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Description
Currently, all AI coding rules are stored as static TypeScript files in the codebase (src/data/rules/). This creates several significant limitations for maintaining and scaling the rules system:
🚫 Current Limitations
- Adding a new rule requires a code change and full deployment
- Non-technical contributors cannot add rules without developer assistance
- Rules are scattered across multiple TypeScript files
- No centralized interface to view, search, or manage all rules
- Difficult to temporarily disable problematic rules
- Contributors must understand TypeScript and project structure
- Pull request process required for simple rule additions
- Maintainers bottlenecked reviewing rule PRs
- High barrier to entry for domain experts who aren't developers
- Cannot personalize rules based on user preferences or project types
🔍 Current Architecture Analysis
File-Based System:
src/data/rules/
├── frontend.ts # React, Vue, Angular rules
├── backend.ts # Node, Python, Java rules
├── database.ts # SQL, MongoDB rules
├── testing.ts # Jest, Cypress rules
└── ...
Issues with Current Approach:
- Rules hard-coded in TypeScript objects
- Build-time compilation required for changes
- No runtime rule modifications possible
- Tight coupling between rules and application code
💡 Proposed Solution Requirements
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Database Storage
- Move rules from files to database storage
- Maintain existing rule structure and organization
- Support for metadata (created_at, sort_order, is_active, etc.)
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Backward Compatibility
- Existing application functionality must not break
- Gradual migration path with fallback mechanisms
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Type Safety
- Maintain TypeScript type safety throughout
- Ensure rule data integrity
- Proper validation of rule content
📋 Success Criteria
This issue will be resolved when:
- Rules can be added/edited without code changes
- Type safety and data integrity preserved
- Fallback mechanism works if database unavailable
- Migration script successfully transfers existing rules
- Documentation updated for new workflow
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