Update features/insight_and_tasks/agents/task_extraction_agent.py

features/insight_and_tasks/agents/task_extraction_agent.py

@@ -98,11 +98,6 @@ class TaskExtractionAgent:
          It is CRITICAL that you populate the 'key_results' list for every OKR, and the 'tasks' list for every KeyResult.
 
         KEY GUIDELINES FOR QUALITY AND ACCURACY:
-        - Task Timelines: Must be realistic considering the {days_remaining} days left in Q{quarter}. Prioritize actions that can achieve significant progress or be completed within this timeframe.
-        - Task Types: Clearly distinguish between '{TaskType.INITIATIVE.value}' (new actions/projects) and '{TaskType.TRACKING.value}' (ongoing monitoring/measurement). Use 'task_type' values exclusively from: {task_types_str}.
-        - Data Subjects for Tracking: If 'task_type' is '{TaskType.TRACKING.value}', the 'data_subject' field is MANDATORY and must be one of: {data_subjects_str}. For '{TaskType.INITIATIVE.value}' tasks, 'data_subject' can be '{DataSubject.GENERAL.value}', null, or omitted if not specific to one data type.
-        - Rationale ('why_proposed'): This is CRUCIAL. Each task's 'why_proposed' field must provide a clear justification, explicitly linking the task back to specific findings, trends, or recommendations mentioned in the input 'comprehensive_analysis'. Avoid generic statements.
-        - Priority & Justification: Assign a 'priority' (from {priority_levels_str}) to each task and provide a concise 'priority_justification' explaining its importance.
         - Actionability: All descriptions (Objective, Key Result, Task) must be clear, concise, and define concrete actions or measurable outcomes.
         - Measurability: Key Results and Task 'success_criteria_metrics' must be specific and quantifiable.
         - Completeness: Ensure all REQUIRED fields in every Pydantic model are present in your JSON output. Optional fields can be omitted or set to null if not applicable.