Coach AI

Instructions

brain_dump_tasks('''
Sprint work (due 11/18):
- URGENT: Smoke test GPU endpoints (2h)
- Deploy to UTest environment

Strategic (this month):
- Draft platform pitch outline (30min) [low effort]
- Research Platform One examples

Quick wins:
- Check ADO board
- Reply to emails
''')

Implementation Reference

• src/coach_ai/server.py:219-346 (handler)

  The 'brain_dump_tasks' tool handles parsing natural language task lists and optionally batch adding them to the system.

  async def brain_dump_tasks(
      text: str,
      auto_create: bool = True,
      default_priority: str = "medium",
      default_timeframe: Optional[str] = None,
  ) -> str:
      """ADHD-friendly brain dump: paste a list of tasks and get them into the system.
  
      Workflow:
      1. Parse natural language task list
      2. Optionally create all todos in batch
      3. Return summary with suggestions
  
      Supports multiple formats:
      - Bullet points: "- Task 1"
      - Numbered lists: "1. Task 2"
      - Checkboxes: "[ ] Task 3"
      - Section headers: "Sprint work:\\n- Task 4"
  
      Smart extraction:
      - Priority: "#high", "URGENT:", "!!"
      - Timeframe: "this week", "(this month)", "someday"
      - Time: "(30min)", "~2h"
      - Energy: "[high energy]", "[low effort]"
      - Theme: "#sprint", "@admin"
  
      Example:
      ```
      brain_dump_tasks('''
      Sprint work (due 11/18):
      - URGENT: Smoke test GPU endpoints (2h)
      - Deploy to UTest environment
  
      Strategic (this month):
      - Draft platform pitch outline (30min) [low effort]
      - Research Platform One examples
  
      Quick wins:
      - Check ADO board
      - Reply to emails
      ''')
      ```
  
      Args:
          text: Natural language task list
          auto_create: Create todos immediately (default: True)
          default_priority: Default priority for unparsed tasks
          default_timeframe: Default timeframe for unparsed tasks
  
      Returns:
          Summary of created tasks with suggestions
      """
      db = await storage.get_db()
  
      # Parse the text
      parse_result = parse_natural_language_task_list(
          text, default_priority, default_timeframe
      )
  
      parsed_todos = parse_result["parsed_todos"]
  
      if not auto_create:
          # Preview mode
          response = f"PREVIEW MODE - {parse_result['parse_summary']}\n\n"
          response += f"Would create {len(parsed_todos)} todos:\n"
          for todo in parsed_todos[:10]:  # Show first 10
              response += f"  • {todo['title']}"
              if todo.get('priority') != 'medium':
                  response += f" ({todo['priority']} priority)"
              if todo.get('timeframe'):
                  response += f" [{todo['timeframe']}]"
              response += "\n"
  
          if len(parsed_todos) > 10:
              response += f"  ... and {len(parsed_todos) - 10} more\n"
  
          response += "\nCall again with auto_create=True to create these todos."
          return response
  
      # Create todos in batch
      if parsed_todos:
          create_result = await add_todos_batch(parsed_todos, db, auto_categorize=True)
          created_todos = create_result["created_todos"]
          suggestions = create_result["suggestions"]
      else:
          return "No tasks parsed. Check formatting."
  
      # Build response
      response = f"✓ Created {len(created_todos)} tasks from brain dump\n\n"
  
      # Show created tasks by category
      by_priority = {"high": [], "medium": [], "low": []}
      quick_wins = []
  
      for todo in created_todos:
          if todo.get('quick') or (todo.get('time_estimate', 999) <= 30):
              quick_wins.append(todo)
          by_priority[todo.get('priority', 'medium')].append(todo)
  
      if by_priority['high']:
          response += f"High priority: {len(by_priority['high'])} tasks\n"
      if quick_wins:
          response += f"Quick wins: {len(quick_wins)} tasks\n"
  
      # Show suggestions
      if suggestions:
          response += "\nAuto-categorization:\n"
          for suggestion in suggestions[:5]:  # Limit to 5
              response += f"  • {suggestion}\n"
  
      # Identify tasks needing review
      needs_review = []
      for todo in created_todos:
          if not todo.get('timeframe'):
              needs_review.append(f"#{todo['id']}: {todo['title']} - no timeframe assigned")
          elif todo.get('time_estimate', 0) > 120:
              needs_review.append(
                  f"#{todo['id']}: {todo['title']} - large task ({todo['time_estimate']}min), consider breaking down"
              )
  
      if needs_review:
          response += f"\n⚠️ {len(needs_review)} tasks need review:\n"
          for review in needs_review[:3]:  # Show first 3
              response += f"  • {review}\n"
  
      response += f"\nRun 'start_my_day' to see which tasks are selected for today."
  
      return response