kyrexis/quantum_skills.py

# kyrexis/quantum_skills.py
"""
Kyrexis Quantum Skills Engine — Advanced Quantum Capabilities

Strategic Planning · Risk Assessment · Innovation Generation · Decision Support
Complex Problem-Solving · Neural Network Optimization · Human-AI Collaboration
"""

from __future__ import annotations

import time
from dataclasses import dataclass
from typing import Any, Dict, List, Optional  # noqa: F401

import numpy as np


@dataclass
class QuantumSkill:
    """Quantum skill definition."""

    id: str
    name: str
    description: str
    quantum_cost: int
    fidelity: float
    cooldown: float
    last_used: float = 0.0


class QuantumSkillsEngine:
    """
    Kyrexis Quantum Skills Engine — 5 core quantum capabilities.

    Skills are simulation-grade: they use the Kyrexis core state
    (fidelity / growth rate / entanglement) to produce decision
    artifacts. Cooldowns and quantum-costs gate execution.
    """

    def __init__(self, kyrexis_core):
        self.core = kyrexis_core
        self.skills: Dict[str, QuantumSkill] = {}
        self.skill_history: List[Dict[str, Any]] = []
        self._init_skills()

    def _init_skills(self) -> None:
        """Initialize the quantum skill registry."""
        skills = [
            QuantumSkill(
                id="strategic_planning",
                name="Strategic Planning",
                description="Develop and execute complex strategic plans",
                quantum_cost=10,
                fidelity=0.95,
                cooldown=60,
            ),
            QuantumSkill(
                id="risk_assessment",
                name="Risk Assessment",
                description="Assess and mitigate risks across multiple dimensions",
                quantum_cost=8,
                fidelity=0.94,
                cooldown=45,
            ),
            QuantumSkill(
                id="innovation_generation",
                name="Innovation Generation",
                description="Generate innovative ideas and solutions",
                quantum_cost=12,
                fidelity=0.92,
                cooldown=30,
            ),
            QuantumSkill(
                id="decision_support",
                name="Decision Support",
                description="Data-driven insights and recommendations",
                quantum_cost=6,
                fidelity=0.97,
                cooldown=20,
            ),
            QuantumSkill(
                id="complex_problem_solving",
                name="Complex Problem Solving",
                description="Tackle complex problems with quantum analysis",
                quantum_cost=15,
                fidelity=0.93,
                cooldown=90,
            ),
        ]
        for skill in skills:
            self.skills[skill.id] = skill

    def execute_skill(self, skill_id: str, parameters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Execute a quantum skill (cooldown-gated, history-logged)."""
        parameters = parameters or {}
        if skill_id not in self.skills:
            return {"error": f"Skill '{skill_id}' not found"}

        skill = self.skills[skill_id]

        # Cooldown gate
        if time.time() - skill.last_used < skill.cooldown:
            remaining = skill.cooldown - (time.time() - skill.last_used)
            return {"error": f"Skill '{skill_id}' on cooldown", "cooldown_remaining": round(remaining, 1)}

        started = time.time()
        result = self._execute_skill_impl(skill_id, parameters)
        execution_time = time.time() - started

        skill.last_used = time.time()
        self.skill_history.append({
            "skill_id": skill_id,
            "timestamp": time.time(),
            "parameters": parameters,
            "result": result,
        })
        return {
            "skill": skill_id,
            "result": result,
            "fidelity": self.core.state.fidelity,
            "execution_time": round(execution_time, 4),
        }

    def _execute_skill_impl(self, skill_id: str, params: Dict[str, Any]) -> Dict[str, Any]:
        """Dispatch to the concrete skill implementation."""
        handlers = {
            "strategic_planning": self._strategic_planning,
            "risk_assessment": self._risk_assessment,
            "innovation_generation": self._innovation_generation,
            "decision_support": self._decision_support,
            "complex_problem_solving": self._complex_problem_solving,
        }
        handler = handlers.get(skill_id)
        if handler is None:
            return {"error": "Unknown skill"}
        return handler(params)

    # ─── Implementations ──────────────────────────────────────────────────
    def _strategic_planning(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """Strategic planning over a horizon using the core CAGR model."""
        horizon = int(params.get("horizon_years", 5))
        growth = (1 + self.core.state.growth_rate) ** horizon
        return {
            "horizon_years": horizon,
            "growth_factor": round(growth, 4),
            "strategies": [
                "Quantum-accelerated decision making",
                "Multiverse scenario analysis",
                "Temporal optimization",
            ],
            "confidence": 0.95,
        }

    def _risk_assessment(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """Risk register with exposure and mitigations."""
        risks = [
            {"id": "quantum_decay", "probability": 0.01, "impact": 0.8},
            {"id": "coherence_loss", "probability": 0.02, "impact": 0.9},
            {"id": "entanglement_break", "probability": 0.005, "impact": 1.0},
        ]
        return {
            "risks": risks,
            "total_risk": round(sum(r["probability"] * r["impact"] for r in risks), 4),
            "mitigations": [
                "Error correction",
                "Coherence maintenance",
                "Entanglement refresh",
            ],
        }

    def _innovation_generation(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """Generate innovation candidates for a domain."""
        domain = params.get("domain", "general")
        innovations = [
            "Quantum-enhanced AI reasoning",
            "Photonic DNA activation",
            "Temporal anomaly prediction",
            "Multiverse resource allocation",
        ]
        return {
            "domain": domain,
            "innovations": innovations,
            "novelty_score": 0.92,
            "feasibility": 0.85,
        }

    def _decision_support(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """Score decision options and recommend the best."""
        options = params.get("options", ["Option A", "Option B", "Option C"])
        rng = np.random.default_rng()
        scores = []
        for option in options:
            scores.append({
                "option": option,
                "score": round(float(rng.random() * 100), 2),
                "confidence": 0.95,
                "entanglement": self.core.state.fidelity,
            })
        return {
            "options": scores,
            "recommendation": max(scores, key=lambda x: x["score"]),
            "fidelity": self.core.state.fidelity,
        }

    def _complex_problem_solving(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """Decompose and solve a complex problem."""
        problem = params.get("problem", "unknown")
        complexity = float(params.get("complexity", 0.5))
        return {
            "problem": problem,
            "complexity": complexity,
            "solution": f"Quantum-optimized solution for {problem}",
            "steps": [
                "Quantum decomposition",
                "Parallel analysis",
                "Entanglement synthesis",
                "Coherence verification",
                "Solution optimization",
            ],
            "confidence": round(0.90 + (0.05 * self.core.state.fidelity), 4),
        }

    # ─── Status ───────────────────────────────────────────────────────────
    def get_skill_status(self) -> Dict[str, Any]:
        """Status of all skills (fidelity + cooldown remaining)."""
        now = time.time()
        return {
            "skills": [
                {
                    "id": s.id,
                    "name": s.name,
                    "description": s.description,
                    "fidelity": s.fidelity,
                    "quantum_cost": s.quantum_cost,
                    "cooldown_remaining": round(max(0.0, s.cooldown - (now - s.last_used)), 1),
                }
                for s in self.skills.values()
            ],
            "total_skills": len(self.skills),
            "history_count": len(self.skill_history),
        }