fix: chat evaluation bugs — task pipeline, prompt grounding, markdown rendering

Addresses 14 bugs from 3 rounds of deep chat evaluation:

- Add chat-to-task pipeline in agents.py with regex-based intent detection,
  agent extraction, priority extraction, and title cleaning
- Filter meta-questions ("how do I create a task?") from task creation
- Inject real-time date/time context into every chat message
- Inject live queue state when user asks about tasks
- Ground system prompts with agent roster, honesty guardrails, self-knowledge,
  math delegation template, anti-filler rules, values-conflict guidance
- Add CSS for markdown code blocks, inline code, lists, blockquotes in chat
- Add highlight.js CDN for syntax highlighting in chat responses
- Reduce small-model memory context budget (4000→2000) for expanded prompt
- Add 27 comprehensive tests covering the full chat-to-task pipeline

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

src/timmy/agent.py

@@ -118,8 +118,9 @@ def create_timmy(
         from timmy.memory_system import memory_system
         memory_context = memory_system.get_system_context()
         if memory_context:
-            # Truncate if too long (keep under token limit)
-            max_context = 4000 if not use_tools else 8000
+            # Truncate if too long — smaller budget for small models
+            # since the expanded prompt (roster, guardrails) uses more tokens
+            max_context = 2000 if not use_tools else 8000
             if len(memory_context) > max_context:
                 memory_context = memory_context[:max_context] + "\n... [truncated]"
             full_prompt = f"{base_prompt}\n\n## Memory Context\n\n{memory_context}"