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/normalize.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, List, TypeVar, Union
 
 from . import dates, schema
 
-T = TypeVar("T", schema.RedditItem, schema.XItem, schema.WebSearchItem)
+T = TypeVar("T", schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem)
 
 
 def filter_by_date_range(
@@ -155,6 +155,51 @@ def normalize_x_items(
     return normalized
 
 
+def normalize_youtube_items(
+    items: List[Dict[str, Any]],
+    from_date: str,
+    to_date: str,
+) -> List[schema.YouTubeItem]:
+    """Normalize raw YouTube items to schema.
+
+    Args:
+        items: Raw YouTube items from yt-dlp
+        from_date: Start of date range
+        to_date: End of date range
+
+    Returns:
+        List of YouTubeItem objects
+    """
+    normalized = []
+
+    for item in items:
+        # Parse engagement
+        eng_raw = item.get("engagement", {})
+        engagement = schema.Engagement(
+            views=eng_raw.get("views"),
+            likes=eng_raw.get("likes"),
+            num_comments=eng_raw.get("comments"),
+        )
+
+        # YouTube dates are reliable (always YYYY-MM-DD from yt-dlp)
+        date_str = item.get("date")
+
+        normalized.append(schema.YouTubeItem(
+            id=item.get("video_id", ""),
+            title=item.get("title", ""),
+            url=item.get("url", ""),
+            channel_name=item.get("channel_name", ""),
+            date=date_str,
+            date_confidence="high",
+            engagement=engagement,
+            transcript_snippet=item.get("transcript_snippet", ""),
+            relevance=item.get("relevance", 0.7),
+            why_relevant=item.get("why_relevant", ""),
+        ))
+
+    return normalized
+
+
 def items_to_dicts(items: List) -> List[Dict[str, Any]]:
     """Convert schema items to dicts for JSON serialization."""
     return [item.to_dict() for item in items]