"""
Unknown
=======

Synthesized skill

Gap Addressed: emotional_intelligence
Synthesized from: 1 agents
Confidence: 0.20
"""

from typing import Any, Dict, List
from skills.base import Skill, SkillMetadata, SkillResult, SkillContext, SkillCategory
from agents.model_router import query_creative, query_reasoning, query_synthesis

class SynthesizedSkill(Skill):
    """Auto-synthesized skill for emotional_intelligence"""

    IMPLEMENTATION = """
Core Approach: 

Patterns to Apply:
- Multidisciplinary framework combining psychological theories, behavioral economics, cognitive science, and social and cultural factors.
- Identifies mechanisms, contextual triggers, and structural influences driving misaligned decisions.

Model: qwen3
Gap: emotional_intelligence

Effective Prompt Patterns:
- What psychological theories can be applied to understand human decision-making?
- How can behavioral economics insights be incorporated into the analysis?
"""

    def metadata(self) -> SkillMetadata:
        return SkillMetadata(
            id="synth-emotional_intelligence-637cb0",
            name="Unknown",
            description="Synthesized skill",
            category=SkillCategory.CODE,
        )

    def capabilities(self) -> List[Dict]:
        return [
            
        ]

    def input_schema(self) -> Dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "task": {"type": "string", "description": "The task to perform"},
                "context": {"type": "object", "description": "Additional context"}
            },
            "required": ["task"]
        }

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

        prompt = f"""You are executing the Unknown skill.

Implementation guidance:
{self.IMPLEMENTATION}

Task: {task}
Context: {ctx}

Apply the patterns and approach described above to solve this task.
Be specific and actionable."""

        # Use synthesis model for complex gaps
        result = query_synthesis(prompt, max_tokens=1000, timeout=120)

        return SkillResult(
            success=bool(result),
            output=result,
            skill_id=self.metadata().id,
        )
