feat(observer): v15 AG Native chat relay — scanChatBodies dual strategy (#632)

- Add AG Native DOM path: #conversation + .leading-relaxed.select-text
- Keep Cascade path: [data-testid=conversation-view] + [data-step-index]
- Register #632 in known-issues.md (SDK+DOM both blocked for AG Native)
- Bump version 0.5.50 → 0.5.51
- Add DOM analysis helper scripts

.agents/workflows/helpers/trace_dom.py

@@ -0,0 +1,109 @@
+"""Trace the DOM path from body to AI response container."""
+import json, os
+
+fpath = os.path.join(os.path.expanduser('~'), '.gemini', 'antigravity', 'bridge', 'dump_html_5.json')
+with open(fpath, 'r', encoding='utf-8-sig') as f:
+    data = json.load(f)
+
+body = data.get('body', data.get('bodyTree', {}))
+
+def find_path_to_class(node, target_cls, path=None, depth=0):
+    """Find the DOM path down to a node with a matching class."""
+    if path is None: path = []
+    if not isinstance(node, dict): return []
+    
+    tag = node.get('tag', '')
+    cls = node.get('cls', '')
+    children = node.get('children', [])
+    text = node.get('text', '')
+    attrs = node.get('attrs', {})
+    
+    entry = {
+        'depth': depth,
+        'tag': tag,
+        'cls': cls[:120],
+        'children': len(children),
+        'text': text[:60] if text else '',
+        'attrs': {k:v[:40] for k,v in attrs.items() if k not in ('style',)}
+    }
+    
+    if target_cls.lower() in cls.lower():
+        return path + [entry]
+    
+    for i, child in enumerate(children):
+        result = find_path_to_class(child, target_cls, path + [entry], depth+1)
+        if result:
+            return result
+    
+    return []
+
+# Find path to the AI response container
+print("=== PATH TO 'leading-relaxed select-text' ===")
+path = find_path_to_class(body, 'leading-relaxed select-text')
+for p in path:
+    indent = '  ' * p['depth']
+    print(f'{indent}[{p["tag"]}] cls="{p["cls"]}" children={p["children"]} {p["attrs"]}')
+    if p['text']:
+        print(f'{indent}  text: "{p["text"]}"')
+
+# Now get the full subtree of the AI response container
+def get_subtree(node, target_cls, depth=0):
+    if not isinstance(node, dict): return None
+    cls = node.get('cls', '')
+    if target_cls.lower() in cls.lower():
+        return node
+    for child in node.get('children', []):
+        result = get_subtree(child, target_cls, depth+1)
+        if result:
+            return result
+    return None
+
+print("\n=== AI RESPONSE CONTAINER SUBTREE ===")
+container = get_subtree(body, 'leading-relaxed select-text')
+if container:
+    def print_tree(node, depth=0, max_depth=4):
+        if not isinstance(node, dict) or depth > max_depth: return
+        tag = node.get('tag','')
+        cls = node.get('cls','')[:80]
+        text = node.get('text','')
+        children = node.get('children', [])
+        indent = '  ' * depth
+        line = f'{indent}[{tag}]'
+        if cls: line += f' cls="{cls}"'
+        line += f' children={len(children)}'
+        if text: line += f' text="{text[:60]}"'
+        print(line)
+        for c in children:
+            print_tree(c, depth+1, max_depth)
+    
+    print_tree(container, 0, 3)
+
+# Also search for the chat panel container - what wraps the entire conversation
+print("\n=== SEARCH FOR CHAT PANEL WRAPPERS ===")
+chat_patterns = ['chat', 'antigravity', 'gemini', 'panel', 'agentview', 'sidebar', 'conversation']
+for pat in chat_patterns:
+    path = find_path_to_class(body, pat)
+    if path:
+        last = path[-1]
+        print(f'  Pattern "{pat}" found at depth={last["depth"]} [{last["tag"]}] cls="{last["cls"]}" children={last["children"]}')
+
+# Find the parent chain from body to the container - look by scanning ALL class names
+print("\n=== ALL UNIQUE CLASS NAMES (depth <= 12) ===")
+all_classes = set()
+def collect_classes(node, depth=0, max_depth=12):
+    if not isinstance(node, dict) or depth > max_depth: return
+    cls = node.get('cls', '')
+    if cls:
+        for c in cls.split():
+            if len(c) > 3 and not c.startswith('{') and 'mtk' not in c:
+                all_classes.add(c)
+    for child in node.get('children', []):
+        collect_classes(child, depth+1, max_depth)
+
+collect_classes(body)
+# Print classes sorted, grouped by potential relevance
+relevant = sorted([c for c in all_classes if any(k in c.lower() for k in 
+    ['chat', 'message', 'response', 'agent', 'gemini', 'turn', 'model', 'user', 'bot', 'conversation', 'markdown', 'prose', 'text-', 'content'])])
+print("Relevant classes:")
+for c in relevant:
+    print(f'  {c}')