SuperCharged-Claude-Code-Upgrade/skills/ralph/__main__.py

#!/usr/bin/env python3
"""
Ralph Command Entry Point

Main entry point for the /ralph command in Claude Code.
This script is invoked when users run /ralph in the CLI.
"""

import os
import sys
import json
import argparse
from pathlib import Path

# Add current directory to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from ralph_agent_integration import RalphAgentIntegration, create_selection_request
from meta_agent_orchestrator import MetaAgent


def main():
    """Main entry point for /ralph command"""
    parser = argparse.ArgumentParser(
        description='RalphLoop - Autonomous agent iteration and orchestration',
        prog='ralph'
    )

    parser.add_argument(
        'task',
        nargs='*',
        help='Task description or requirements'
    )

    parser.add_argument(
        '--mode',
        choices=['single', 'multi', 'auto'],
        default='auto',
        help='Execution mode: single agent, multi-agent, or auto-detect'
    )

    parser.add_argument(
        '--workers',
        type=int,
        help='Number of worker agents (for multi-agent mode)'
    )

    parser.add_argument(
        '--delegate',
        action='store_true',
        help='Enable automatic agent delegation'
    )

    parser.add_argument(
        '--no-delegate',
        action='store_true',
        help='Disable automatic agent delegation'
    )

    parser.add_argument(
        '--proactive',
        action='store_true',
        help='Enable proactive agents'
    )

    parser.add_argument(
        '--status',
        action='store_true',
        help='Show Ralph status and exit'
    )

    parser.add_argument(
        '--list-agents',
        action='store_true',
        help='List all available agents and exit'
    )

    parser.add_argument(
        '--observability',
        action='store_true',
        help='Enable observability dashboard'
    )

    args = parser.parse_args()

    # Check environment variables for defaults
    multi_agent = os.getenv('RALPH_MULTI_AGENT', '').lower() == 'true'
    auto_delegate = os.getenv('RALPH_AUTO_DELEGATE', '').lower() == 'true'
    proactive_agents = os.getenv('RALPH_PROACTIVE_AGENTS', '').lower() == 'true'
    observability = os.getenv('RALPH_OBSERVABILITY_ENABLED', '').lower() == 'true'

    # Override with command line flags
    if args.delegate:
        auto_delegate = True
    elif args.no_delegate:
        auto_delegate = False

    if args.proactive:
        proactive_agents = True

    if args.observability:
        observability = True

    # Initialize Ralph integration
    integration = RalphAgentIntegration()

    # Handle special commands
    if args.status:
        status = integration.get_agent_status()
        print(json.dumps(status, indent=2))
        return 0

    if args.list_agents:
        agents = integration.registry.get_all_agents()
        print(f"\n=== Ralph Agents ({len(agents)} total) ===\n")

        by_category = {}
        for name, agent in agents.items():
            cat = agent.category.value
            if cat not in by_category:
                by_category[cat] = []
            by_category[cat].append((name, agent))

        for category, agent_list in sorted(by_category.items()):
            print(f"\n{category.upper()}:")
            for name, agent in agent_list:
                print(f"  - {name}: {agent.description[:80]}...")

        return 0

    # Get task from arguments or stdin
    if args.task:
        task = ' '.join(args.task)
    else:
        # Read from stdin if no task provided
        print("Enter your task (press Ctrl+D when done):")
        task = sys.stdin.read().strip()

    if not task:
        parser.print_help()
        return 1

    # Determine execution mode
    mode = args.mode
    if mode == 'auto':
        # Auto-detect based on task complexity
        complexity = integration.analyzer.estimate_complexity(task, [])
        if complexity >= 7.0 or multi_agent:
            mode = 'multi'
        else:
            mode = 'single'

    print(f"\n{'='*60}")
    print(f"RalphLoop: 'Tackle Until Solved'")
    print(f"{'='*60}")
    print(f"\nTask: {task[:100]}")
    print(f"Mode: {mode}")
    print(f"Auto-Delegate: {auto_delegate}")
    print(f"Proactive Agents: {proactive_agents}")
    print(f"\n{'='*60}\n")

    # Execute task
    try:
        if mode == 'multi':
            # Multi-agent orchestration
            print("🚀 Starting multi-agent orchestration...")

            orchestrator = MetaAgent()
            tasks = orchestrator.analyze_project(task)
            orchestrator.distribute_tasks(tasks)
            orchestrator.spawn_worker_agents(args.workers or int(os.getenv('RALPH_MAX_WORKERS', 12)))
            orchestrator.monitor_tasks()

            report = orchestrator.generate_report()
            print("\n=== EXECUTION REPORT ===")
            print(json.dumps(report, indent=2))

        else:
            # Single agent with optional delegation
            if auto_delegate:
                print("🔍 Analyzing task for agent delegation...\n")

                response = integration.process_user_message(task)

                print(f"\nAction: {response['action'].upper()}")
                if 'agent' in response:
                    agent_info = response['agent']
                    print(f"Agent: {agent_info['name']}")
                    print(f"Confidence: {agent_info.get('confidence', 0):.2%}")
                    if agent_info.get('reasons'):
                        print(f"Reasons:")
                        for reason in agent_info['reasons']:
                            print(f"  - {reason}")

                # Handle multi-agent workflow if appropriate
                workflow = integration.suggest_multi_agent_workflow(task)
                if len(workflow) > 1:
                    print(f"\n📋 Suggested Multi-Agent Workflow ({len(workflow)} phases):")
                    for i, step in enumerate(workflow, 1):
                        print(f"  {i}. [{step['phase']}] {step['agent']}: {step['task']}")

                    # Ask if user wants to proceed
                    print("\nWould you like to execute this workflow? (Requires multi-agent mode)")

            else:
                # Direct handling without delegation
                print("🎯 Processing task directly (no delegation)\n")
                print("Task would be processed by Claude directly.")

        print(f"\n{'='*60}")
        print(f"✅ Ralph execution complete")
        print(f"{'='*60}\n")

        return 0

    except KeyboardInterrupt:
        print("\n\n⚠️  Ralph interrupted by user")
        return 130
    except Exception as e:
        print(f"\n\n❌ Error: {e}")
        import traceback
        traceback.print_exc()
        return 1


if __name__ == '__main__':
    sys.exit(main())