perf: optimize dedup, parallelize handle searches and enrichment

The dedup hot path recomputed normalize_text() 4 times per comparison
and recomputed item_text() on every inner-loop iteration. Pre-computing
n-gram sets and token sets into a _PreparedText cache cuts dedup time
by 6x (2.16s to 0.39s on 300 unique items).

Bird handle searches spawned one Node process per handle sequentially.
Now uses ThreadPoolExecutor so N handles run concurrently. Same pattern
applied to YouTube comment enrichment (was serial, Reddit was already
parallel) and the retry-thin-sources phase in the pipeline.

Clustering now pre-computes candidate text and uses prepared_similarity
for the O(n^2) grouping and MMR representative selection loops.

Minor: _is_wsl() cached with lru_cache, Bundle.add_items() uses
extend() instead of list concatenation.

End-to-end: 5.2s -> 3.7s (29% faster) on a typical 4-source query.

scripts/lib/schema.py

@@ -168,8 +168,7 @@ class RetrievalBundle:
 
     def add_items(self, label: str, source: str, items: list[SourceItem]) -> None:
         """Atomically append items to both items_by_source_and_query and items_by_source."""
-        existing = self.items_by_source_and_query.get((label, source), [])
-        self.items_by_source_and_query[(label, source)] = existing + items
+        self.items_by_source_and_query.setdefault((label, source), []).extend(items)
         self.items_by_source.setdefault(source, []).extend(items)