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
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Variet Worker
160
.agents/workflows/helpers/analyze_dom.py
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160
.agents/workflows/helpers/analyze_dom.py
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"""Analyze AG Native DOM structure to find AI response containers."""
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import json, os, sys
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def load_dump():
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bridge = os.path.join(os.path.expanduser('~'), '.gemini', 'antigravity', 'bridge')
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# Try deep-inspect result first, then dump_html
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for fname in ['deep-inspect-result.json', 'dump_html.json']:
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fpath = os.path.join(bridge, fname)
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if os.path.exists(fpath):
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print(f"Loading: {fname} ({os.path.getsize(fpath)} bytes)")
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with open(fpath, 'r', encoding='utf-8-sig') as f:
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return json.load(f), fname
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return None, None
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def find_text_containers(node, path="", depth=0, results=None):
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"""Recursively find nodes with substantial text content (potential AI response containers)."""
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if results is None:
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results = []
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if not isinstance(node, dict):
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return results
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tag = node.get('tag', '')
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cls = node.get('cls', '')
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text = node.get('text', '')
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attrs = node.get('attrs', {})
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children = node.get('children', [])
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cur_path = f"{path}/{tag}"
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if cls:
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short_cls = cls[:60]
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cur_path += f".{short_cls}"
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# Look for nodes with long text (potential AI responses)
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if text and len(text) > 50:
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results.append({
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'path': cur_path,
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'depth': depth,
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'tag': tag,
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'cls': cls[:100],
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'text_len': len(text),
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'text_preview': text[:120],
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'attrs': {k:v for k,v in attrs.items() if k not in ('style',)}
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})
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for child in children:
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find_text_containers(child, cur_path, depth+1, results)
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return results
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def find_by_class_pattern(node, patterns, path="", depth=0, results=None):
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"""Find nodes matching class patterns."""
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if results is None:
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results = []
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if not isinstance(node, dict):
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return results
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tag = node.get('tag', '')
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cls = node.get('cls', '')
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attrs = node.get('attrs', {})
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children = node.get('children', [])
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text = node.get('text', '')
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cur_path = f"{path}/{tag}"
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for pattern in patterns:
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if pattern.lower() in cls.lower() or pattern.lower() in str(attrs).lower():
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child_count = len(children)
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results.append({
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'path': cur_path,
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'depth': depth,
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'tag': tag,
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'cls': cls[:150],
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'pattern': pattern,
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'text_preview': text[:80] if text else '',
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'child_count': child_count,
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'attrs': {k:v[:50] for k,v in attrs.items() if k != 'style'}
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})
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for child in children:
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find_by_class_pattern(child, patterns, cur_path, depth+1, results)
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return results
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def analyze_chat_structure(node, path="", depth=0):
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"""Find the chat/conversation area by looking at the main layout."""
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if not isinstance(node, dict):
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return
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tag = node.get('tag', '')
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cls = node.get('cls', '')
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children = node.get('children', [])
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text = node.get('text', '')
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attrs = node.get('attrs', {})
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# Print interesting structural nodes at shallow depths
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if depth <= 6:
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child_count = len(children)
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has_text = bool(text and len(text) > 10)
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info = f"{' '*depth}{tag}"
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if cls:
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info += f" .{cls[:80]}"
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if attrs:
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attr_str = ' '.join(f'{k}={v[:30]}' for k,v in attrs.items() if k not in ('style','class'))
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if attr_str:
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info += f" [{attr_str}]"
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info += f" children={child_count}"
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if has_text:
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info += f" text=\"{text[:50]}...\""
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print(info)
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for child in children:
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analyze_chat_structure(child, f"{path}/{tag}", depth+1)
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data, fname = load_dump()
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if not data:
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print("No dump file found!")
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sys.exit(1)
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# Handle both dump formats
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body = data.get('body', data)
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qi = data.get('quickInfo', {})
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print("=" * 60)
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print("QUICK INFO")
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print("=" * 60)
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if qi:
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for k, v in qi.items():
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if k == 'buttons':
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print(f"buttons ({len(v)}):")
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for b in v[:15]:
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print(f" [{b.get('tag')}] \"{b.get('text','')[:50]}\" visible={b.get('visible')} cls={b.get('cls','')[:60]}")
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elif k == 'dataAttrs':
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print(f"dataAttrs: {v[:30]}")
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else:
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print(f"{k}: {v}")
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print("\n" + "=" * 60)
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print("CHAT-RELATED CLASS PATTERNS")
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print("=" * 60)
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patterns = ['chat', 'message', 'conversation', 'response', 'answer', 'reply',
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'markdown', 'prose', 'content', 'panel', 'agent', 'assistant',
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'planner', 'step', 'trajectory', 'bot', 'ai-', 'turn']
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matches = find_by_class_pattern(body, patterns)
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for m in matches:
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print(f" [{m['tag']}] cls=\"{m['cls']}\" pattern={m['pattern']} children={m['child_count']} {m.get('attrs',{})}")
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print("\n" + "=" * 60)
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print("LONG TEXT NODES (potential AI responses)")
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print("=" * 60)
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texts = find_text_containers(body)
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texts.sort(key=lambda x: x['text_len'], reverse=True)
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for t in texts[:20]:
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print(f" [{t['tag']}] depth={t['depth']} len={t['text_len']} cls=\"{t['cls'][:60]}\"")
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print(f" text: \"{t['text_preview']}\"")
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if t['attrs']:
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print(f" attrs: {t['attrs']}")
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print("\n" + "=" * 60)
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print("DOM TREE (depth<=6)")
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print("=" * 60)
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analyze_chat_structure(body)
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