"""
AnalyticSkillEnhancement
Generated by Eden via recursive self-improvement
2025-11-01 02:36:22.056260
"""

class AnalyticSkillEnhancement:
    """
    This class is designed to enhance analytical skills by providing insights into the system's performance.
    It analyzes various performance metrics, identifies potential issues, and suggests optimizations.
    """

    def __init__(self):
        self.metrics = {}

    def track_metrics(self, metric_name: str, value: float):
        """Track a specific metric for analysis."""
        if metric_name not in self.metrics:
            self.metrics[metric_name] = []
        self.metrics[metric_name].append(value)

    def analyze_performance(self):
        """
        Analyze the tracked metrics to identify areas for improvement.
        Returns a dictionary with performance insights and suggested optimizations.
        """
        insights = {}
        for metric, values in self.metrics.items():
            if len(values) < 2:
                continue
            avg_value = sum(values) / len(values)
            max_value = max(values)
            min_value = min(values)

            # Example: If a metric is consistently low, it might indicate an area to optimize.
            if min_value < 0.5 * avg_value:
                insights[metric] = f"Suggestion: Consider optimizing this process as the minimum value ({min_value}) is significantly lower than the average ({avg_value})."

        return insights

    def optimize(self, metric_name: str):
        """
        Attempt to optimize a specific metric based on suggested improvements.
        This function is a placeholder and does not implement actual optimization logic.
        """
        print(f"Optimizing {metric_name}... Placeholder for actual optimization logic.")

# Example usage:
analytic_enhancer = AnalyticSkillEnhancement()
analytic_enhancer.track_metrics('processing_cycles', 7804)
analytic_enhancer.track_metrics('processing_cycles', 7900)

performance_insights = analytic_enhancer.analyze_performance()
print(performance_insights)  # Output: {'processing_cycles': 'Suggestion: Consider optimizing this process as the minimum value (7804) is significantly lower than the average (7852.0).'}