Integrate shared query.py into per-source modules

Replace duplicated _extract_core_subject() in bird_x, reddit, youtube_yt,
tiktok, instagram, bluesky, and scrapecreators_x with thin wrappers that
delegate to query.extract_core_subject() with platform-specific noise sets.

Each module preserves its current behavior exactly:
- bird_x: max_words=5, strip_suffixes=True, full noise set
- youtube_yt: keeps tips/tricks/tutorial/guide/review (content types)
- reddit: preserves original smaller noise set
- tiktok/instagram: same small noise set
- bluesky/scrapecreators_x: minimal noise set

Existing tests pass without modification since _extract_core_subject()
still exists as a callable on each module.

scripts/lib/bird_x.py

@@ -54,56 +54,10 @@ def _extract_core_subject(topic: str) -> str:
 
     X search is literal keyword AND matching — all words must appear.
     Aggressively strip question/meta/research words to keep only the
-    core product/concept name (2-3 words max).
+    core product/concept name (max 5 words).
     """
-    text = topic.lower().strip()
+    from .query import NOISE_WORDS, extract_core_subject
-
+    return extract_core_subject(topic, max_words=5, strip_suffixes=True)
-    # Phase 1: Strip multi-word prefixes (longest first)
-    prefixes = [
-        'what are the best', 'what is the best', 'what are the latest',
-        'what are people saying about', 'what do people think about',
-        'how do i use', 'how to use', 'how to',
-        'what are', 'what is', 'tips for', 'best practices for',
-    ]
-    for p in prefixes:
-        if text.startswith(p + ' '):
-            text = text[len(p):].strip()
-            break
-
-    # Phase 2: Strip multi-word suffixes
-    suffixes = [
-        'best practices', 'use cases', 'prompt techniques',
-        'prompting techniques', 'prompting tips',
-    ]
-    for s in suffixes:
-        if text.endswith(' ' + s):
-            text = text[:-len(s)].strip()
-            break
-
-    # Phase 3: Filter individual noise words
-    _noise = {
-        # Question/filler words
-        'a', 'an', 'the', 'is', 'are', 'was', 'were', 'and', 'or',
-        'of', 'in', 'on', 'for', 'with', 'about', 'to',
-        'people', 'saying', 'think', 'said', 'lately',
-        # Research/meta descriptors
-        'best', 'top', 'good', 'great', 'awesome', 'killer',
-        'latest', 'new', 'news', 'update', 'updates',
-        'trendiest', 'trending', 'hottest', 'hot', 'popular', 'viral',
-        'practices', 'features', 'guide', 'tutorial',
-        'recommendations', 'advice', 'review', 'reviews',
-        'usecases', 'examples', 'comparison', 'versus', 'vs',
-        'plugin', 'plugins', 'skill', 'skills', 'tool', 'tools',
-        # Prompting meta words
-        'prompt', 'prompts', 'prompting', 'techniques', 'tips',
-        'tricks', 'methods', 'strategies', 'approaches',
-        # Action words
-        'using', 'uses', 'use',
-    }
-    words = text.split()
-    result = [w for w in words if w not in _noise]
-
-    return ' '.join(result[:3]) or topic.lower().strip()  # Max 3 words
 
 
 def is_bird_installed() -> bool:

scripts/lib/bluesky.py

@@ -67,26 +67,14 @@ def _create_session(handle: str, app_password: str) -> Optional[str]:
 
 def _extract_core_subject(topic: str) -> str:
     """Extract core subject from verbose query for Bluesky search."""
-    text = topic.lower().strip()
+    from .query import extract_core_subject
-    prefixes = [
+    _BSKY_NOISE = frozenset({
-        'what are the best', 'what is the best', 'what are the latest',
-        'what are people saying about', 'what do people think about',
-        'how do i use', 'how to use', 'how to',
-        'what are', 'what is', 'tips for', 'best practices for',
-    ]
-    for p in prefixes:
-        if text.startswith(p + ' '):
-            text = text[len(p):].strip()
-    noise = {
         'best', 'top', 'good', 'great', 'awesome',
         'latest', 'new', 'news', 'update', 'updates',
         'trending', 'hottest', 'popular', 'viral',
         'practices', 'features', 'recommendations', 'advice',
-    }
+    })
-    words = text.split()
+    return extract_core_subject(topic, noise=_BSKY_NOISE)
-    filtered = [w for w in words if w not in noise]
-    result = ' '.join(filtered) if filtered else text
-    return result.rstrip('?!.')
 
 
 def _parse_date(item: Dict[str, Any]) -> Optional[str]:

scripts/lib/instagram.py

@@ -99,25 +99,9 @@ def _compute_relevance(query: str, text: str, hashtags: List[str] = None) -> flo
 
 
 def _extract_core_subject(topic: str) -> str:
-    """Extract core subject from verbose query for Instagram search.
+    """Extract core subject from verbose query for Instagram search."""
-
+    from .query import extract_core_subject
-    Strips meta/research words to keep only the core product/concept name.
+    _INSTAGRAM_NOISE = frozenset({
-    """
-    text = topic.lower().strip()
-
-    # Strip multi-word prefixes
-    prefixes = [
-        'what are the best', 'what is the best', 'what are the latest',
-        'what are people saying about', 'what do people think about',
-        'how do i use', 'how to use', 'how to',
-        'what are', 'what is', 'tips for', 'best practices for',
-    ]
-    for p in prefixes:
-        if text.startswith(p + ' '):
-            text = text[len(p):].strip()
-
-    # Strip individual noise words
-    noise = {
         'best', 'top', 'good', 'great', 'awesome', 'killer',
         'latest', 'new', 'news', 'update', 'updates',
         'trending', 'hottest', 'popular', 'viral',
@@ -125,12 +109,8 @@ def _extract_core_subject(topic: str) -> str:
         'recommendations', 'advice',
         'prompt', 'prompts', 'prompting',
         'methods', 'strategies', 'approaches',
-    }
+    })
-    words = text.split()
+    return extract_core_subject(topic, noise=_INSTAGRAM_NOISE)
-    filtered = [w for w in words if w not in noise]
-
-    result = ' '.join(filtered) if filtered else text
-    return result.rstrip('?!.')
 
 
 def _log(msg: str):

scripts/lib/reddit.py

@@ -48,7 +48,9 @@ DEPTH_CONFIG = {
     },
 }
 
-# Stopwords for query extraction
+from .query import extract_core_subject as _query_extract
+
+# Reddit-specific noise words (preserves original smaller set)
 NOISE_WORDS = frozenset({
     'best', 'top', 'good', 'great', 'awesome', 'killer',
     'latest', 'new', 'news', 'update', 'updates',
@@ -82,24 +84,7 @@ def _extract_core_subject(topic: str) -> str:
 
     Strips meta/research words to keep only the core product/concept name.
     """
-    text = topic.lower().strip()
+    return _query_extract(topic, noise=NOISE_WORDS)
-
-    # Strip multi-word prefixes
-    prefixes = [
-        'what are the best', 'what is the best', 'what are the latest',
-        'what are people saying about', 'what do people think about',
-        'how do i use', 'how to use', 'how to',
-        'what are', 'what is', 'tips for', 'best practices for',
-    ]
-    for p in prefixes:
-        if text.startswith(p + ' '):
-            text = text[len(p):].strip()
-
-    words = text.split()
-    filtered = [w for w in words if w not in NOISE_WORDS]
-
-    result = ' '.join(filtered) if filtered else text
-    return result.rstrip('?!.')
 
 
 def expand_reddit_queries(topic: str, depth: str) -> List[str]:

scripts/lib/scrapecreators_x.py

@@ -66,26 +66,14 @@ def _compute_relevance(query: str, text: str) -> float:
 
 def _extract_core_subject(topic: str) -> str:
     """Extract core subject from verbose query for Twitter search."""
-    text = topic.lower().strip()
+    from .query import extract_core_subject
-    prefixes = [
+    _SC_X_NOISE = frozenset({
-        'what are the best', 'what is the best', 'what are the latest',
-        'what are people saying about', 'what do people think about',
-        'how do i use', 'how to use', 'how to',
-        'what are', 'what is', 'tips for', 'best practices for',
-    ]
-    for p in prefixes:
-        if text.startswith(p + ' '):
-            text = text[len(p):].strip()
-    noise = {
         'best', 'top', 'good', 'great', 'awesome',
         'latest', 'new', 'news', 'update', 'updates',
         'trending', 'hottest', 'popular', 'viral',
         'practices', 'features', 'recommendations', 'advice',
-    }
+    })
-    words = text.split()
+    return extract_core_subject(topic, noise=_SC_X_NOISE)
-    filtered = [w for w in words if w not in noise]
-    result = ' '.join(filtered) if filtered else text
-    return result.rstrip('?!.')
 
 
 def _log(msg: str):

scripts/lib/tiktok.py

@@ -99,25 +99,9 @@ def _compute_relevance(query: str, text: str, hashtags: List[str] = None) -> flo
 
 
 def _extract_core_subject(topic: str) -> str:
-    """Extract core subject from verbose query for TikTok search.
+    """Extract core subject from verbose query for TikTok search."""
-
+    from .query import extract_core_subject
-    Strips meta/research words to keep only the core product/concept name.
+    _TIKTOK_NOISE = frozenset({
-    """
-    text = topic.lower().strip()
-
-    # Strip multi-word prefixes
-    prefixes = [
-        'what are the best', 'what is the best', 'what are the latest',
-        'what are people saying about', 'what do people think about',
-        'how do i use', 'how to use', 'how to',
-        'what are', 'what is', 'tips for', 'best practices for',
-    ]
-    for p in prefixes:
-        if text.startswith(p + ' '):
-            text = text[len(p):].strip()
-
-    # Strip individual noise words
-    noise = {
         'best', 'top', 'good', 'great', 'awesome', 'killer',
         'latest', 'new', 'news', 'update', 'updates',
         'trending', 'hottest', 'popular', 'viral',
@@ -125,12 +109,8 @@ def _extract_core_subject(topic: str) -> str:
         'recommendations', 'advice',
         'prompt', 'prompts', 'prompting',
         'methods', 'strategies', 'approaches',
-    }
+    })
-    words = text.split()
+    return extract_core_subject(topic, noise=_TIKTOK_NOISE)
-    filtered = [w for w in words if w not in noise]
-
-    result = ' '.join(filtered) if filtered else text
-    return result.rstrip('?!.')
 
 
 def _log(msg: str):

scripts/lib/youtube_yt.py

@@ -110,26 +110,12 @@ def is_ytdlp_installed() -> bool:
 def _extract_core_subject(topic: str) -> str:
     """Extract core subject from verbose query for YouTube search.
 
-    Strips meta/research words to keep only the core product/concept name,
+    NOTE: 'tips', 'tricks', 'tutorial', 'guide', 'review', 'reviews'
-    similar to bird_x.py's approach.
+    are intentionally KEPT — they're YouTube content types that improve search.
     """
-    text = topic.lower().strip()
+    from .query import extract_core_subject
-
+    # YouTube-specific noise set: smaller than default, keeps content-type words
-    # Strip multi-word prefixes
+    _YT_NOISE = frozenset({
-    prefixes = [
-        'what are the best', 'what is the best', 'what are the latest',
-        'what are people saying about', 'what do people think about',
-        'how do i use', 'how to use', 'how to',
-        'what are', 'what is', 'tips for', 'best practices for',
-    ]
-    for p in prefixes:
-        if text.startswith(p + ' '):
-            text = text[len(p):].strip()
-
-    # Strip individual noise words
-    # NOTE: 'tips', 'tricks', 'tutorial', 'guide', 'review', 'reviews'
-    # are intentionally KEPT — they're YouTube content types that improve search
-    noise = {
         'best', 'top', 'good', 'great', 'awesome', 'killer',
         'latest', 'new', 'news', 'update', 'updates',
         'trending', 'hottest', 'popular', 'viral',
@@ -137,12 +123,8 @@ def _extract_core_subject(topic: str) -> str:
         'recommendations', 'advice',
         'prompt', 'prompts', 'prompting',
         'methods', 'strategies', 'approaches',
-    }
+    })
-    words = text.split()
+    return extract_core_subject(topic, noise=_YT_NOISE)
-    filtered = [w for w in words if w not in noise]
-
-    result = ' '.join(filtered) if filtered else text
-    return result.rstrip('?!.')
 
 
 def search_youtube(