#!/usr/bin/env python3
"""
Google Ecosystem Mastery Sprint Launcher
=========================================
Spawns 5 parallel research agents to audit the complete Google ecosystem.
Produces a Capabilities Matrix for operational mastery.

Run: python google_ecosystem_sprint.py
"""

import sys
import os
import json
import time
from datetime import datetime

# Add genesis-system to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from blackboard import Blackboard, EntryType, TaskStatus

# Sprint configuration
SPRINT_ID = f"google_mastery_{int(time.time())}"
SPRINT_TASKS = [
    {
        "agent": "gemini_api_master",
        "track": "Gemini API Mastery",
        "description": "Audit all Gemini models (Flash, Pro, Ultra). Document pricing tiers, context windows, and multimodal capabilities. Create model comparison table and decision tree for best model per use case.",
        "deliverables": ["model_comparison.md", "cost_optimization_guide.md", "decision_tree.md"],
        "priority": 1,
        "tags": ["mastery", "gemini", "api", "research"]
    },
    {
        "agent": "cloud_run_master",
        "track": "Cloud Run / Vertex AI",
        "description": "Audit Cloud Run for serverless agent scaling. Evaluate Vertex AI Agent Builder vs DIY. Document cold start optimizations. Create deployment templates (Terraform/Pulumi).",
        "deliverables": ["scaling_playbook.md", "vertex_comparison.md", "deployment_templates/"],
        "priority": 1,
        "tags": ["mastery", "cloud-run", "vertex-ai", "infrastructure"]
    },
    {
        "agent": "search_grounding_master",
        "track": "Search Grounding",
        "description": "Test Google Search integration with Gemini. Document retrieval-augmented patterns. Compare vs custom embeddings (Qdrant). Build search grounding wrapper implementation.",
        "deliverables": ["search_grounding.py", "rag_guide.md", "hybrid_search_strategy.md"],
        "priority": 1,
        "tags": ["mastery", "search", "grounding", "rag"]
    },
    {
        "agent": "gcp_infrastructure_master",
        "track": "GCP Infrastructure",
        "description": "Audit GCP services for Genesis compatibility. Document IAM best practices. Evaluate Pub/Sub for agent messaging. Create cost analysis for $10/day budget allocation.",
        "deliverables": ["service_matrix.md", "security_playbook.md", "budget_allocation.md"],
        "priority": 2,
        "tags": ["mastery", "gcp", "infrastructure", "security"]
    },
    {
        "agent": "integration_patterns_master",
        "track": "Integration Patterns",
        "description": "Map n8n to GCP triggers. Document MCP to Cloud integrations. Create unified logging strategy. Build end-to-end demo prototype connecting all components.",
        "deliverables": ["workflow_templates.json", "bridge_architecture.md", "observability_dashboard.md", "demo_prototype.py"],
        "priority": 2,
        "tags": ["mastery", "integration", "n8n", "demo"]
    }
]

def init_sprint():
    """Initialize the Google Ecosystem Mastery Sprint."""
    print(f"[SPRINT] Initializing: {SPRINT_ID}")
    print(f"[SPRINT] Timestamp: {datetime.now().isoformat()}")
    
    bb = Blackboard()
    
    # Create sprint metadata entry
    sprint_meta = {
        "sprint_id": SPRINT_ID,
        "name": "Google Ecosystem Mastery Sprint",
        "tracks": len(SPRINT_TASKS),
        "started": datetime.now().isoformat(),
        "status": "active"
    }
    
    bb.write(
        entry_type=EntryType.DECISION,
        content=sprint_meta,
        author="sprint_controller",
        tags=["sprint", "google", "mastery"],
        confidence=1.0
    )
    
    # Create research tasks for each agent track
    task_ids = []
    for task in SPRINT_TASKS:
        task_content = {
            "sprint_id": SPRINT_ID,
            "track": task["track"],
            "description": task["description"],
            "deliverables": task["deliverables"],
            "priority": task["priority"],
            "assigned_agent": task["agent"]
        }
        
        task_id = bb.write(
            entry_type=EntryType.TASK,
            content=task_content,
            author="sprint_controller",
            status=TaskStatus.OPEN,
            tags=task["tags"],
            confidence=0.9
        )
        
        task_ids.append(task_id)
        print(f"[TASK] Created: {task_id[:12]} - {task['track']}")
    
    print(f"\n[SPRINT] {len(task_ids)} research tasks created")
    print(f"[SPRINT] Workers will auto-claim from Blackboard")
    
    return task_ids

def check_sprint_status():
    """Check the status of the sprint."""
    bb = Blackboard()
    stats = bb.stats()
    
    print(f"\n[SPRINT STATUS]")
    print(f"Total Entries: {stats['total_entries']}")
    print(f"By Type: {stats['by_type']}")
    print(f"By Status: {stats['by_status']}")
    
    # Count mastery tasks specifically
    mastery_tasks = bb.query(
        entry_type=EntryType.TASK,
        tags=["mastery"],
        limit=50
    )
    
    open_count = len([t for t in mastery_tasks if t.status == TaskStatus.OPEN])
    claimed_count = len([t for t in mastery_tasks if t.status == TaskStatus.CLAIMED])
    completed_count = len([t for t in mastery_tasks if t.status == TaskStatus.COMPLETED])
    
    print(f"\nMastery Tasks: {len(mastery_tasks)}")
    print(f"  Open: {open_count}")
    print(f"  Claimed: {claimed_count}")
    print(f"  Completed: {completed_count}")
    
    return {
        "total": len(mastery_tasks),
        "open": open_count,
        "claimed": claimed_count,
        "completed": completed_count
    }

def get_findings():
    """Get all findings from the sprint."""
    bb = Blackboard()
    
    findings = bb.query(
        entry_type=EntryType.FINDING,
        tags=["proof_of_work"],
        limit=100
    )
    
    print(f"\n[FINDINGS] {len(findings)} Proof of Work entries:")
    for f in findings:
        task_id = f.content.get("task_id", "unknown")[:12]
        agent = f.content.get("agent_id", "unknown")
        evidence_preview = f.content.get("evidence", "")[:100]
        print(f"  [{task_id}] {agent}: {evidence_preview}...")
    
    return findings

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser(description="Google Ecosystem Mastery Sprint")
    parser.add_argument("--init", action="store_true", help="Initialize sprint")
    parser.add_argument("--status", action="store_true", help="Check sprint status")
    parser.add_argument("--findings", action="store_true", help="Get sprint findings")
    args = parser.parse_args()
    
    if args.init:
        init_sprint()
    elif args.status:
        check_sprint_status()
    elif args.findings:
        get_findings()
    else:
        # Default: init and show status
        init_sprint()
        check_sprint_status()