docs: tighten videodb listener guidance

skills/videodb/reference/capture-reference.md

@@ -383,8 +383,13 @@ For RTStream methods (indexing, transcription, alerts, batch config), see [rtstr
   │    active      │ ──> Start AI pipelines
   └───────┬──────────────┐
           │              │
-          │              └──────────────┐
-          │  client.stop_capture()      │ unrecoverable capture error
+          │              v
+          │      ┌───────────────┐     WebSocket: capture_session.failed
+          │      │    failed      │ ──> Inspect error payload and retry setup
+          │      └───────────────┘
+          │      unrecoverable capture error
+          │
+          │  client.stop_capture()
           v
   ┌───────────────┐     WebSocket: capture_session.stopping
   │   stopping     │ ──> Finalize streams
@@ -399,11 +404,4 @@ For RTStream methods (indexing, transcription, alerts, batch config), see [rtstr
   ┌───────────────┐     WebSocket: capture_session.exported
   │   exported     │ ──> Access video_id, stream_url, player_url
   └───────────────┘
-  
-  unrecoverable capture error
-          │
-          v
-  ┌───────────────┐     WebSocket: capture_session.failed
-  │    failed      │ ──> Inspect error payload and retry setup
-  └───────────────┘
 ```

skills/videodb/reference/generative.md

@@ -165,7 +165,7 @@ Combine scene extraction with text generation:
 from videodb import SceneExtractionType
 
 # First index scenes
-video.index_scenes(
+scenes = video.index_scenes(
     extraction_type=SceneExtractionType.time_based,
     extraction_config={"time": 10},
     prompt="Describe the visual content in this scene.",
@@ -173,11 +173,21 @@ video.index_scenes(
 
 # Get transcript for spoken context
 transcript_text = video.get_transcript_text()
+scene_descriptions = []
+for scene in scenes:
+    if isinstance(scene, dict):
+        description = scene.get("description") or scene.get("summary")
+    else:
+        description = getattr(scene, "description", None) or getattr(scene, "summary", None)
+    scene_descriptions.append(description or str(scene))
+
+scenes_text = "\n".join(scene_descriptions)
 
 # Analyze with collection LLM
 result = coll.generate_text(
     prompt=(
         f"Given this video transcript:\n{transcript_text}\n\n"
+        f"And these visual scene descriptions:\n{scenes_text}\n\n"
         "Based on the spoken and visual content, describe the main topics covered."
     ),
     model_name="pro",