"""
Organic Optimizer - Evolve Efficiency
"""
from typing import Any, Dict
from skills.base import Skill, SkillMetadata, SkillResult, SkillContext, SkillCategory
from agents.model_router import query_reasoning

class OrganicOptimizerSkill(Skill):
    def metadata(self) -> SkillMetadata:
        return SkillMetadata(
            id="synthesized/organic-optimizer",
            name="Organic Optimizer",
            description="Evolve efficiency like biology, not linear math",
            category=SkillCategory.CODE,
        )

    def input_schema(self) -> Dict[str, Any]:
        return {"type": "object", "properties": {"system": {"type": "object"}, "fitness_function": {"type": "string"}}, "required": ["system"]}

    def execute(self, input_data: Dict[str, Any], context: SkillContext) -> SkillResult:
        system = input_data.get("system", {})
        fitness = input_data.get("fitness_function", "efficiency")

        prompt = f"""Apply biological optimization to this system:

System: {system}
Fitness: {fitness}

Use organic strategies:
1. Mutation - small random variations
2. Selection - keep what works
3. Recombination - combine successful traits
4. Adaptation - respond to environment
5. Emergence - allow complexity to arise

Generate 3 evolutionary generations:
- Gen 1: Current state + mutations
- Gen 2: Selected survivors + new mutations
- Gen 3: Emergent optimized form

This is not calculation. This is evolution."""

        result = query_reasoning(prompt, max_tokens=1000, timeout=120)
        return SkillResult(success=True, output={"evolution": result}, skill_id=self.metadata().id)
