@cloudkinetix/bmad-enhanced

# Semantic Dependency Analysis Report **Generated**: {{timestamp}} **Analysis ID**: {{analysisId}} **Confidence Level**: {{overallConfidence}}% ## Executive Summary The LLM-native analysis identified **{{totalDependencies}} dependencies** across {{workItemCount}} work items, including **{{hiddenCount}} hidden dependencies** that would not be detected by traditional file-based analysis. ### Key Findings - **Direct File Conflicts**: {{directConflicts}} - **Semantic Dependencies**: {{semanticDeps}} - **Architectural Impacts**: {{archImpacts}} - **Risk Level**: {{riskLevel}} ({{riskReason}}) ## Detailed Dependency Analysis ### Work Item Dependencies Matrix | Work Item | Direct Files | Semantic Dependencies | Hidden Risks | Wave | | --------- | ------------ | --------------------- | ------------ | ---- | {{#each workItems}} | {{description}} | {{files.length}} files | {{semanticDeps.length}} deps | {{risks.level}} | Wave {{wave}} | {{/each}} ### Hidden Dependencies Discovered {{#each hiddenDependencies}} #### {{@index}}. {{title}} **Type**: {{type}} **Affected Components**: {{components.join(", ")}} **Discovery Method**: {{method}} **Confidence**: {{confidence}}% **Analysis**: {{reasoning}} {{#llm-analyze type confidence}} Based on the dependency type ({{type}}) and confidence level ({{confidence}}%): - If business logic dependency with >80% confidence: Provide specific code examples and method signatures that would be affected - If data flow dependency: Map the complete data transformation pipeline - If architectural dependency with <60% confidence: List additional investigation steps needed - If security dependency: Highlight specific vulnerabilities and compliance impacts {{/llm-analyze}} **Impact if Missed**: {{impact}} {{#llm-generate risk_level="{{risk}}"}} Generate detailed impact scenarios: - If HIGH risk: Provide 3-5 specific failure scenarios with production impact estimates - If MEDIUM risk: List 2-3 degradation scenarios and user experience impacts - If LOW risk: Brief confirmation of minimal impact with monitoring recommendations {{/llm-generate}} **Mitigation Strategy**: {{mitigation}} {{#llm-enhance mitigation_complexity}} Enhance the mitigation strategy based on complexity: - If complex: Break down into numbered step-by-step implementation guide - If moderate: Provide code snippets or configuration examples - If simple: Confirm approach with best practice references {{/llm-enhance}} --- {{/each}} ### API Contract Dependencies {{#each apiDependencies}} #### {{endpoint}} **Consumers**: {{consumers.join(", ")}} **Contract Changes**: {{changes}} **Breaking Change Risk**: {{breakingRisk}} {{/each}} ## Architectural Impact Assessment ### Service Boundaries {{architecturalAnalysis.serviceBoundaries}} ### Design Pattern Implications {{architecturalAnalysis.patternImplications}} ### Performance Considerations {{architecturalAnalysis.performanceImpact}} ## Wave Planning Rationale ### Recommended Execution Waves ``` {{waveVisualization}} ``` ### Wave Composition Reasoning {{#each waves}} #### Wave {{number}}: {{title}} **Work Items**: {{items.join(", ")}} **Rationale**: {{reasoning}} **Dependencies Resolved**: {{resolved.join(", ")}} **Risk Level**: {{risk}} {{#llm-analyze wave_number="{{number}}" risk_level="{{risk}}" item_count="{{items.length}}"}} Provide wave-specific insights: - If Wave 1: Emphasize foundation-setting and risk mitigation strategies - If final wave: Focus on integration readiness and rollback procedures - If high risk with >3 items: Suggest sub-wave breakdown with timing - If low risk: Confirm parallel execution safety with performance benefits {{/llm-analyze}} {{#llm-recommend optimization_potential}} Based on wave composition, recommend optimizations: - Check for items that could be promoted to earlier waves - Identify opportunities for further parallelization within the wave - Suggest monitoring checkpoints between sub-tasks - Estimate time savings vs sequential execution {{/llm-recommend}} {{/each}} ## User Review Section ### Dependency Analysis for Review > 📝 **Instructions**: Please review the analysis below and add your corrections or insights in the marked sections. {{#each workItems}} #### Work Item: {{description}} **AI Analysis**: - Files to modify: {{predictedFiles.join(", ")}} - API impacts: {{apiImpacts.join(", ")}} - Hidden dependencies: {{hiddenDeps.join(", ")}} - Architectural concerns: {{archConcerns.join(", ")}} **Your Review**: ```yaml # Please provide your feedback here corrections: files: # Add any files the AI missed dependencies: # Identify any missed dependencies risks: # Note any additional risks agreement_level: # high/medium/low notes: | # Additional insights or corrections ``` --- {{/each}} ### Wave Planning Review **AI's Proposed Wave Plan**: {{proposedWavePlan}} **Your Alternative Suggestion**: ```yaml # Propose alternative wave composition if needed alternative_waves: wave_1: items: [] reasoning: "" wave_2: items: [] reasoning: "" ``` ## Confidence Metrics ### Analysis Confidence Breakdown | Aspect | Confidence | Factors | | --------------------- | ----------------------- | ------------------- | | File Dependencies | {{fileConfidence}}% | {{fileFactors}} | | Semantic Dependencies | {{semanticConfidence}}% | {{semanticFactors}} | | Hidden Dependencies | {{hiddenConfidence}}% | {{hiddenFactors}} | | Wave Planning | {{waveConfidence}}% | {{waveFactors}} | ### Areas Needing Human Validation {{#each lowConfidenceAreas}} - **{{area}}**: {{reason}} (Confidence: {{confidence}}%) {{/each}} ## Learning Opportunities ### Questions for User {{#each questions}} {{@index}}. {{question}} - Context: {{context}} - Why this helps: {{benefit}} {{/each}} ### Pattern Recognition Based on this analysis, the AI identified these patterns that could improve future analyses: {{#each patterns}} - **Pattern**: {{pattern}} - **Occurrence**: {{occurrence}} - **Implication**: {{implication}} {{/each}} --- 📊 **Next Steps**: 1. Review this analysis for accuracy 2. Provide feedback in [user-review.md](./user-review.md) 3. View machine-readable data in [dependency-matrix.json](./dependency-matrix.json) 4. Proceed with execution or request re-analysis