diff --git a/SKILL.md b/SKILL.md
index 6cb4e6c..96eca42 100644
--- a/SKILL.md
+++ b/SKILL.md
@@ -375,7 +375,7 @@ Common patterns:
 - Always active: Reddit, Hacker News, Polymarket
 - If gh CLI is installed (check `which gh`): add GitHub
 - If AUTH_TOKEN/CT0 or XAI_API_KEY or FROM_BROWSER is set: add X
-- If yt-dlp is installed (check `which yt-dlp`): add YouTube
+- If yt-dlp is installed (check `which yt-dlp`): add YouTube AND Podcasts
 - If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains tiktok: add TikTok
 - If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains instagram: add Instagram
 - If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains threads: add Threads
@@ -615,6 +615,27 @@ Store as `RESOLVED_IG_CREATORS`.
 
 Store as `RESOLVED_YT_QUERIES`.
 
+**6. Podcast channels** — **INFER 6-12 YouTube podcast channel @handles from topic knowledge.** Think in two dimensions:
+
+1. **Domain podcasts** — What YouTube podcasts focus on this topic's domain?
+   - Hip-hop/music → `DrinkChamps,JoeBuddenTV,BreakfastClubPower1051FM,OfficialFlagrant`
+   - Tech/AI/startups → `lexfridman,DwarkeshPatel,AllInPod,MyFirstMillionPod,LennysPodcast`
+   - Business/finance → `AcquiredFM,InvestLikeTheBest,PatrickBoyleOnFinance,PropGPod`
+   - Sports → `PatMcAfeeShowOfficial,ShannonSharpe,ClubShayShay`
+   - Culture/celebs → `joerogan,CallHerDaddy,ClubShayShay`
+   - Knitting/crafts → `FruityKnitting,VeryPinkKnits,GroceryGirlsKnit`
+
+2. **Cross-domain podcasts** — What popular interview/deep-dive podcasts might cover this topic even if it's not their main focus?
+   - Business-adjacent topics → `AcquiredFM,InvestLikeTheBest` (company deep dives)
+   - Tech-adjacent topics → `lexfridman,AllInPod` (broad tech interviews)
+   - Culture-adjacent topics → `joerogan,OfficialFlagrant` (celebrity interviews)
+
+**Rationale:** The engine uses these channels for transcript-first discovery. Even if the topic isn't in an episode title, it may be discussed within the episode. Acquired's "The NFL" episode mentions Taylor Swift 18 times, ESPN 117 times — invisible to YouTube search but found by transcript scanning.
+
+**Handle accuracy:** Return your best guess at the exact @handle. If wrong, the engine falls back to a search-based lookup. Don't stress the exact spelling — `@AcquiredFM`, `@lexfridman`, `@joerogan` work; `@FLAGRANT` fails but falls back to find `@OfficialFlagrant`.
+
+Store as `RESOLVED_PODCAST_CHANNELS` (comma-separated, no @ prefix).
+
 **Concrete examples:**
 
 | Topic | WebSearches needed | Reddit subs | TikTok hashtags | TikTok creators | IG creators | YT queries |
@@ -635,6 +656,7 @@ Resolved:
 - Reddit: r/{sub1}, r/{sub2}, r/{sub3}
 - TikTok: #{hashtag1}, #{hashtag2}
 - YouTube: {query1}, {query2}
+- Podcasts: @{channel1}, @{channel2}, @{channel3}
 ```
 
 Only show lines for platforms where something was resolved. Skip empty lines. This display replaces the old "Parsed intent" block with something more useful.
@@ -760,6 +782,7 @@ fi
 - `--ig-creators={RESOLVED_IG_CREATORS}` (from Step 0.55)
 - `--github-user={RESOLVED_GITHUB_USER}` (from Step 0.5b, person topics only)
 - `--github-repo={RESOLVED_GITHUB_REPOS}` (from Step 0.5c, product/project topics only)
+- `--podcast-channels={RESOLVED_PODCAST_CHANNELS}` (from Step 0.55, 6-12 @handles)
 - Omit any flag where the value was not resolved (empty).
 
 **If you skipped Steps 0.55 and 0.75 (no WebSearch -- OpenClaw, Codex, etc.), add:**
diff --git a/scripts/lib/pipeline.py b/scripts/lib/pipeline.py
index 0b50eca..17487da 100644
--- a/scripts/lib/pipeline.py
+++ b/scripts/lib/pipeline.py
@@ -124,7 +124,10 @@ def available_sources(config: dict[str, Any], requested_sources: list[str] | Non
         available.append("pinterest")
     if env.is_xquik_available(config):
         available.append("xquik")
-    if podcast_yt.is_available() and ("podcasts" in include_sources or (requested_sources and "podcasts" in requested_sources)):
+    # Podcasts: available whenever yt-dlp is installed (same as YouTube).
+    # Opt-out only. The source returns empty when no channels are resolved,
+    # so there's no cost to having it available.
+    if podcast_yt.is_available():
         available.append("podcasts")
     return available
 
diff --git a/scripts/lib/podcast_yt.py b/scripts/lib/podcast_yt.py
index 23b7bad..ab64e18 100644
--- a/scripts/lib/podcast_yt.py
+++ b/scripts/lib/podcast_yt.py
@@ -214,12 +214,52 @@ def _fetch_captions(video_id: str, temp_dir: str) -> Optional[str]:
         return None
 
 
+_NOISE_WORDS = frozenset({
+    "the", "a", "an", "of", "and", "or", "for", "to", "in", "on", "at",
+    "best", "top", "new", "latest", "review", "news", "vs", "versus",
+    "album", "song", "episode", "podcast", "interview", "this", "that",
+    "what", "how", "why", "where", "when", "who",
+})
+
+
+def _extract_key_terms(topic: str) -> List[str]:
+    """Extract meaningful terms from topic for matching.
+
+    For "Kanye West Bully album" -> ["Kanye West", "Bully"] or similar.
+    For single words, just returns the word.
+    """
+    words = [w.strip() for w in topic.split() if w.strip()]
+    # Remove noise words
+    meaningful = [w for w in words if w.lower() not in _NOISE_WORDS and len(w) > 2]
+    if not meaningful:
+        return [topic.strip()]
+
+    # If the topic has 2+ meaningful words, also include the full phrase
+    # and the first 2 words as a potential entity name
+    terms = []
+    if len(meaningful) >= 2:
+        # Full phrase first (for exact entity matches like "Taylor Swift")
+        terms.append(" ".join(meaningful[:2]))
+    terms.extend(meaningful)
+    return terms
+
+
 def _count_mentions(text: str, topic: str) -> int:
-    """Count case-insensitive topic mentions in text."""
-    # Build a regex pattern from the topic words
-    # For multi-word topics like "Taylor Swift", search for the full phrase
-    pattern = re.escape(topic.strip())
-    return len(re.findall(pattern, text, re.IGNORECASE))
+    """Count case-insensitive topic mentions in text.
+
+    Uses the maximum mention count across key terms extracted from the topic.
+    "Kanye West Bully album" -> max mentions of ["Kanye West", "Kanye", "West", "Bully"].
+    This way, an episode mentioning "Kanye" 85 times counts as 85, not 0.
+    """
+    text_lower = text.lower()
+    terms = _extract_key_terms(topic)
+    max_count = 0
+    for term in terms:
+        pattern = re.escape(term.lower())
+        count = len(re.findall(pattern, text_lower))
+        if count > max_count:
+            max_count = count
+    return max_count
 
 
 def _extract_mention_context(text: str, topic: str, max_excerpts: int = 3) -> List[str]: