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Major documentation update focused on technical accuracy and developer clarity: Documentation Changes: - Rewrote README.md with focus on hooks system architecture - Updated all core docs (Overview, Integration, Performance) to match implementation - Created 6 missing configuration docs for undocumented YAML files - Updated all 7 hook docs to reflect actual Python implementations - Created docs for 2 missing shared modules (intelligence_engine, validate_system) - Updated all 5 pattern docs with real YAML examples - Added 4 essential operational docs (INSTALLATION, TROUBLESHOOTING, CONFIGURATION, QUICK_REFERENCE) Key Improvements: - Removed all marketing language in favor of humble technical documentation - Fixed critical configuration discrepancies (logging defaults, performance targets) - Used actual code examples and configuration from implementation - Complete coverage: 15 configs, 10 modules, 7 hooks, 3 pattern tiers - Based all documentation on actual file review and code analysis Technical Accuracy: - Corrected performance targets to match performance.yaml - Fixed timeout values from settings.json (10-15 seconds) - Updated module count and descriptions to match actual shared/ directory - Aligned all examples with actual YAML and Python implementations The documentation now provides accurate, practical information for developers working with the Framework-Hooks system, focusing on what it actually does rather than aspirational features. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Validation Intelligence Configuration (validation_intelligence.yaml)
Overview
The validation_intelligence.yaml file configures intelligent validation patterns, adaptive quality gates, and smart validation optimization for the SuperClaude-Lite framework.
Purpose and Role
This configuration provides:
- Intelligent Validation: Context-aware validation rules and patterns
- Adaptive Quality Gates: Dynamic quality thresholds based on context
- Validation Learning: Learn from validation patterns and outcomes
- Smart Optimization: Optimize validation processes for efficiency and accuracy
Key Configuration Areas
1. Intelligent Validation Patterns
- Context-Aware Rules: Apply different validation rules based on operation context
- Pattern-Based Validation: Use learned patterns to improve validation accuracy
- Risk Assessment: Assess validation risk based on operation characteristics
- Adaptive Thresholds: Adjust validation strictness based on context and history
2. Quality Gate Intelligence
- Dynamic Quality Metrics: Adjust quality requirements based on operation type
- Multi-Dimensional Quality: Consider multiple quality factors simultaneously
- Quality Learning: Learn what quality means in different contexts
- Progressive Quality: Apply increasingly sophisticated quality checks
3. Validation Optimization
- Efficiency Patterns: Learn which validations provide the most value
- Validation Caching: Cache validation results to avoid redundant checks
- Selective Validation: Apply validation selectively based on risk assessment
- Performance-Quality Balance: Optimize the trade-off between speed and thoroughness
4. Learning and Adaptation
- Validation Effectiveness: Track which validations catch real issues
- False Positive Learning: Reduce false positive validation failures
- Pattern Recognition: Recognize validation patterns across operations
- Continuous Improvement: Continuously improve validation accuracy and efficiency
Configuration Structure
The file includes:
- Intelligent validation rule definitions
- Context-aware quality gate configurations
- Learning and adaptation parameters
- Optimization strategies and thresholds
Integration Points
Framework Integration
- Works with all hooks that perform validation
- Integrates with quality gate systems
- Provides input to performance optimization
- Coordinates with error handling and recovery
Learning Integration
- Learns from validation outcomes and user feedback
- Adapts to project-specific quality requirements
- Improves validation patterns over time
- Shares learning with other intelligence systems
Usage Guidelines
This configuration controls the intelligent validation capabilities:
- Validation Depth: Balance thorough validation with performance needs
- Learning Sensitivity: Configure how quickly validation patterns adapt
- Quality Standards: Set appropriate quality thresholds for your use cases
- Optimization Balance: Balance validation thoroughness with efficiency
Related Documentation
- Validation Configuration:
validation.yaml.mdfor basic validation settings - Intelligence Patterns:
intelligence_patterns.yaml.mdfor core learning patterns - Quality Gates: Framework quality gate documentation for validation integration