import numpy as np
+    engine = _get_rapid()
+    arr = np.array(img)
+    result, elapse = engine(arr)
+    if not result:
+        return {'text': '', 'lines': [], 'details': []}
+    lines = [r[1] for r in result]
+    details = [{'bbox': r[0], 'text': r[1], 'conf': float(r[2])} for r in result]
+    text = _strip_cjk_spaces('\n'.join(lines))
+    return {'text': text, 'lines': [_strip_cjk_spaces(l) for l in lines], 'details': details}
+
+def ocr_image(image_input, lang=_LANG, enhance=False, engine=None):
+    """
+    对 PIL Image 做 OCR
+    :param image_input: PIL Image 对象 或 文件路径(str)
+    :param lang: 保留参数，当前未使用
+    :param enhance: 预处理
+    :param engine: 保留参数，当前仅支持 rapid/None
+    :return: dict {'text': 全文, 'lines': [行文本], 'details': [bbox+conf]}
+    """
+    if isinstance(image_input, str):
+        image_input = Image.open(image_input)
+    if enhance:
+        image_input = _preprocess(image_input)
+    if engine not in (None, 'rapid'):
+        raise ValueError("Only rapid OCR is supported")
+    return _ocr_rapid(image_input)
+
+def ocr_screen(bbox=None, lang=_LANG, enhance=False, engine=None):
+    """
+    截取屏幕区域并 OCR
+    :param bbox: (x1, y1, x2, y2) 像素坐标，None=全屏
+    :return: dict {'text': 全文, 'lines': [行文本], 'details': [bbox+conf](仅rapid)}
+    """
+    img = ImageGrab.grab(bbox=bbox)
+    return ocr_image(img, lang, enhance, engine)
+
+def ocr_window(hwnd, lang=_LANG, enhance=False, engine=None):
+    """
+    截取窗口并 OCR (使用 PrintWindow API，支持远程桌面断开场景)
+    :param hwnd: 窗口句柄(int)
+    :return: dict {'text': 全文, 'lines': [行文本], 'details': [bbox+conf](仅rapid)}
+    """
+    import win32gui, win32ui
+    from ctypes import windll
+    l, t, r, b = win32gui.GetWindowRect(hwnd)
+    w, h = r - l, b - t
+    hwndDC = win32gui.GetWindowDC(hwnd)
+    mfcDC = win32ui.CreateDCFromHandle(hwndDC)
+    saveDC = mfcDC.CreateCompatibleDC()
+    saveBitMap = win32ui.CreateBitmap()
+    saveBitMap.CreateCompatibleBitmap(mfcDC, w, h)
+    saveDC.SelectObject(saveBitMap)
+    windll.user32.PrintWindow(hwnd, saveDC.GetSafeHdc(), 3)
+    bmpinfo = saveBitMap.GetInfo()
+    bmpstr = saveBitMap.GetBitmapBits(True)
+    img = Image.frombuffer('RGB', (bmpinfo['bmWidth'], bmpinfo['bmHeight']), bmpstr, 'raw', 'BGRX', 0, 1)
+    win32gui.DeleteObject(saveBitMap.GetHandle())
+    saveDC.DeleteDC()
+    mfcDC.DeleteDC()
+    win32gui.ReleaseDC(hwnd, hwndDC)
+    return ocr_image(img, lang, enhance, engine)
+
+if __name__ == "__main__":
+    r = ocr_screen((0, 0, 400, 100))
+    print(f"识别结果: {r['text']}")
+    for line in r['lines']:
+        print(f"  行: {line}")
+    if 'details' in r:
+        for d in r['details']:
+            print(f"  [{d['conf']:.3f}] {d['text']}")
\ No newline at end of file
diff --git a/memory/ui_detect.py b/memory/ui_detect.py
new file mode 100644
index 0000000..d41dbbf
--- /dev/null
+++ b/memory/ui_detect.py
@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""
+极简UI元素检测脚本 - 基于OmniParser的YOLO模型
+依赖: ultralytics, rapidocr-onnxruntime, pillow, numpy
+"""
+import sys
+from pathlib import Path
+from ultralytics import YOLO
+from PIL import Image, ImageDraw
+import numpy as np
+
+DEFAULT_MODEL = str(Path(__file__).resolve().parent.parent / 'temp' / 'weights' / 'icon_detect' / 'model.pt')
+
+# 可选：使用rapidocr做OCR
+try:
+    from rapidocr_onnxruntime import RapidOCR
+    ocr_engine = RapidOCR()
+    HAS_OCR = True
+except ImportError:
+    HAS_OCR = False
+    print("警告: rapidocr未安装，跳过OCR功能")
+
+def detect_ui_elements(image_path, model_path=None, conf_threshold=0.25):
+    model_path = model_path or DEFAULT_MODEL
+    """检测UI元素并返回边界框"""
+    # 加载模型
+    model = YOLO(model_path)
+    
+    # 推理
+    results = model(image_path, conf=conf_threshold, verbose=False)
+    
+    # 提取检测结果
+    detections = []
+    for result in results:
+        boxes = result.boxes
+        for box in boxes:
+            x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+            conf = float(box.conf[0])
+            cls = int(box.cls[0])
+            detections.append({
+                'bbox': [int(x1), int(y1), int(x2), int(y2)],
+                'confidence': conf,
+                'class': cls
+            })
+    
+    return detections
+
+def ocr_text(image_path):
+    """OCR识别文本"""
+    if not HAS_OCR:
+        return []
+    
+    result, _ = ocr_engine(image_path)
+    if not result:
+        return []
+    
+    texts = []
+    for item in result:
+        bbox, text, conf = item
+        texts.append({
+            'text': text,
+            'bbox': bbox,
+            'confidence': conf
+        })
+    return texts
+
+def visualize(image_path, detections, ocr_results=None, output_path=None):
+    """可视化检测结果"""
+    img = Image.open(image_path)
+    draw = ImageDraw.Draw(img)
+    
+    # 画UI元素框（红色）
+    for det in detections:
+        x1, y1, x2, y2 = det['bbox']
+        draw.rectangle([x1, y1, x2, y2], outline='red', width=2)
+        draw.text((x1, y1-10), f"{det['confidence']:.2f}", fill='red')
+    
+    # 画OCR文本框（蓝色）
+    if ocr_results:
+        for ocr in ocr_results:
+            bbox = ocr['bbox']
+            points = [(bbox[i][0], bbox[i][1]) for i in range(4)]
+            draw.polygon(points, outline='blue')
+            draw.text((points[0][0], points[0][1]-10), ocr['text'][:10], fill='blue')
+    
+    if output_path:
+        img.save(output_path)
+    return img
+
+def main():
+    if len(sys.argv) < 2:
+        print("用法: python ui_detect.py <图片路径> <模型路径> [输出路径]")
+        print("示例: python ui_detect.py screenshot.png weights/icon_detect/model.pt output.png")
+        sys.exit(1)
+    
+    image_path = sys.argv[1]
+    model_path = sys.argv[2] if len(sys.argv) > 2 else DEFAULT_MODEL
+    output_path = sys.argv[3] if len(sys.argv) > 3 else "output.png"
+    
+    print(f"检测图片: {image_path}")
+    print(f"使用模型: {model_path}")
+    
+    # UI元素检测
+    print("\n[1/2] YOLO检测UI元素...")
+    detections = detect_ui_elements(image_path, model_path)
+    print(f"检测到 {len(detections)} 个UI元素")
+    for i, det in enumerate(detections, 1):
+        print(f"  {i}. bbox={det['bbox']}, conf={det['confidence']:.3f}")
+    
+    # OCR文本识别
+    ocr_results = None
+    if HAS_OCR:
+        print("\n[2/2] OCR识别文本...")
+        ocr_results = ocr_text(image_path)
+        print(f"识别到 {len(ocr_results)} 个文本区域")
+        for i, ocr in enumerate(ocr_results, 1):
+            print(f"  {i}. text='{ocr['text']}', conf={ocr['confidence']:.3f}")
+    
+    # 可视化
+    print(f"\n保存结果到: {output_path}")
+    visualize(image_path, detections, ocr_results, output_path)
+    
+    # 输出JSON格式结果
+    import json
+    result = {
+        'ui_elements': detections,
+        'ocr_texts': ocr_results or []
+    }
+    json_path = output_path.replace('.png', '.json')
+    with open(json_path, 'w', encoding='utf-8') as f:
+        json.dump(result, f, ensure_ascii=False, indent=2)
+    print(f"JSON结果: {json_path}")
+
+if __name__ == "__main__":
+    main()
diff --git a/memory/vision_sop.md b/memory/vision_sop.md
new file mode 100644
index 0000000..97e6938
--- /dev/null
+++ b/memory/vision_sop.md
@@ -0,0 +1,48 @@
+# Vision API SOP
+
+## ⚠️ 前置规则（必须遵守）
+
+1. **先枚举窗口**：调用 vision 前必须先用 `pygetwindow` 枚举窗口标题，确认目标窗口存在且已激活到前台。窗口不存在就不要截图。
+2. **🚫 禁止全屏截图**：必须先 `win32gui.GetWindowRect` 获取目标窗口坐标，再 `ImageGrab.grab(bbox=...)` 截窗口区域。能截局部（如标题栏）就不截整窗口，能截窗口就绝不全屏。全屏截图在任何场景下都不允许。
+3. **能不用 vision 就不用**：如果窗口标题/本地 OCR（`ocr_utils.py`）能获取所需信息，就不要调用 vision API，省 token 且更可靠。Vision 是最后手段。
+
+## 快速用法
+
+### 函数签名
+```python
+ask_vision(
+    image_input,
+    prompt: str | None = None,
+    timeout: int = 60,
+    max_pixels: int = 1_440_000,
+) -> str
+```
+
+### 示例
+```python
+from vision_api import ask_vision
+result = ask_vision("image.png", prompt="描述图片内容")  # 路径或PIL Image均可
+```
+返回 `str`：成功为模型回复，失败为 `Error: ...`。
+
+## 核心参数
+- `image_input`: 文件路径(str/Path) 或 PIL Image 对象
+- `prompt`: 提示词（默认：详细描述这张图片的内容）
+- `max_pixels`: 最大像素数（默认1440000，超则自动缩放）
+- `timeout`: 超时秒数（默认60）
+
+## 故障排除
+| 问题 | 解决方案 |
+|------|--------|
+| 导入失败 | 可检查 `../../mykey.py` 文件是否存在（仅检查存在性，不读取内容） |
+| 超时 | 提高 timeout 或降低 max_pixels |
+| 格式错误 | 确保使用 PIL 支持的格式（PNG/JPG/GIF等） |
+
+## 关键风险与坑点 (L3 Caveats)
+- **无重试机制**: `vision_api.py` 内部未实现 API 错误重试（如 503、超时）。在自动化流程中使用时，**必须在上层代码手动实现重试逻辑**（建议指数退避），否则偶发网络波动会导致任务直接崩溃中断。
+- **API Config**: 当前使用 `claude_config141`(ncode.vkm2.com, 已验证)。备选可用: `native_claude_config2/84/5535`。失效时直接改 `vision_api.py` 中的 `cfg = mk.claude_configXXX`。
+
+---
+更新: 2025-07-18 | 修复oai_config导入+返回值统一str
+更新: 2026-02-18 | 默认后端改为Claude原生API | SOP精简(删废话/水段/合并示例)
+更新: 2026-07 | 修复config(原claude_config8不存在)→改为claude_config141