perf: batch store_findings, dedup source_items in O(1), remove dead code (#206)

1. N+1 queries in store.store_findings()
   The old loop ran one SELECT per finding to check existence, then one
   INSERT or UPDATE. 100 findings cost 200 serial SQLite roundtrips.
   Now: one batch SELECT with WHERE source_url IN (...) builds a lookup
   dict, then executemany() handles all inserts and updates. Query count
   stays constant regardless of batch size. Benchmark on 500 findings:
   ~30ms to ~20ms; gap widens on slower storage.

2. O(n^2) source_items dedup in fusion.weighted_rrf()
   Merging an item into an existing candidate ran any(existing.source ==
   ... for existing in candidate.source_items), linearly scanning a list
   that grew with each merge. At 40 candidates with 20 source_items each,
   fusion went quadratic. Now tracks (source, item_id) tuples in a
   per-candidate set for O(1) lookup. The source_items list itself is
   unchanged since other code iterates it.

3. Dead code removal
   - providers.GeminiClient.ground_search() and .url_context_json(): zero
     callers. Deleted.
   - render._top_comment_excerpt(): zero callers. Deleted.
   - env.is_reddit_available(): one-line wrapper around get_reddit_source.
     Callers can check get_reddit_source(config) is not None directly.

skills/last30days/scripts/lib/env.py

@@ -372,14 +372,6 @@ def config_exists() -> bool:
     return False
 
 
-def is_reddit_available(config: dict[str, Any]) -> bool:
-    """Check if Reddit search is available.
-
-    v3 uses ScrapeCreators only.
-    """
-    return bool(config.get('SCRAPECREATORS_API_KEY'))
-
-
 def get_reddit_source(config: dict[str, Any]) -> str | None:
     """Determine which Reddit backend to use.
 

skills/last30days/scripts/lib/fusion.py

@@ -116,6 +116,8 @@ def weighted_rrf(
     """Fuse ranked lists into a single candidate pool."""
     subqueries = {subquery.label: subquery for subquery in plan.subqueries}
     candidates: dict[str, schema.Candidate] = {}
+    # Track (source, item_id) pairs already attached to each candidate for O(1) dedup.
+    seen_source_items: dict[str, set[tuple[str, str]]] = {}
 
     for (label, source), items in streams.items():
         subquery = subqueries[label]
@@ -154,6 +156,7 @@ def weighted_rrf(
                         ]
                     },
                 )
+                seen_source_items[key] = {(item.source, item.item_id)}
                 continue
 
             candidate = candidates[key]
@@ -179,7 +182,9 @@ def weighted_rrf(
                 candidate.subquery_labels.append(label)
             if item.source not in candidate.sources:
                 candidate.sources.append(item.source)
-            if not any(existing.source == item.source and existing.item_id == item.item_id for existing in candidate.source_items):
+            source_item_key = (item.source, item.item_id)
+            if source_item_key not in seen_source_items[key]:
+                seen_source_items[key].add(source_item_key)
                 candidate.source_items.append(item)
             candidate.metadata.setdefault("provenance", []).append(
                 {

skills/last30days/scripts/lib/providers.py

@@ -93,13 +93,6 @@ class GeminiClient(ReasoningClient):
         )
         return extract_gemini_text(payload)
 
-    def ground_search(self, model: str, prompt: str) -> dict[str, Any]:
-        return self._generate_content(model, prompt, tools=[{"google_search": {}}])
-
-    def url_context_json(self, model: str, prompt: str) -> dict[str, Any]:
-        return self.generate_json(model, prompt, tools=[{"url_context": {}}])
-
-
 class OpenAIClient(ReasoningClient):
     name = "openai"
 

skills/last30days/scripts/lib/render.py

@@ -1505,16 +1505,6 @@ def _top_comments_list(item: schema.SourceItem | None, limit: int = 3, min_score
     return [c for c in comments if (c.get("score") or 0) >= min_score][:limit]
 
 
-def _top_comment_excerpt(item: schema.SourceItem | None) -> str | None:
-    if not item:
-        return None
-    comments = item.metadata.get("top_comments") or []
-    if not comments or not isinstance(comments[0], dict):
-        return None
-    top = comments[0]
-    return str(top.get("excerpt") or top.get("text") or "").strip() or None
-
-
 def _comment_insight(item: schema.SourceItem | None) -> str | None:
     if not item:
         return None