feat: channel-level conversation analysis with compact formatting

Switch from per-user message batching to per-channel conversation
analysis. The LLM now sees the full interleaved conversation with
relative timestamps, reply chains, and consecutive message collapsing
instead of isolated flat text per user.

Key changes:
- Fix gpt-5-nano temperature incompatibility (conditional temp param)
- Add mention-triggered scan: users @mention bot to analyze recent chat
- Refactor debounce buffer from (channel_id, user_id) to channel_id
- Replace per-message analyze_message() with analyze_conversation()
  returning per-user findings from a single LLM call
- Add CONVERSATION_TOOL schema with coherence, topic, and game fields
- Compact message format: relative timestamps, reply arrows (→),
  consecutive same-user message collapsing
- Separate mention scan tasks from debounce tasks
- Remove _store_context/_get_context (conversation block IS the context)
- Escalation timeout config: [30, 60, 120, 240] minutes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

cogs/chat.py

@@ -153,6 +153,19 @@ class ChatCog(commands.Cog):
             if not content:
                 content = "(just pinged me)" if not is_proactive else message.content
 
+            # If a mention scan is running, await it so we can include findings
+            scan_summary = ""
+            if self.bot.user in message.mentions:
+                sentiment_cog = self.bot.get_cog("SentimentCog")
+                if sentiment_cog:
+                    task = sentiment_cog._mention_scan_tasks.get(message.channel.id)
+                    if task and not task.done():
+                        try:
+                            await asyncio.wait_for(asyncio.shield(task), timeout=45)
+                        except (asyncio.TimeoutError, asyncio.CancelledError):
+                            pass
+                    scan_summary = sentiment_cog._mention_scan_results.pop(message.id, "")
+
             # Add drama score context only when noteworthy
             drama_score = self.bot.drama_tracker.get_drama_score(message.author.id)
             user_data = self.bot.drama_tracker.get_user(message.author.id)
@@ -169,6 +182,10 @@ class ChatCog(commands.Cog):
             if user_notes:
                 extra_context += f"[Notes about {message.author.display_name}: {user_notes}]\n"
 
+            # Include mention scan findings if available
+            if scan_summary:
+                extra_context += f"[You just scanned recent chat. Results: {scan_summary}]\n"
+
             recent_user_msgs = []
             try:
                 async for msg in message.channel.history(limit=50, before=message):
@@ -239,8 +256,7 @@ class ChatCog(commands.Cog):
         # warnings/mutes appear before the chat reply
         sentiment_cog = self.bot.get_cog("SentimentCog")
         if sentiment_cog:
-            key = (message.channel.id, message.author.id)
-            task = sentiment_cog._debounce_tasks.get(key)
+            task = sentiment_cog._debounce_tasks.get(message.channel.id)
             if task and not task.done():
                 try:
                     await asyncio.wait_for(asyncio.shield(task), timeout=15)