feat(hackernews): add Hacker News as 5th research source

Add HN search via free Algolia API (no key needed). Two-phase approach:
search for stories, then enrich top ones with comments. Integrated into
the full pipeline (normalize, score, dedupe, render) running in parallel
with Reddit/X/YouTube. Source priority: Reddit > X > HN > YouTube > Web.

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

scripts/lib/schema.py

@@ -207,6 +207,43 @@ class YouTubeItem:
         }
 
 
+@dataclass
+class HackerNewsItem:
+    """Normalized Hacker News item."""
+    id: str           # "HN1", "HN2", ...
+    title: str
+    url: str          # Original article URL
+    hn_url: str       # news.ycombinator.com/item?id=...
+    author: str       # HN username
+    date: Optional[str] = None
+    date_confidence: str = "high"  # Algolia provides exact timestamps
+    engagement: Optional[Engagement] = None  # points + num_comments
+    top_comments: List[Comment] = field(default_factory=list)
+    comment_insights: List[str] = field(default_factory=list)
+    relevance: float = 0.5
+    why_relevant: str = ""
+    subs: SubScores = field(default_factory=SubScores)
+    score: int = 0
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            'id': self.id,
+            'title': self.title,
+            'url': self.url,
+            'hn_url': self.hn_url,
+            'author': self.author,
+            'date': self.date,
+            'date_confidence': self.date_confidence,
+            'engagement': self.engagement.to_dict() if self.engagement else None,
+            'top_comments': [c.to_dict() for c in self.top_comments],
+            'comment_insights': self.comment_insights,
+            'relevance': self.relevance,
+            'why_relevant': self.why_relevant,
+            'subs': self.subs.to_dict(),
+            'score': self.score,
+        }
+
+
 @dataclass
 class Report:
     """Full research report."""
@@ -221,6 +258,7 @@ class Report:
     x: List[XItem] = field(default_factory=list)
     web: List[WebSearchItem] = field(default_factory=list)
     youtube: List[YouTubeItem] = field(default_factory=list)
+    hackernews: List[HackerNewsItem] = field(default_factory=list)
     best_practices: List[str] = field(default_factory=list)
     prompt_pack: List[str] = field(default_factory=list)
     context_snippet_md: str = ""
@@ -229,6 +267,7 @@ class Report:
     x_error: Optional[str] = None
     web_error: Optional[str] = None
     youtube_error: Optional[str] = None
+    hackernews_error: Optional[str] = None
     # Cache info
     from_cache: bool = False
     cache_age_hours: Optional[float] = None
@@ -248,6 +287,7 @@ class Report:
             'x': [x.to_dict() for x in self.x],
             'web': [w.to_dict() for w in self.web],
             'youtube': [y.to_dict() for y in self.youtube],
+            'hackernews': [h.to_dict() for h in self.hackernews],
             'best_practices': self.best_practices,
             'prompt_pack': self.prompt_pack,
             'context_snippet_md': self.context_snippet_md,
@@ -260,6 +300,8 @@ class Report:
             d['web_error'] = self.web_error
         if self.youtube_error:
             d['youtube_error'] = self.youtube_error
+        if self.hackernews_error:
+            d['hackernews_error'] = self.hackernews_error
         if self.from_cache:
             d['from_cache'] = self.from_cache
         if self.cache_age_hours is not None:
@@ -359,6 +401,31 @@ class Report:
                 score=y.get('score', 0),
             ))
 
+        # Reconstruct HackerNews items
+        hn_items = []
+        for h in data.get('hackernews', []):
+            eng = None
+            if h.get('engagement'):
+                eng = Engagement(**h['engagement'])
+            comments = [Comment(**c) for c in h.get('top_comments', [])]
+            subs = SubScores(**h.get('subs', {})) if h.get('subs') else SubScores()
+            hn_items.append(HackerNewsItem(
+                id=h['id'],
+                title=h['title'],
+                url=h.get('url', ''),
+                hn_url=h.get('hn_url', ''),
+                author=h.get('author', ''),
+                date=h.get('date'),
+                date_confidence=h.get('date_confidence', 'high'),
+                engagement=eng,
+                top_comments=comments,
+                comment_insights=h.get('comment_insights', []),
+                relevance=h.get('relevance', 0.5),
+                why_relevant=h.get('why_relevant', ''),
+                subs=subs,
+                score=h.get('score', 0),
+            ))
+
         return cls(
             topic=data['topic'],
             range_from=range_from,
@@ -371,6 +438,7 @@ class Report:
             x=x_items,
             web=web_items,
             youtube=youtube_items,
+            hackernews=hn_items,
             best_practices=data.get('best_practices', []),
             prompt_pack=data.get('prompt_pack', []),
             context_snippet_md=data.get('context_snippet_md', ''),
@@ -378,6 +446,7 @@ class Report:
             x_error=data.get('x_error'),
             web_error=data.get('web_error'),
             youtube_error=data.get('youtube_error'),
+            hackernews_error=data.get('hackernews_error'),
             from_cache=data.get('from_cache', False),
             cache_age_hours=data.get('cache_age_hours'),
         )