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feat: Sync pipeline-fixes epic to GitHub (Epic #234, Tasks #235-#244)
- Created Epic issue #234 for pipeline-fixes - Created 10 sub-issues (#235-#244) linked to parent epic - Renamed task files from numbered format to issue IDs - Updated all task dependencies to use issue numbers - Added GitHub URLs to all frontmatter - Created GitHub mapping file for reference All tasks properly linked and ready for parallel execution of first 6 tasks. Co-Authored-By: Claude <[email protected]>
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‎.claude/epics/pipeline-fixes/235.md‎

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# Task 001: Remove debug output and implement logging
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## Metadata
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- **Task ID**: 001
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- **Epic**: pipeline-fixes
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- **Name**: Remove debug output and implement logging
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- **Status**: TODO
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- **Priority**: Medium
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- **Size**: M (8 hours)
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- **Created**: 2025-08-22T13:29:31Z
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- **Dependencies**: None
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- **Can Run in Parallel**: Yes
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## Description
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Remove all debug print statements and implement proper logging throughout the codebase. This task involves:
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1. Identifying all files with print statements or DEBUG output
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2. Replacing print statements with appropriate logging calls
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3. Ensuring consistent logging configuration across the project
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4. Setting up proper log levels and formatting
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## Acceptance Criteria
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- [ ] All print statements used for debugging are removed or replaced with logging
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- [ ] Consistent logging configuration is implemented
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- [ ] Log levels are appropriately set (DEBUG, INFO, WARNING, ERROR)
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- [ ] No debug output appears in production runs unless explicitly enabled
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- [ ] Logging format is consistent across all modules
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## Files Affected
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- All Python files with print/DEBUG statements
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- Logging configuration files
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- Pipeline execution scripts
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## Implementation Notes
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- Use Python's standard `logging` module
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- Consider implementing a centralized logging configuration
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- Maintain backward compatibility where possible
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- Ensure logging doesn't impact performance significantly
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## Testing Requirements
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- Verify no unwanted output in production runs
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- Test logging configuration works correctly
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- Confirm log levels can be adjusted as needed
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- Validate log formatting is consistent
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## Definition of Done
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- All debug print statements removed or converted to logging
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- Logging configuration properly implemented
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- Tests pass with new logging implementation
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- Documentation updated if logging configuration changes

‎.claude/epics/pipeline-fixes/236.md‎

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# Task 002: Integrate UnifiedTemplateResolver into tools
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## Metadata
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- **Task ID**: 002
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- **Epic**: pipeline-fixes
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- **Name**: Integrate UnifiedTemplateResolver into tools
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- **Status**: TODO
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- **Priority**: Medium
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- **Size**: M (8 hours)
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- **Created**: 2025-08-22T13:29:31Z
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- **Dependencies**: None
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- **Can Run in Parallel**: Yes
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## Description
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Integrate the UnifiedTemplateResolver into remaining tools and control systems that are not yet using it. This ensures consistent template resolution across the entire pipeline system.
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The UnifiedTemplateResolver provides:
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- Consistent variable resolution
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- Support for nested templates
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- Proper handling of different data types
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- Standardized template syntax
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## Acceptance Criteria
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- [ ] All tools use UnifiedTemplateResolver for template processing
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- [ ] Control systems consistently use UnifiedTemplateResolver
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- [ ] Template resolution behavior is consistent across all components
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- [ ] No legacy template resolution code remains
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- [ ] All template syntax is standardized
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## Files Affected
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- Control systems that don't yet use UnifiedTemplateResolver
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- Tool implementations with custom template resolution
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- Pipeline configuration files
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- Template-related utility functions
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## Implementation Notes
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- Identify tools/systems still using legacy template resolution
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- Replace custom template logic with UnifiedTemplateResolver calls
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- Ensure backward compatibility for existing templates
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- Update any tool-specific template syntax to use standard format
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- Consider performance implications of template resolution changes
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## Testing Requirements
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- Verify all tools work with UnifiedTemplateResolver
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- Test template resolution with various data types
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- Confirm nested template resolution works correctly
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- Validate backward compatibility with existing templates
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- Performance testing to ensure no significant degradation
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## Definition of Done
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- All tools and control systems use UnifiedTemplateResolver
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- Legacy template resolution code removed
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- Tests pass with new template resolution
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- Template behavior is consistent across all components
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- Documentation updated to reflect standardized template syntax

‎.claude/epics/pipeline-fixes/237.md‎

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# Task 003: Fix generate-structured return format
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## Metadata
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- **Task ID**: 003
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- **Epic**: pipeline-fixes
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- **Name**: Fix generate-structured return format
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- **Status**: TODO
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- **Priority**: Medium
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- **Size**: S (4 hours)
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- **Created**: 2025-08-22T13:29:31Z
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- **Dependencies**: None
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- **Can Run in Parallel**: Yes
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## Description
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Fix the generate-structured tool to return proper objects instead of strings. Currently, the tool may be returning string representations of structured data instead of the actual structured objects, which breaks downstream processing that expects to work with the structured data directly.
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This affects the model_based_control_system.py and any other components that rely on structured output from text generation.
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## Acceptance Criteria
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- [ ] generate-structured returns proper Python objects (dict, list, etc.)
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- [ ] Return format is consistent with expected data structures
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- [ ] Downstream processing works correctly with returned objects
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- [ ] No string-to-object conversion needed in consuming code
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- [ ] Error handling preserves structured format when possible
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## Files Affected
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- `model_based_control_system.py`
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- generate-structured tool implementation
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- Any pipeline steps that use generate-structured output
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- Related test files
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## Implementation Notes
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- Identify where generate-structured is returning strings instead of objects
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- Ensure proper parsing/deserialization of generated content
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- Maintain backward compatibility if any code expects string format
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- Consider JSON parsing, YAML parsing, or other structured data formats
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- Handle edge cases where structured generation fails
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## Testing Requirements
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- Test generate-structured with various output formats
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- Verify downstream processing works with object returns
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- Test error cases and ensure graceful handling
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- Validate that structured data maintains proper types (int, float, bool, etc.)
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- Integration tests with model_based_control_system.py
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## Definition of Done
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- generate-structured returns proper objects, not strings
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- All downstream processing works without additional conversion
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- Tests pass with new return format
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- Error handling maintains structured approach
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- Documentation updated to reflect correct return types

‎.claude/epics/pipeline-fixes/238.md‎

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# Task 004: Standardize tool return format
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## Metadata
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- **Task ID**: 004
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- **Epic**: pipeline-fixes
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- **Name**: Standardize tool return format
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- **Status**: TODO
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- **Priority**: High
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- **Size**: L (12 hours)
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- **Created**: 2025-08-22T13:29:31Z
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- **Dependencies**: None
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- **Can Run in Parallel**: Yes
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## Description
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Standardize the return format for all tools to use a consistent structure with `result`, `success`, and `error` fields. Currently, tools return inconsistent formats, making it difficult for pipeline systems to handle outputs uniformly.
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The standardized format should be:
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```python
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{
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"success": bool, # True if operation succeeded
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"result": Any, # The actual result data (if success=True)
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"error": str | None, # Error message (if success=False)
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"metadata": dict | None # Optional metadata about the operation
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}
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```
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## Acceptance Criteria
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- [ ] All tools return consistent format with success/result/error structure
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- [ ] Base Tool class defines and enforces standard return format
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- [ ] Pipeline systems can rely on consistent return structure
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- [ ] Error handling is standardized across all tools
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- [ ] Backward compatibility maintained where possible
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- [ ] Tool documentation reflects standard return format
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## Files Affected
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- Base Tool class definition
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- All tool implementations (likely 20+ files)
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- Pipeline control systems that process tool outputs
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- Test files for tools
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- Tool interface documentation
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## Implementation Notes
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- Update base Tool class to define standard return format
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- Create helper methods for consistent return value creation
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- Update all individual tool implementations
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- Consider using a decorator or wrapper for automatic format conversion
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- Handle edge cases where tools currently return complex formats
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- Ensure error information is preserved and meaningful
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## Testing Requirements
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- Test all tools return standard format
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- Verify pipeline systems work with new format
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- Test error cases return proper error structure
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- Integration tests with control systems
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- Backward compatibility tests where applicable
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- Performance testing to ensure format changes don't impact speed
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## Definition of Done
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- All tools return standardized format
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- Base Tool class enforces consistent returns
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- Pipeline systems updated to use standard format
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- All tests pass with new return format
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- Tool documentation updated
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- Error handling is consistent and informative across all tools

‎.claude/epics/pipeline-fixes/239.md‎

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# Task 005: Implement OutputSanitizer for clean outputs
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## Metadata
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- **Task ID**: 005
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- **Epic**: pipeline-fixes
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- **Name**: Implement OutputSanitizer
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- **Status**: TODO
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- **Priority**: Medium
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- **Size**: S (4 hours)
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- **Created**: 2025-08-22T13:29:31Z
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- **Dependencies**: None
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- **Can Run in Parallel**: Yes
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## Description
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Create a new OutputSanitizer class to remove conversational markers and other unwanted content from pipeline outputs. This task involves:
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1. Creating an OutputSanitizer class that can identify and remove conversational markers
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2. Implementing pattern matching for common conversational phrases
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3. Integrating the sanitizer into the pipeline output processing
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4. Ensuring clean, professional outputs without conversational artifacts
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## Acceptance Criteria
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- [ ] OutputSanitizer class is implemented with clear interface
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- [ ] Removes conversational markers like "Certainly!", "Here is...", etc.
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- [ ] Handles hard-coded values that should be computed dynamically
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- [ ] Preserves legitimate content while removing unwanted artifacts
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- [ ] Can be easily integrated into existing pipeline workflows
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- [ ] Includes comprehensive pattern matching for common issues
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## Files Affected
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- New OutputSanitizer class file
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- Pipeline output processing modules
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- Integration points in existing pipelines
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- Test files for the new functionality
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## Implementation Notes
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- Use regular expressions for pattern matching
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- Consider configurable sanitization rules
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- Maintain performance for large outputs
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- Ensure backward compatibility with existing pipelines
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- Make the sanitizer extensible for future pattern additions
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## Testing Requirements
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- Test removal of various conversational markers
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- Verify legitimate content is preserved
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- Test performance with large outputs
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- Validate integration with existing pipelines
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- Test edge cases and corner scenarios
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## Definition of Done
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- OutputSanitizer class is fully implemented and tested
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- Integration with pipeline output processing is complete
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- All tests pass including edge cases
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- Documentation includes usage examples
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- Code review completed and approved

‎.claude/epics/pipeline-fixes/240.md‎

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# Task 006: Fix DataProcessingTool and ValidationTool
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## Metadata
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- **Task ID**: 006
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- **Epic**: pipeline-fixes
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- **Name**: Fix DataProcessingTool CSV handling and ValidationTool schemas
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- **Status**: TODO
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- **Priority**: Medium
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- **Size**: M (6 hours)
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- **Created**: 2025-08-22T13:29:31Z
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- **Dependencies**: None
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- **Can Run in Parallel**: Yes
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## Description
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Fix critical issues in DataProcessingTool CSV handling and implement missing ValidationTool schemas. This task involves:
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1. Fixing CSV handling bugs in DataProcessingTool
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2. Implementing the missing quality_check schema in ValidationTool
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3. Ensuring proper error handling for malformed data
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4. Adding comprehensive validation for data processing operations
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5. Testing with various CSV formats and edge cases
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## Acceptance Criteria
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- [ ] DataProcessingTool correctly handles CSV files of various formats
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- [ ] ValidationTool includes complete quality_check schema implementation
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- [ ] Proper error handling for malformed CSV data
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- [ ] Edge cases are handled gracefully (empty files, missing columns, etc.)
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- [ ] Validation schemas are comprehensive and accurate
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- [ ] Performance is maintained for large CSV files
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## Files Affected
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- DataProcessingTool implementation files
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- ValidationTool schema definitions
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- Related utility functions for CSV processing
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- Test files for both tools
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- Pipeline configurations that use these tools
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## Implementation Notes
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- Use robust CSV parsing libraries (pandas, csv module)
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- Implement proper schema validation using appropriate libraries
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- Consider memory efficiency for large files
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- Ensure cross-platform compatibility
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- Add detailed error messages for debugging
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- Maintain backward compatibility where possible
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## Testing Requirements
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- Test with various CSV formats and encodings
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- Validate schema enforcement works correctly
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- Test error handling with malformed data
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- Performance testing with large datasets
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- Integration testing with existing pipelines
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- Edge case testing (empty files, single rows, etc.)
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## Definition of Done
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- DataProcessingTool CSV handling is robust and reliable
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- ValidationTool quality_check schema is fully implemented
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- All tests pass including edge cases and performance tests
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- Error handling provides clear, actionable messages
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- Integration with existing pipelines works seamlessly
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- Code review completed and approved

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