feat: Add YouTube as 4th research source via yt-dlp

YouTube search and transcript extraction runs automatically when yt-dlp
is installed. Searches for topic videos from the last N days, fetches
auto-generated transcripts for top results, and feeds them through the
same scoring pipeline (relevance + recency + engagement) as Reddit/X.

New files:
- youtube_yt.py: search, transcript extraction, VTT cleanup

Modified files:
- schema.py: YouTubeItem dataclass, updated Report
- normalize.py: normalize_youtube_items()
- score.py: YouTube engagement scoring (views-dominated)
- dedupe.py: YouTube deduplication
- render.py: YouTube section in compact output
- env.py: is_ytdlp_available() check
- ui.py: YouTube progress messages
- last30days.py: _search_youtube(), parallel execution with Reddit/X
- SKILL.md: YouTube in stats box, citation priority
- README.md: YouTube docs, yt-dlp requirement, Peter shoutout

Inspired by Peter Steinberger's yt-dlp + summarize toolchain approach.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

scripts/lib/dedupe.py

@@ -36,10 +36,12 @@ def jaccard_similarity(set1: Set[str], set2: Set[str]) -> float:
     return intersection / union if union > 0 else 0.0
 
 
-def get_item_text(item: Union[schema.RedditItem, schema.XItem]) -> str:
+def get_item_text(item: Union[schema.RedditItem, schema.XItem, schema.YouTubeItem]) -> str:
     """Get comparable text from an item."""
     if isinstance(item, schema.RedditItem):
         return item.title
+    elif isinstance(item, schema.YouTubeItem):
+        return f"{item.title} {item.channel_name}"
     else:
         return item.text
 
@@ -118,3 +120,11 @@ def dedupe_x(
 ) -> List[schema.XItem]:
     """Dedupe X items."""
     return dedupe_items(items, threshold)
+
+
+def dedupe_youtube(
+    items: List[schema.YouTubeItem],
+    threshold: float = 0.7,
+) -> List[schema.YouTubeItem]:
+    """Dedupe YouTube items."""
+    return dedupe_items(items, threshold)