# Correction Capture System Implementation

## Overview

The correction capture system (Phase 1) has been successfully implemented for the Claude PM Framework. This system enables automatic capture and storage of user corrections to agent responses, laying the foundation for future automatic prompt evaluation and improvement.

## Implementation Summary

### 1. Dependencies Installation

**Added to pyproject.toml:**
```toml
evaluation = [
    # Evaluation and prompt optimization
    "mirascope>=0.1.0",
    "pydantic>=2.5.0",
    "asyncio>=3.4.0"
]
```

**Updated `all` dependency group:**
```toml
all = [
    "claude-multiagent-pm[dev,production,ai,evaluation]"
]
```

### 2. Directory Structure Creation

**Automatic directory creation at `~/.claude-pm/training/`:**
- `corrections/` - Raw correction data storage
- `evaluations/` - Future evaluation results
- `agent-prompts/` - Future improved prompts
- `session_<id>/` - Session-specific correction storage

### 3. Core Components Implemented

#### A. CorrectionCapture Service
**File:** `claude_pm/services/correction_capture.py`

**Key Features:**
- JSON-based storage with proper file naming and rotation
- Data integrity validation and error handling
- Session management with unique IDs
- Automatic directory structure creation
- Configurable storage settings

**Core Classes:**
- `CorrectionCapture` - Main service class
- `CorrectionData` - Data structure for corrections
- `CorrectionType` - Enum for correction types
- `CorrectionStorageConfig` - Storage configuration

#### B. Configuration Integration
**File:** `claude_pm/core/config.py`

**Added Settings:**
```python
# Evaluation system
"enable_evaluation": True,
"evaluation_storage_path": str(Path.home() / ".claude-pm" / "training"),
"correction_capture_enabled": True,
"correction_storage_rotation_days": 30,
"evaluation_logging_enabled": True,
"auto_prompt_improvement": False,  # Disabled by default for Phase 1
```

#### C. Task Tool Integration
**File:** `claude_pm/utils/task_tool_helper.py`

**Enhanced Features:**
- Correction capture service integration
- Automatic correction hooks for subprocess creation
- Seamless correction capture from subprocess responses
- Extended configuration options

**New Methods:**
- `capture_correction()` - Capture corrections for subprocess responses
- `get_correction_statistics()` - Get correction statistics
- Enhanced `create_agent_subprocess()` with correction hooks

### 4. Key Features

#### Correction Types Supported
- `CONTENT_CORRECTION` - Basic content fixes
- `APPROACH_CORRECTION` - Methodology corrections
- `MISSING_INFORMATION` - Information gaps
- `INCORRECT_INTERPRETATION` - Misunderstood requirements
- `QUALITY_IMPROVEMENT` - Quality enhancements
- `FORMATTING_CORRECTION` - Format fixes
- `TECHNICAL_CORRECTION` - Technical issues

#### Severity Levels
- `low` - Minor corrections
- `medium` - Standard corrections
- `high` - Important corrections
- `critical` - Critical issues

#### Data Storage Format
```json
{
  "correction_id": "corr_20250715_123456_abc12345",
  "agent_type": "engineer",
  "original_response": "Original agent response",
  "user_correction": "User's corrected version",
  "context": {
    "subprocess_id": "engineer_20250715_123456",
    "task_description": "Task description",
    "working_directory": "/path/to/working/dir"
  },
  "correction_type": "content_correction",
  "timestamp": "2025-07-15T12:34:56.789Z",
  "severity": "medium",
  "user_feedback": "Optional user feedback",
  "metadata": {}
}
```

### 5. Integration Points

#### A. Task Tool Subprocess System
- Automatic correction hooks created for each subprocess
- Seamless integration without disrupting existing functionality
- Correction capture tied to subprocess lifecycle

#### B. PM Orchestrator Integration
- Works with existing PM orchestrator system
- Memory collection integration maintained
- Agent delegation workflow preserved

#### C. Configuration System
- Uses existing Config class infrastructure
- Environment variable support
- Backward compatibility maintained

### 6. Usage Examples

#### Basic Correction Capture
```python
from claude_pm.services.correction_capture import CorrectionCapture, CorrectionType

# Initialize service
capture = CorrectionCapture()

# Capture a correction
correction_id = capture.capture_correction(
    agent_type="engineer",
    original_response="def hello(): pass",
    user_correction="def hello(): print('Hello, World!')",
    context={"task": "Create hello function"},
    correction_type=CorrectionType.CONTENT_CORRECTION,
    severity="medium",
    user_feedback="Function was incomplete"
)
```

#### Task Tool Integration
```python
from claude_pm.utils.task_tool_helper import TaskToolHelper, TaskToolConfiguration

# Create helper with correction capture enabled
config = TaskToolConfiguration(
    correction_capture_enabled=True,
    correction_capture_auto_hook=True
)

helper = TaskToolHelper(config=config)

# Create subprocess (automatically creates correction hook)
subprocess_result = helper.create_agent_subprocess(
    agent_type="engineer",
    task_description="Implement authentication system",
    requirements=["JWT tokens", "Password hashing"],
    deliverables=["Auth module", "Tests"]
)

# Capture correction for subprocess response
correction_id = helper.capture_correction(
    subprocess_id=subprocess_result["subprocess_id"],
    original_response="Simple password check",
    user_correction="Secure bcrypt password hashing",
    correction_type="TECHNICAL_CORRECTION",
    severity="high"
)
```

#### Statistics and Reporting
```python
# Get correction statistics
stats = capture.get_correction_stats()
print(f"Total corrections: {stats['total_corrections']}")
print(f"Most corrected agent: {stats['most_corrected_agent']}")

# Get agent-specific corrections
corrections = capture.get_corrections(agent_type="engineer", limit=10)

# Export corrections
export_path = capture.export_corrections(format="json")
```

### 7. Testing and Validation

#### Test Files Created
- `tests/test_correction_capture.py` - Comprehensive test suite
- `claude_pm/services/correction_capture_demo.py` - Demo script
- `validate_correction_capture.py` - Validation script

#### Test Coverage
- Correction capture and storage
- Data integrity validation
- Task Tool integration
- Configuration integration
- Statistics generation
- Export functionality
- Cleanup procedures

### 8. Future Phases

#### Phase 2 - Mirascope Integration
- Automatic evaluation of corrections
- Prompt optimization based on corrections
- Quality scoring and improvement metrics

#### Phase 3 - Advanced Features
- Machine learning-based pattern recognition
- Automatic prompt generation
- Real-time correction suggestions

### 9. File Structure

```
claude_pm/
├── core/
│   └── config.py                    # Enhanced with evaluation settings
├── services/
│   ├── correction_capture.py        # Main correction capture service
│   └── correction_capture_demo.py   # Demo script
├── utils/
│   └── task_tool_helper.py          # Enhanced with correction capture
└── tests/
    └── test_correction_capture.py   # Test suite

docs/
└── correction_capture_implementation.md  # This documentation

~/.claude-pm/training/               # Auto-created storage structure
├── corrections/                     # Raw correction data
├── evaluations/                     # Future evaluation results
├── agent-prompts/                   # Future improved prompts
└── metadata.json                    # System metadata
```

### 10. Configuration

#### Environment Variables
```bash
# Enable/disable correction capture
export CLAUDE_MULTIAGENT_PM_CORRECTION_CAPTURE_ENABLED=true

# Set custom storage path
export CLAUDE_MULTIAGENT_PM_EVALUATION_STORAGE_PATH=/custom/path/training

# Set rotation days
export CLAUDE_MULTIAGENT_PM_CORRECTION_STORAGE_ROTATION_DAYS=30
```

#### Configuration File
```json
{
  "enable_evaluation": true,
  "evaluation_storage_path": "~/.claude-pm/training",
  "correction_capture_enabled": true,
  "correction_storage_rotation_days": 30,
  "evaluation_logging_enabled": true,
  "auto_prompt_improvement": false
}
```

## Implementation Status

- ✅ **Dependencies**: Mirascope added to optional dependencies
- ✅ **Directory Structure**: Auto-creation of training directories
- ✅ **Correction Capture**: Core service implemented
- ✅ **Data Storage**: JSON-based storage with rotation
- ✅ **Task Tool Integration**: Seamless integration with subprocess system
- ✅ **Configuration**: Extended config system with evaluation settings
- ✅ **Testing**: Comprehensive test suite and validation
- ✅ **Documentation**: Complete implementation documentation

## Next Steps

1. **Phase 2 Implementation**: Integrate Mirascope for automatic evaluation
2. **Advanced Analytics**: Implement correction pattern analysis
3. **Prompt Optimization**: Build automatic prompt improvement system
4. **User Interface**: Create CLI commands for correction management
5. **Performance Monitoring**: Add metrics for correction capture performance

## Technical Notes

- **Thread Safety**: All operations are thread-safe with proper file locking
- **Error Handling**: Comprehensive error handling with graceful degradation
- **Memory Management**: Efficient memory usage with file-based storage
- **Scalability**: Designed to handle large volumes of corrections
- **Backward Compatibility**: Maintains full compatibility with existing systems

This implementation provides a solid foundation for automatic prompt evaluation and improvement while maintaining the existing framework architecture and user experience.