"""Create recovery_planner
Generated by Phi-Octopus Eden
2025-12-14 09:31:03.031410
"""

Sure! Here's a comprehensive implementation of the "Create Recovery_Planner" capability:

```python
from typing import List, Dict, Optional
import datetime as dt
from dataclasses import dataclass

@dataclass
class ErrorRecord:
    """Represents an error entry with metadata."""
    id: str
    timestamp: dt.datetime
    exception_type: str
    message: str
    source_module: str
    recovery_attempted: bool = False
    recovery_success: Optional[bool] = None

class RecoveryPlanner:
    """
    A capability that manages error recovery and plans future resilience strategies.
    
    Attributes:
        error_storage: List of recorded errors with their details.
        analysis_history: History of planned recoveries and their outcomes.
    """

    def __init__(self):
        self.error_storage: List[ErrorRecord] = []
        self.analysis_history: List[Dict[str, Optional[bool]]] = []

    def record_error(self, exception: Exception, source_module: str) -> None:
        """
        Records an error occurrence with detailed information.
        
        Args:
            exception: The captured exception object
            source_module: Name of the module where the error occurred
        """
        error_id = f"ERR_{dt.datetime.now().strftime('%Y%m%d%H%M%S%f')}"
        self.error_storage.append(ErrorRecord(
            id=error_id,
            timestamp=dt.datetime.now(),
            exception_type=type(exception).__name__,
            message=str(exception),
            source_module=source_module
        ))
        
    def analyze_errors(self) -> Dict[str, int]:
        """
        Analyzes recorded errors and categorizes by type.
        
        Returns:
            Dictionary mapping error types to their frequency count.
        """
        error_freq = {}
        for err in self.error_storage:
            if not err.recovery_attempted:
                error_freq[err.exception_type] = error_freq.get(err.exception_type, 0) + 1
                
        # Plan recovery strategies based on most frequent errors
        plan_recommendations = {
            'FileNotFoundError': ['Add existence check before file operations', 'Use pathlib'],
            'ConnectionError': ['Implement retry logic with exponential backoff', 'Consider connection pooling'],
            'MemoryError': ['Increase memory allocation limits', 'Optimize data structures'],
            'TimeoutError': ['Increase timeout limits', 'Parallelize operations']
        }
        
        recommendations = {}
        for err_type, recommendation in plan_recommendations.items():
            if err_type in error_freq:
                recommendations[err_type] = {
                    'frequency': error_freq[err_type],
                    'recommendations': recommendation
                }
                
        self.analysis_history.append({
            'timestamp': dt.datetime.now(),
            'findings': error_freq,
            **{k: True for k, v in recommendations.items()}
        })
        
        return recommendations

    def apply_recovery(self, error_id: str) -> bool:
        """
        Attempts to recover from a specific recorded error.
        
        Args:
            error_id: ID of the error to attempt recovery on
            
        Returns:
            Whether recovery was successful or not
        """
        for err in self.error_storage:
            if err.id == error_id and not err.recovery_attempted:
                # Simulate recovery attempt based on common strategies
                if err.exception_type in ['FileNotFoundError', 'ConnectionError']:
                    # Apply recommended strategy
                    return True  # Mock success
                
                elif err.exception_type == 'TimeoutError':
                    # Apply recommended strategy
                    return False  # Mock failure
        
        raise ValueError(f"Error with ID {error_id} not found or already recovered")

    def get_recommendations(self) -> List[Dict[str, str]]:
        """
        Retrieves actionable recommendations based on error analysis.
        
        Returns:
            List of recommendation dictionaries with type and details
        """
        findings = self.analysis_history[-1]['findings'] if self.analysis_history else {}
        return [
            {
                'error_type': err_type,
                'frequency': info['frequency'],
                'recommendations': '\n'.join(info['recommendations'])
            }
            for err_type, info in findings.items()
        ]

# Example usage:
if __name__ == "__main__":
    import logging
    
    # Initialize recovery planner
    try:
        recovery_planner = RecoveryPlanner()
        
        # Simulate some errors
        file_error = FileNotFoundError("test.txt")
        conn_error = ConnectionError("Connection failed")
        timeout_error = TimeoutError("Timeout occurred")
        
        recovery_planner.record_error(file_error, "moduleA")
        recovery_planner.record_error(conn_error, "moduleB")
        recovery_planner.record_error(timeout_error, "moduleC")
        
        # Analyze errors
        findings = recovery_planner.analyze_errors()
        logging.info("Error analysis findings: %s", findings)
        
        # Apply recovery attempts based on recommendations
        for rec in recovery_planner.get_recommendations():
            logging.info(
                "Recommendation: %s\nFrequency: %d\nActionable steps:\n%s",
                rec['error_type'], rec['frequency'], rec['recommendations']
            )
            
            # Attempt recovery if frequency is high enough
            if rec['frequency'] > 3:
                success = recovery_planner.apply_recovery(rec.error_type)
                logging.info("Recovery attempt successful: %s", success)
                
    except Exception as e:
        logging.error("Error during execution: %s", str(e))
```

This code implements a comprehensive recovery planner that records errors, analyzes them, provides actionable recommendations, and simulates recovery attempts. It includes error handling, logging, and detailed documentation.