Fix v2 output quality: stats format, Reddit results, citations, summary structure

- Stats: replace BAD/GOOD examples with strict fill-in-the-blank template
- Reddit: add subreddit-targeted fallback search, soften scoring penalties
  (engagement -10→-3, date confidence -10→-5), add minimum result guarantee
- Citations: limit to 1 per insight, short format, no engagement metrics
- Summary: add bold topic headers template for structured paragraphs

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

scripts/last30days.py

@@ -114,6 +114,25 @@ def _search_reddit(
             except Exception:
                 pass
 
+    # Subreddit-targeted fallback if still < 3 results
+    if len(reddit_items) < 3 and not mock and not reddit_error:
+        sub_query = openai_reddit._build_subreddit_query(topic)
+        try:
+            sub_raw = openai_reddit.search_reddit(
+                config["OPENAI_API_KEY"],
+                selected_models["openai"],
+                sub_query,
+                from_date, to_date,
+                depth=depth,
+            )
+            sub_items = openai_reddit.parse_reddit_response(sub_raw)
+            existing_urls = {item.get("url") for item in reddit_items}
+            for item in sub_items:
+                if item.get("url") not in existing_urls:
+                    reddit_items.append(item)
+        except Exception:
+            pass
+
     return reddit_items, raw_openai, reddit_error
 
 
@@ -488,6 +507,13 @@ def main():
     deduped_reddit = dedupe.dedupe_reddit(sorted_reddit)
     deduped_x = dedupe.dedupe_x(sorted_x)
 
+    # Minimum result guarantee: if all Reddit results were filtered out but
+    # we had raw results, keep top 3 by relevance regardless of score
+    if not deduped_reddit and normalized_reddit:
+        print("[REDDIT WARNING] All results scored below threshold, keeping top 3 by relevance", file=sys.stderr)
+        by_relevance = sorted(normalized_reddit, key=lambda item: item.relevance, reverse=True)
+        deduped_reddit = by_relevance[:3]
+
     progress.end_processing()
 
     # Create report

scripts/lib/openai_reddit.py

@@ -103,6 +103,18 @@ def _extract_core_subject(topic: str) -> str:
     return ' '.join(result[:3]) or topic  # Keep max 3 words
 
 
+def _build_subreddit_query(topic: str) -> str:
+    """Build a subreddit-targeted search query for fallback.
+
+    When standard search returns few results, try searching for the
+    subreddit itself: 'r/kanye', 'r/howie', etc.
+    """
+    core = _extract_core_subject(topic)
+    # Remove dots and special chars for subreddit name guess
+    sub_name = core.replace('.', '').replace(' ', '').lower()
+    return f"r/{sub_name} site:reddit.com"
+
+
 def search_reddit(
     api_key: str,
     model: str,

scripts/lib/score.py

@@ -21,7 +21,7 @@ WEBSEARCH_NO_DATE_PENALTY = 20  # Heavy penalty for no date signals (low confide
 
 # Default engagement score for unknown
 DEFAULT_ENGAGEMENT = 35
-UNKNOWN_ENGAGEMENT_PENALTY = 10
+UNKNOWN_ENGAGEMENT_PENALTY = 3
 
 
 def log1p_safe(x: Optional[int]) -> float:
@@ -152,9 +152,9 @@ def score_reddit_items(items: List[schema.RedditItem]) -> List[schema.RedditItem
 
         # Apply penalty for low date confidence
         if item.date_confidence == "low":
-            overall -= 10
-        elif item.date_confidence == "med":
             overall -= 5
+        elif item.date_confidence == "med":
+            overall -= 2
 
         item.score = max(0, min(100, int(overall)))
 
@@ -212,9 +212,9 @@ def score_x_items(items: List[schema.XItem]) -> List[schema.XItem]:
 
         # Apply penalty for low date confidence
         if item.date_confidence == "low":
-            overall -= 10
-        elif item.date_confidence == "med":
             overall -= 5
+        elif item.date_confidence == "med":
+            overall -= 2
 
         item.score = max(0, min(100, int(overall)))