class CapabilityImprovementAnalyzer:
    def __init__(self):
        self.name = "CapabilityImprovementAnalyzer"
        self.capabilities_data = []
        self.metrics = {
            "execution_time": {"max_threshold": 10, "min_threshold": 5},
            "success_rate": {"max_threshold": 95, "min_threshold": 80},
            "resource_usage": {"max_memory_usage": 200, "min_memory_usage": 100},
            "error_count": {"threshold": 5},
            "accuracy": {"threshold": 85}
        }

    def analyze_capabilities(self):
        """Evaluates existing capabilities and suggests improvements."""
        for capability in self.capabilities_data:
            improvement_suggestions = []
            # Analyze execution time
            if capability.execution_time > self.metrics["execution_time"]["max_threshold"]:
                improvement_suggestions.append("Optimize algorithms to reduce execution time")
            elif capability.execution_time < self.metrics["execution_time"]["min_threshold"]:
                improvement_suggestions.append("Review for potential performance bottlenecks")

            # Analyze success rate
            if capability.success_rate < self.metrics["success_rate"]["max_threshold"]:
                improvement_suggestions.append(f"Improve accuracy to reach at least {self.metrics['success_rate']['max_threshold']}%")
            elif capability.success_rate > self.metrics["success_rate"]["min_threshold"]:
                improvement_suggestions.append("Success rate is satisfactory but room for improvement")

            # Analyze resource usage
            if capability.resource_usage.memory_usage > self.metrics["resource_usage"]["max_memory_usage"]:
                improvement_suggestions.append(f"Optimize memory usage to stay below {self.metrics['resource_usage']['max_memory_usage']}MB")
            elif capability.resource_usage.memory_usage < self.metrics["resource_usage"]["min_memory_usage"]:
                improvement_suggestions.append("Memory usage is within optimal range")

            # Analyze error count
            if capability.error_count > self.metrics["error_count"]["threshold"]:
                improvement_suggestions.append(f"Reduce errors to below {self.metrics['error_count']['threshold']} occurrences")

            # Analyze accuracy
            if capability.accuracy < self.metrics["accuracy"]["threshold"]:
                improvement_suggestions.append(f"Improve accuracy to reach at least {self.metrics['accuracy']['threshold']}%")
            
            # Set final suggestions for the capability
            capability.improvement_suggestions = improvement_suggestions

    def analyze_improvement(self, capabilities):
        """Evaluates all capabilities and generates a comprehensive report."""
        self.capabilities_data = capabilities.copy()
        if not self.capabilities_data:
            return []
        
        # Analyze each capability
        for capability in self.capabilities_data:
            self.analyze_capabilities(capability)
        
        # Generate priority-based suggestions
        improvement_report = []
        for capability in self.capabilities_data:
            priority_level = 0
            if "Optimize algorithms" in capability.improvement_suggestions or \
               f"Reduce errors to below {self.metrics['error_count']['threshold']} occurrences" in capability.improvement_suggestions:
                priority_level = 3
            elif "Improve accuracy" in capability.improvement_suggestions or \
                 f"Improve accuracy to reach at least {self.metrics['accuracy']['threshold']}%" in capability.improvement_suggestions:
                priority_level = 2
            else:
                priority_level = 1
                
            improvement_report.append({
                "name": capability.name,
                "improvement_suggestions": capability.improvement_suggestions,
                "priority_level": priority_level
            })
        
        # Sort by priority level (highest first)
        improvement_report.sort(key=lambda x: -x["priority_level"])
        return improvement_report

    def __repr__(self):
        return f"CapabilityImprovementAnalyzer()"

# Example usage:
"""
improvement_analyzer = CapabilityImprovementAnalyzer()
capabilities = [capability1, capability2, ...]  # List of capability objects
report = improvement_analyzer.analyze_improvement(capabilities)
for item in report:
    print(f"{item['name']} has suggestions: {item['improvement_suggestions']}")
"""