Add query-type-aware source tiering and scoring

Detect query type (product/concept/opinion/how_to/comparison/breaking_news/
prediction) via lightweight regex patterns and use it for:

1. Source selection: each query type has tier-1 (always run) and tier-2
   (run if available) sources. Unlisted sources are opt-in only.
   Truth Social is always opt-in regardless of query type.

2. WebSearch penalty: varies by query type instead of flat -15pt.
   Concept queries get 0 penalty (web docs are authoritative),
   how_to gets 5pt, breaking_news gets 10pt, product/opinion get 15pt.

3. Tiebreaker ordering: source priority varies by query type.
   YouTube ranks first for how_to, Polymarket for prediction,
   HN for concept queries, X for breaking news.

All changes are backward-compatible: callers that don't pass query_type
get the original behavior (15pt penalty, Reddit > X > YouTube tiebreaker).

scripts/lib/score.py

@@ -4,6 +4,7 @@ import math
 from typing import List, Optional, Union
 
 from . import dates, schema
+from .query_type import QueryType, WEBSEARCH_PENALTY_BY_TYPE, TIEBREAKER_BY_TYPE
 
 # Score weights for Reddit/X (has engagement)
 WEIGHT_RELEVANCE = 0.45
@@ -642,19 +643,17 @@ def score_polymarket_items(items: List[schema.PolymarketItem]) -> List[schema.Po
     return items
 
 
-def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebSearchItem]:
+def score_websearch_items(items: List[schema.WebSearchItem], query_type: QueryType = None) -> List[schema.WebSearchItem]:
     """Compute scores for WebSearch items WITHOUT engagement metrics.
 
-    Uses reweighted formula: 55% relevance + 45% recency - 15pt source penalty.
-    This ensures WebSearch items rank below comparable Reddit/X items.
-
-    Date confidence adjustments:
-    - High confidence (URL-verified date): +10 bonus
-    - Med confidence (snippet-extracted date): no change
-    - Low confidence (no date signals): -20 penalty
+    Uses reweighted formula: 55% relevance + 45% recency - penalty.
+    Penalty varies by query type: concept queries get 0 penalty (web docs
+    are authoritative), while product/opinion queries get full 15pt penalty
+    (social discussion is more valuable).
 
     Args:
         items: List of WebSearch items
+        query_type: Query classification for penalty adjustment
 
     Returns:
         Items with updated scores
@@ -682,8 +681,9 @@ def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebS
             WEBSEARCH_WEIGHT_RECENCY * rec_score
         )
 
-        # Apply source penalty (WebSearch < Reddit/X for same relevance/recency)
-        overall -= WEBSEARCH_SOURCE_PENALTY
+        # Apply source penalty (varies by query type)
+        penalty = WEBSEARCH_PENALTY_BY_TYPE.get(query_type, WEBSEARCH_SOURCE_PENALTY) if query_type else WEBSEARCH_SOURCE_PENALTY
+        overall -= penalty
 
         # Apply date confidence adjustments
         # High confidence (URL-verified): reward with bonus
@@ -699,15 +699,33 @@ def score_websearch_items(items: List[schema.WebSearchItem]) -> List[schema.WebS
     return items
 
 
-def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.TikTokItem, schema.InstagramItem, schema.HackerNewsItem, schema.PolymarketItem]]) -> List:
+_ITEM_SOURCE_MAP = {
+    schema.RedditItem: "reddit",
+    schema.XItem: "x",
+    schema.YouTubeItem: "youtube",
+    schema.TikTokItem: "tiktok",
+    schema.InstagramItem: "instagram",
+    schema.HackerNewsItem: "hn",
+    schema.PolymarketItem: "polymarket",
+}
+_DEFAULT_TIEBREAKER = {"reddit": 0, "x": 1, "youtube": 2, "tiktok": 3, "instagram": 4, "hn": 5, "polymarket": 6, "web": 7}
+
+
+def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSearchItem, schema.YouTubeItem, schema.TikTokItem, schema.InstagramItem, schema.HackerNewsItem, schema.PolymarketItem]], query_type: QueryType = None) -> List:
     """Sort items by score (descending), then date, then source priority.
 
+    Source priority varies by query type: YouTube ranks first for how_to,
+    X ranks first for breaking_news, Polymarket ranks first for prediction.
+
     Args:
         items: List of items to sort
+        query_type: Query classification for tiebreaker adjustment
 
     Returns:
         Sorted items
     """
+    tiebreaker = TIEBREAKER_BY_TYPE.get(query_type, _DEFAULT_TIEBREAKER) if query_type else _DEFAULT_TIEBREAKER
+
     def sort_key(item):
         # Primary: score descending (negate for descending)
         score = -item.score
@@ -716,23 +734,9 @@ def sort_items(items: List[Union[schema.RedditItem, schema.XItem, schema.WebSear
         date = item.date or "0000-00-00"
         date_key = -int(date.replace("-", ""))
 
-        # Tertiary: source priority (Reddit > X > YouTube > TikTok > HN > Polymarket > WebSearch)
-        if isinstance(item, schema.RedditItem):
-            source_priority = 0
-        elif isinstance(item, schema.XItem):
-            source_priority = 1
-        elif isinstance(item, schema.YouTubeItem):
-            source_priority = 2
-        elif isinstance(item, schema.TikTokItem):
-            source_priority = 3
-        elif isinstance(item, schema.InstagramItem):
-            source_priority = 4
-        elif isinstance(item, schema.HackerNewsItem):
-            source_priority = 5
-        elif isinstance(item, schema.PolymarketItem):
-            source_priority = 6
-        else:  # WebSearchItem
-            source_priority = 7
+        # Tertiary: query-type-aware source priority
+        source_name = _ITEM_SOURCE_MAP.get(type(item), "web")
+        source_priority = tiebreaker.get(source_name, 99)
 
         # Quaternary: title/text for stability
         text = getattr(item, "title", "") or getattr(item, "text", "")