docs: AG Native bundle reverse engineering analysis — plannerResponse/Whi renderer structure, V8 cache fix, known-issues update

2026-04-12 07:05:57 +09:00
parent eef59e6bb2
commit 353265bed8
25 changed files with 1236 additions and 33 deletions
--- a/scratch_dom_classes.py
+++ b/scratch_dom_classes.py
@@ -0,0 +1,94 @@
+"""
+Modify observer script to dump the actual DOM structure around detected buttons
+and bot message containers. Write results to bridge/dom_structure.json
+"""
+import requests, json
+
+BASE = "http://127.0.0.1:34332"
+
+# The trick: use test-rpc endpoint to NOT call an RPC, but instead
+# post a probe via the /dump-html endpoint that our observer script 
+# will see as content.
+
+# Actually, better approach: Write a tiny probe script that the
+# observer should execute. But we can't inject new scripts at runtime.
+
+# BEST APPROACH: Read the actual HTML of the workbench to understand
+# what classes the AG Native React app renders.
+
+# Check the AG main JS files to understand class names
+import os, re
+
+ag_base = r"C:\Users\Variet-Worker\AppData\Local\Programs\Antigravity\resources\app\out"
+
+# The jetski agent JS is the main entry point
+jetski_dir = os.path.join(ag_base, "vs", "code", "electron-browser", "workbench")
+
+# Search for CSS class patterns in the built JS
+# Look for message/chat/conversation related classes
+search_patterns = [
+    r'message[-_]block',
+    r'bot[-_](?:message|color|response|turn)',
+    r'agent[-_](?:convo|message|response)',
+    r'chat[-_](?:body|message|content)',
+    r'markdown[-_]body',
+    r'text[-_]ide',
+    r'(?:pending|approval|approve)[-_]',
+    r'actions[-_]container',
+    r'tool[-_](?:call|action|result)',
+]
+
+# Search in the jetski JS bundle
+js_files = []
+for root, dirs, files in os.walk(ag_base):
+    for f in files:
+        if f.endswith('.js') and ('jetski' in f.lower() or 'agent' in f.lower()):
+            js_files.append(os.path.join(root, f))
+    # Don't recurse too deep
+    if root.count(os.sep) - ag_base.count(os.sep) > 5:
+        dirs.clear()
+
+print(f"Found {len(js_files)} jetski/agent JS files")
+for jf in js_files[:10]:
+    print(f"  {os.path.relpath(jf, ag_base)}: {os.path.getsize(jf)} bytes")
+
+# Search the main jetski bundle for relevant class patterns
+main_js = os.path.join(jetski_dir, "jetskiAgent.js")
+if os.path.exists(main_js):
+    content = open(main_js, encoding='utf-8', errors='replace').read()
+    print(f"\njetskiAgent.js: {len(content)} chars")
+    
+    # Find all CSS class-like strings
+    # Look for patterns like className:"something" or class:"something"
+    class_matches = re.findall(r'(?:className|class)\s*[:=]\s*["\']([^"\']{5,80})["\']', content)
+    
+    # Filter for conversation/message related
+    relevant = set()
+    for cls in class_matches:
+        lower = cls.lower()
+        if any(kw in lower for kw in ['message', 'chat', 'bot', 'agent', 'response', 
+                                        'markdown', 'convo', 'turn', 'approval',
+                                        'pending', 'action', 'tool', 'content',
+                                        'text-ide', 'block']):
+            relevant.add(cls)
+    
+    print(f"\nRelevant CSS classes ({len(relevant)}):")
+    for cls in sorted(relevant)[:50]:
+        print(f"  .{cls}")
+    
+    # Also search for data-testid patterns
+    testid_matches = re.findall(r'data-testid\s*[:=]\s*["\']([^"\']+)["\']', content)
+    if testid_matches:
+        print(f"\ndata-testid values ({len(testid_matches)}):")
+        for tid in sorted(set(testid_matches))[:30]:
+            print(f"  [{tid}]")
+    
+    # Search for the specific bot/assistant message container patterns  
+    for pat in search_patterns:
+        matches = re.findall(f'["\']([^"\']*{pat}[^"\']*)["\']', content, re.IGNORECASE)
+        if matches:
+            unique = sorted(set(matches))[:5]
+            print(f"\n  Pattern '{pat}': {unique}")
+
+else:
+    print(f"\njetskiAgent.js NOT FOUND at {main_js}")