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

scripts/lib/query.py

@@ -1,9 +1,5 @@
-"""Shared query utilities for /last30days search modules.
-
-Consolidates duplicated _extract_core_subject() logic from bird_x, reddit,
-youtube_yt, tiktok, instagram, bluesky, and scrapecreators_x into one
-parameterized function. Each platform calls with its own overrides.
-"""
+"""Shared query preprocessing utilities: noise-word stripping, core subject
+extraction, and compound term detection. Used by all search modules."""
 
 import re
 from typing import FrozenSet, List, Optional, Set
@@ -99,49 +95,6 @@ def extract_core_subject(
     return result.rstrip('?!.') if not max_words else (result or topic.lower().strip())
 
 
-# ---- Query type detection (heuristic, no LLM) ----
-
-_OPINION_SIGNALS = frozenset({
-    'worth', 'thoughts', 'opinion', 'opinions', 'review', 'reviews',
-    'recommend', 'recommendation', 'recommendations', 'should',
-    'anyone', 'anybody', 'experience', 'experiences',
-})
-
-_HOW_TO_SIGNALS = frozenset({
-    'how', 'setup', 'configure', 'install', 'tutorial', 'guide',
-    'step', 'steps', 'instructions',
-})
-
-_COMPARISON_SIGNALS = frozenset({
-    'vs', 'versus', 'compared', 'comparison', 'better', 'alternative',
-    'alternatives', 'difference', 'differences',
-})
-
-_PRODUCT_SIGNALS = frozenset({
-    'pricing', 'price', 'cost', 'plan', 'plans', 'tier', 'tiers',
-    'buy', 'purchase', 'subscription', 'trial', 'free',
-})
-
-
-def detect_query_type(topic: str) -> str:
-    """Classify query intent without an LLM.
-
-    Returns one of: "product", "concept", "opinion", "how_to", "comparison".
-    Used to adapt per-platform query construction.
-    """
-    words = set(topic.lower().split())
-
-    if words & _COMPARISON_SIGNALS:
-        return "comparison"
-    if words & _HOW_TO_SIGNALS or topic.lower().startswith("how "):
-        return "how_to"
-    if words & _OPINION_SIGNALS:
-        return "opinion"
-    if words & _PRODUCT_SIGNALS:
-        return "product"
-    return "concept"
-
-
 def extract_compound_terms(topic: str) -> List[str]:
     """Detect multi-word terms that should be quoted in search queries.