#!/usr/bin/env python3 """ Generated Task Script for content_generation Complexity: 3/4 Estimated Duration: 1.1 hours Estimated Tokens: 65,000 """

from typing import List, Dict import subprocess import json

class TaskExecution: def init(self): self.primary_agent = "ai-curriculum-specialist" self.supporting_agents = ['assessment-creation-agent'] self.required_skills = ['ai-curriculum-development'] self.commands = [] self.execution_order = ['research', 'ai-curriculum-specialist', 'assessment-creation-agent'] self.progress = {}

def execute_phase(self, phase: str, agent: str, prompt: str) -> Dict:
    """Execute single phase with specified agent"""

    # Using Claude Code Task protocol from CLAUDE.md
    task_call = f"""

Task( subagent_type="general-purpose", description="{phase}", prompt="""Use {{agent}} subagent to {{prompt}}

Context:
- Task Type: content_generation
- Skill Levels: ['beginner', 'intermediate', 'advanced', 'expert']
- Modules: ['module3_deep_learning']
- Deliverables: ['content', 'assessments', 'notebooklm_optimization']

Requirements:
- Follow curriculum development best practices
- Create content with proper metadata for NotebookLM
- Include assessment integration
- Track progress with checkboxes

Report back:
- What was completed
- What remains to be done
- Current status and any blockers
- Recommendations for next steps
\"\"\"

)"""

    print(f"Executing Phase: {phase}")
    print(f"Agent: {agent}")
    print(f"Task Call:\n{task_call}")
    
    # In real implementation, this would invoke Claude Code
    # For now, return mock result
    result = {
        "phase": phase,
        "agent": agent,
        "status": "completed",
        "output": "Mock execution result",
        "next_steps": []
    }
    
    self.progress[phase] = result
    return result

def run_complete_workflow(self):
    """Execute complete workflow according to execution order"""
    
    results = []
    
    for step in self.execution_order:
        if "->" in step:
            # Complex orchestration step
            agent, action = step.split(" -> ")
            result = self.execute_orchestrated_phase(agent, action)
        else:
            # Simple agent invocation
            result = self.execute_simple_phase(step)
        
        results.append(result)
        
    return results

def execute_simple_phase(self, agent: str) -> Dict:
    """Execute simple single-agent phase"""
    
    phase_prompts = {
        "research": "Research and analyze requirements for curriculum development task",
        "ai-curriculum-specialist": "Generate comprehensive curriculum content with multi-level progression",
        "educational-content-generator": "Create engaging educational content with proper pedagogical frameworks",
        "assessment-creation-agent": "Design adaptive assessments with bias detection and accessibility features",
        "create-plan": "Create detailed project plan with checkboxes and progress tracking"
    }
    
    prompt = phase_prompts.get(agent, f"Execute {agent} workflow for curriculum development")
    return self.execute_phase(agent, agent, prompt)

def execute_orchestrated_phase(self, orchestrator: str, action: str) -> Dict:
    """Execute complex orchestrated phase"""

    orchestrator_prompt = f"""Act as project manager and {{action}}.

    Create comprehensive project plan with:
    - Detailed task breakdown with checkboxes
    - Agent assignment and coordination
    - Progress tracking and milestone management
    - Quality gates and validation steps
    - Token budget and timeline management

    Coordinate the following workflow:
    - Task Type: content_generation
    - Complexity: 3/4
    - Deliverables: ['content', 'assessments', 'notebooklm_optimization']
    - Timeline: 2-3 weeks
    """
    
    return self.execute_phase("orchestration", orchestrator, orchestrator_prompt)

def generate_progress_report(self) -> str:
    """Generate comprehensive progress report"""
    
    total_phases = len(self.execution_order)
    completed_phases = len([p for p in self.progress.values() if p["status"] == "completed"])
    
    report = f"""

Curriculum Development Progress Report

Task Overview

• Task Type: content_generation
• Complexity: 3/4
• Timeline: 2-3 weeks
• Progress: {completed_phases}/{total_phases} phases completed ({completed_phases/total_phases*100:.1f}%)

Execution Summary

"""

    for phase, result in self.progress.items():
        status_icon = "✅" if result["status"] == "completed" else "🔄" if result["status"] == "in_progress" else "❌"
        report += f"{status_icon} **{phase}**: {result['status']}\n"
    
    report += f"""

Resource Usage

• Estimated Tokens: 65,000
• Estimated Duration: 1.1 hours

Next Steps

• Continue with remaining phases in execution order

• Monitor token usage and adjust if needed

• Validate deliverables meet quality standards """

    return report

if name == "main": # Execute the generated task workflow executor = TaskExecution() results = executor.run_complete_workflow()

print("\n" + "="*60)
print("CURRICULUM DEVELOPMENT TASK COMPLETED")
print("="*60)

# Generate final report
report = executor.generate_progress_report()
print(report)

# Save results
with open("task_execution_results.json", "w") as f:
    json.dump({
        "task_requirements": {
            "task_type": "content_generation",
            "complexity": "3",
            "skill_levels": ['beginner', 'intermediate', 'advanced', 'expert'],
            "modules": ['module3_deep_learning'],
            "deliverables": ['content', 'assessments', 'notebooklm_optimization']
        },
        "recommendations": {
            "primary_agent": "ai-curriculum-specialist",
            "supporting_agents": ['assessment-creation-agent'],
            "required_skills": ['ai-curriculum-development'],
            "execution_order": ['research', 'ai-curriculum-specialist', 'assessment-creation-agent']
        },
        "execution_results": results,
        "progress": executor.progress
    }, f, indent=2)

print("\n📊 Detailed results saved to task_execution_results.json")