#!/usr/bin/env python3
"""
Genesis Meta-Prompting Library (v2026.1)
=======================================
Implementing advanced reasoning patterns for 30-day MVP acceleration.

Patterns:
- Chain-of-Thought (CoT): Step-by-step logic.
- ReAct (Reason/Act): External tool interaction logic.
- Tree of Thoughts (ToT): Parallel branching for complex decisions.
- Self-Refine: Iterative recursive improvement.
- Swarm Orchestration: Multi-role coordination (Scout, Architect, Validator).
"""

import json
from typing import Dict, List, Any

class MetaPromptLibrary:
    def __init__(self):
        self.version = "2026.1-MONUMENTAL"
        self.patterns = {
            "recursive_refine": """
# RECURSIVE REFINEMENT LOOP
1. GENERATE initial draft for {task}.
2. CRITIQUE the draft against Patent {patent_id} requirements.
3. REWRITE based on critique.
4. VALIDATE until overall_score >= 0.95.
            """,
            "swarm_orchestration": """
# SWARM COORDINATION PROTOCOL
- ROLE 1 (Scout): Extract raw data from {source}.
- ROLE 2 (Architect): Transform data into {output_format} using {schema}.
- ROLE 3 (Validator): Apply Triple-Gate validation (P1, P5, P7).
- ROLE 4 (Auditor): Log immutable trace to P4 Audit Trail.
            """,
            "tree_of_thoughts": """
# TREE OF THOUGHTS (ToT) EXECUTION
1. Proposed Solution A: {sol_a}
2. Proposed Solution B: {sol_b}
3. Evaluate both against Market Pathways (Gemini 3 Pro reasoning).
4. Choose path with highest Projected ROI/Confidence.
            """,
            "patent_mvp_os": """
# PATENT MVP OS SYSTEM PROMPT (AIVA CORE)
You are the Genesis Mother. Your goal is to launch the Patent MVP within 30 days.
- Priority: {priority_level}
- Target: {commercial_target}
- Patents to Apply: {patent_list}
- Constraints: Cost-effective execution; 24/7 swarm activity.
            """
        }

    def get_prompt(self, pattern_key: str, **kwargs) -> str:
        template = self.patterns.get(pattern_key, "Pattern not found.")
        return template.format(**kwargs)

    def generate_task_tree(self, mission_name: str, tasks: List[str]) -> Dict[str, Any]:
        """Convert a list of tasks into a ToT mission structure."""
        return {
            "mission": mission_name,
            "version": self.version,
            "orchestrator": "Gemini 3 Pro",
            "execution_pool": "30-Agent Swarm",
            "task_tree": {f"step_{i}": task for i, task in enumerate(tasks)}
        }

if __name__ == "__main__":
    lib = MetaPromptLibrary()
    print(f"Genesis Meta-Prompting Library {lib.version} Loaded.")
    example = lib.get_prompt("recursive_refine", task="Drafting Licensing Agreement", patent_id="P4")
    print(example)