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/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"