315 lines
11 KiB
Markdown
315 lines
11 KiB
Markdown
# Pattern Intelligence
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## Advanced Pattern Recognition and Application System
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Pattern Intelligence enables Claude Code to recognize, learn from, and apply successful development patterns while avoiding known anti-patterns and failure modes.
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### Pattern Recognition Framework
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#### Pattern Types and Detection
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```yaml
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pattern_categories:
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architectural_patterns:
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microservices_adoption:
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- service_decomposition_strategies
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- inter_service_communication
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- data_consistency_patterns
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- deployment_orchestration
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monolith_to_services:
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- strangler_fig_pattern
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- database_decomposition
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- gradual_migration_strategies
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- rollback_safety_nets
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event_driven_architecture:
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- event_sourcing_patterns
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- saga_patterns
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- event_store_design
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- eventual_consistency_handling
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code_patterns:
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design_pattern_usage:
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- factory_patterns: "Object creation strategies"
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- observer_patterns: "Event notification systems"
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- strategy_patterns: "Algorithm selection"
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- decorator_patterns: "Behavior extension"
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anti_pattern_detection:
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- god_objects: "Classes with too many responsibilities"
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- spaghetti_code: "Unstructured control flow"
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- copy_paste_programming: "Code duplication"
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- magic_numbers: "Unexplained constants"
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workflow_patterns:
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development_workflows:
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- feature_branch_strategies
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- code_review_patterns
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- testing_workflows
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- deployment_patterns
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collaboration_patterns:
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- pair_programming_effectiveness
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- mob_programming_scenarios
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- async_collaboration_tools
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- knowledge_sharing_methods
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performance_patterns:
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optimization_strategies:
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- caching_layer_patterns
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- database_optimization
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- api_efficiency_patterns
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- frontend_performance
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scaling_patterns:
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- horizontal_scaling_strategies
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- load_balancing_patterns
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- database_sharding
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- cdn_utilization
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```
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### Pattern Recognition Engine
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#### Feature Extraction and Analysis
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```python
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async def extract_code_patterns(project_path):
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"""
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Extract patterns from codebase using Claude Code tools
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"""
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# Use Glob to discover all code files
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code_files = await discover_codebase(project_path, "**/*.{ts,js,py,java}")
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# Use Read to analyze file contents
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file_analyses = await asyncio.gather(*[
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analyze_file_patterns(file_path) for file_path in code_files
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])
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# Use Grep to find specific pattern indicators
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pattern_indicators = await search_pattern_indicators(project_path)
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# Extract structural patterns
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structural_patterns = extract_structural_patterns(file_analyses)
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# Identify behavioral patterns
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behavioral_patterns = extract_behavioral_patterns(pattern_indicators)
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return {
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'structural': structural_patterns,
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'behavioral': behavioral_patterns,
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'quality_metrics': calculate_quality_metrics(file_analyses)
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}
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async def detect_anti_patterns(codebase_analysis):
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"""
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Identify problematic patterns that should be avoided
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"""
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anti_patterns = {
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'god_objects': detect_god_objects(codebase_analysis),
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'circular_dependencies': detect_circular_deps(codebase_analysis),
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'code_duplication': detect_duplication(codebase_analysis),
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'performance_issues': detect_performance_anti_patterns(codebase_analysis)
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}
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# Generate recommendations for each anti-pattern
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recommendations = {}
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for pattern_type, instances in anti_patterns.items():
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if instances:
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recommendations[pattern_type] = generate_refactoring_recommendations(
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pattern_type, instances
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)
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return {
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'detected_anti_patterns': anti_patterns,
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'refactoring_recommendations': recommendations
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}
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```
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#### Pattern Similarity and Matching
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```yaml
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pattern_matching:
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similarity_detection:
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1_extract_features:
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- normalize_metrics: "Standardize measurements"
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- weight_importance: "Prioritize key characteristics"
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- create_signature: "Unique pattern identifier"
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2_compare_patterns:
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- calculate_distance: "Similarity scoring algorithm"
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- apply_thresholds: "Minimum similarity requirements"
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- rank_matches: "Order by relevance and confidence"
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3_validate_match:
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- context_compatibility: "Does pattern fit current context?"
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- constraint_satisfaction: "Can constraints be met?"
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- outcome_prediction: "Likely success probability"
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pattern_evolution:
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- track_variations: "How patterns adapt over time"
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- identify_mutations: "Natural evolution of patterns"
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- merge_similar_patterns: "Consolidate redundant patterns"
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- deprecate_obsolete: "Remove outdated patterns"
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```
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### Pattern Application Engine
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#### Intelligent Pattern Recommendation
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```python
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async def recommend_patterns(current_context, problem_description):
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"""
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Recommend optimal patterns based on current development context
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"""
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# Analyze current project state using multiple tools
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project_state = await analyze_project_state(current_context)
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# Search pattern repository for relevant patterns
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candidate_patterns = search_pattern_repository(
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problem_description,
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project_state.technology_stack,
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project_state.constraints
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)
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# Rank patterns by fit and success probability
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ranked_patterns = rank_patterns_by_fit(
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candidate_patterns,
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project_state,
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historical_success_data()
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)
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# Generate implementation guidance
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implementation_guides = []
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for pattern in ranked_patterns[:3]: # Top 3 recommendations
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guide = await generate_implementation_guide(
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pattern,
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project_state,
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current_context
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)
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implementation_guides.append(guide)
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return {
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'recommended_patterns': ranked_patterns,
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'implementation_guides': implementation_guides,
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'risk_assessments': assess_implementation_risks(ranked_patterns)
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}
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async def apply_pattern_with_validation(pattern, target_location):
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"""
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Apply a pattern with built-in validation and rollback capability
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"""
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# Create backup using git
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backup_created = await create_pattern_backup(target_location)
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try:
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# Apply pattern using appropriate Claude Code tools
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if pattern.type == 'code_pattern':
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await apply_code_pattern(pattern, target_location)
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elif pattern.type == 'architecture_pattern':
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await apply_architecture_pattern(pattern, target_location)
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elif pattern.type == 'workflow_pattern':
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await apply_workflow_pattern(pattern, target_location)
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# Validate application using Bash tools
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validation_results = await validate_pattern_application(
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pattern, target_location
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)
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if validation_results.success:
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await document_pattern_application(pattern, validation_results)
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return {'status': 'success', 'validation': validation_results}
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else:
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await rollback_pattern_application(backup_created)
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return {'status': 'failed', 'errors': validation_results.errors}
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except Exception as e:
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await rollback_pattern_application(backup_created)
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return {'status': 'error', 'exception': str(e)}
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```
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### Pattern Learning and Evolution
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#### Success Pattern Extraction
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```yaml
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pattern_learning:
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success_identification:
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metrics_tracking:
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- performance_improvements: "Before/after measurements"
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- quality_enhancements: "Bug reduction, maintainability"
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- development_velocity: "Feature delivery speed"
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- team_satisfaction: "Developer experience metrics"
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pattern_attribution:
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- isolate_pattern_impact: "What specifically caused improvement?"
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- control_for_variables: "Account for other changes"
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- measure_confidence: "How certain are we of the attribution?"
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failure_analysis:
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failure_indicators:
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- performance_degradation: "Slower than expected"
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- increased_complexity: "Harder to maintain"
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- team_resistance: "Adoption difficulties"
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- integration_problems: "Doesn't play well with existing code"
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root_cause_analysis:
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- context_mismatch: "Pattern didn't fit the situation"
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- implementation_errors: "Pattern applied incorrectly"
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- prerequisite_missing: "Missing foundational elements"
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- environmental_factors: "External constraints interfered"
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```
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### Pattern Repository Management
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#### Pattern Storage and Retrieval
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```yaml
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pattern_repository:
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pattern_metadata:
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identification:
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- pattern_id: "unique_identifier"
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- pattern_name: "descriptive_name"
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- pattern_category: "architectural|code|workflow|performance"
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- pattern_tags: ["microservices", "async", "resilient"]
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context_information:
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- applicable_technologies: ["nodejs", "react", "mongodb"]
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- project_sizes: ["small", "medium", "enterprise"]
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- team_sizes: ["1-3", "4-10", "10+"]
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- complexity_levels: ["simple", "moderate", "complex"]
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success_metrics:
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- implementation_count: "number_of_times_applied"
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- success_rate: "percentage_successful_implementations"
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- average_impact: "typical_improvement_metrics"
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- confidence_score: "reliability_rating"
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search_and_retrieval:
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multi_dimensional_search:
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- by_problem_type: "What are you trying to solve?"
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- by_context: "What's your current situation?"
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- by_technology: "What tools are you using?"
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- by_constraints: "What limitations do you have?"
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intelligent_ranking:
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- relevance_score: "How well does this pattern fit?"
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- success_probability: "How likely is this to work?"
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- implementation_effort: "How hard is this to implement?"
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- risk_assessment: "What could go wrong?"
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```
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### Claude Code Integration Commands
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```bash
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# Pattern discovery and analysis
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bmad patterns analyze --project <path>
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bmad patterns detect --anti-patterns --project <path>
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bmad patterns extract --successful --from-history
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# Pattern recommendation and application
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bmad patterns recommend --problem "scaling-issues" --context "microservices"
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bmad patterns apply --pattern "circuit-breaker" --location "api-gateway"
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bmad patterns validate --applied-pattern "event-sourcing"
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# Pattern learning and evolution
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bmad patterns learn --from-outcome "successful" --project <path>
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bmad patterns evolve --pattern-id "microservice-decomposition"
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bmad patterns optimize --based-on "recent-applications"
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```
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This Pattern Intelligence system transforms Claude Code into a pattern-aware development assistant that can recognize successful approaches, avoid known pitfalls, and continuously learn from development experiences to provide increasingly sophisticated guidance. |