Address review feedback: deduplicate query_type, clean unused imports, fix defaults

- Remove duplicate detect_query_type from query.py (divergent 5-type version);
  canonical 7-type version lives in query_type.py
- Fix reddit.py import to use query_type.detect_query_type
- Clean unused STOPWORDS/SYNONYMS/tokenize imports from youtube_yt, instagram,
  tiktok, scrapecreators_x, bird_x after relevance consolidation
- Fix _relevance_filter default from 0.7 to 0.0 (items without relevance
  should not silently pass the filter)
- Remove --dateafter from yt-dlp (returns 0 results for evergreen topics)
- Remove restrictSearchableAttributes from HN search (misses Ask/Show HN)
- Lower HN points filter from >5 to >2 (avoids filtering niche posts)
- Add error logging to select_openai_model HTTP failures
- Remove mise.toml and internal planning doc from repo
- Update module docstrings to describe current purpose, not migration history
- Update tests to import from canonical relevance module

tests/test_scrapecreators_x.py

@@ -6,26 +6,27 @@ from pathlib import Path
 
 sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
 from lib import scrapecreators_x
+from lib.relevance import tokenize as _tokenize
 
 
 class TestTokenize(unittest.TestCase):
     def test_lowercases(self):
-        tokens = scrapecreators_x._tokenize("Claude AI")
+        tokens = _tokenize("Claude AI")
         self.assertIn("claude", tokens)
 
     def test_strips_stopwords(self):
-        tokens = scrapecreators_x._tokenize("the best AI tool")
+        tokens = _tokenize("the best AI tool")
         self.assertNotIn("the", tokens)
         self.assertIn("best", tokens)  # 'best' is not a stopword in tokenizer
 
     def test_removes_single_char(self):
-        tokens = scrapecreators_x._tokenize("a b cd ef")
+        tokens = _tokenize("a b cd ef")
         self.assertNotIn("a", tokens)
         self.assertNotIn("b", tokens)
         self.assertIn("cd", tokens)
 
     def test_expands_synonyms(self):
-        tokens = scrapecreators_x._tokenize("ai research")
+        tokens = _tokenize("ai research")
         self.assertIn("artificial", tokens)
         self.assertIn("intelligence", tokens)