--- name: Enhanced LLM-Native JIRA Workflows description: Example workflows demonstrating advanced context management, prompt optimization, and multi-turn reasoning version: 1.0.0 workflows: - id: epic-breakdown-with-reasoning name: Intelligent Epic Breakdown description: Break down complex epics using multi-turn reasoning and context awareness tags: [epic, planning, multi-turn] context_template: feature_epic_breakdown phases: - name: Analysis description: Analyze epic complexity with context awareness steps: - id: load-context task: Execute context-manager action: | Load or create epic breakdown context Check for previous breakdown attempts Apply team's preferred patterns - id: analyze-epic task: Execute prompt-optimizer action: | Select optimal epic analysis prompt based on: - Epic size and complexity - Team's historical patterns - Available context richness Execute analysis with enhanced prompt - id: present-analysis task: Execute reasoning-engine action: | Present epic analysis with progressive disclosure: - Summary for quick review - Detailed breakdown on request - Risk factors highlighted - Suggest breakdown approaches - name: Interactive Planning description: Guide user through story generation with checkpoints steps: - id: breakdown-conversation task: Execute interaction-flows action: | Start epic_breakdown_flow: Turn 1: "I've analyzed epic {{epic_key}}. It's {{complexity}} with {{estimate}} potential stories." Turn 2: Present breakdown options (vertical, horizontal, risk-based) Turn 3: Generate stories based on chosen approach Turn 4: Refine with user input - id: checkpoint-progress task: Execute checkpoint-manager action: | Create checkpoint after each turn Allow resume if interrupted Track decisions made - name: Story Creation description: Create stories with optimized prompts steps: - id: optimize-creation task: Execute prompt-chains action: | Chain story creation prompts: 1. Validate story completeness 2. Check dependencies 3. Create in JIRA with proper links 4. Update BMAD documentation - id: verify-sync task: Execute universal-sync-analysis action: | Verify three-way sync health Update context with created stories Generate sync report - id: sprint-planning-adaptive name: Adaptive Sprint Planning description: Plan sprints with intelligent capacity analysis and story selection tags: [sprint, planning, adaptive] context_template: sprint_planning_standard phases: - name: Capacity Intelligence description: Smart capacity calculation with pattern learning steps: - id: analyze-patterns task: Execute context-analyzer action: | Analyze team's sprint patterns: - Historical velocity trends - Completion rates by story type - Common planning mistakes Generate insights for current sprint - id: calculate-capacity task: Execute prompt-library action: | Use optimized capacity calculation prompt Factor in: - Team availability - Historical accuracy - Current context (holidays, releases) - name: Story Selection description: Intelligent story selection with multi-criteria optimization steps: - id: selection-conversation task: Execute decision-trees action: | Navigate sprint_planning_decision_tree: - Check capacity vs velocity - Evaluate story readiness - Consider dependencies - Balance priorities - id: optimize-selection task: Execute prompt-optimizer action: | Use context-aware story selection: - Team's skill distribution - Epic priorities - Technical dependencies - Risk balance - id: intelligent-sync-recovery name: Sync Operation with Recovery description: Demonstrate checkpoint recovery for interrupted sync operations tags: [sync, recovery, resilience] phases: - name: Sync Preparation description: Prepare for sync with checkpoint creation steps: - id: create-sync-checkpoint task: Execute checkpoint-manager action: | Create checkpoint before sync: - Current sync scope - Entity states - User preferences Mark as resumable - name: Simulated Interruption description: Handle sync interruption gracefully steps: - id: detect-incomplete task: Execute reasoning-engine action: | On next session: "I see you have an incomplete sync operation from {{time_ago}}. You were syncing {{entity_count}} stories and completed {{completed_count}}. Would you like to continue where you left off?" - id: resume-sync task: Execute checkpoint-manager action: | If user confirms: - Restore full context - Continue from last successful operation - Show what was completed and what remains - id: personalized-daily-standup name: Context-Aware Daily Standup description: Generate standups that learn from team patterns tags: [standup, daily, personalized] phases: - name: Pattern Learning description: Learn team's standup preferences steps: - id: analyze-preferences task: Execute context-analyzer action: | Analyze past standup patterns: - Preferred format and detail level - Common discussion topics - Time constraints - Team distribution - name: Intelligent Generation description: Create personalized standup agenda steps: - id: optimize-standup-prompt task: Execute prompt-optimizer action: | Select standup template based on: - Day of week (Monday vs Friday) - Sprint phase (early, mid, late) - Recent team events - Detected urgency - id: progressive-standup task: Execute interaction-flows action: | Use progressive disclosure: - Quick summary first - Detailed sections on request - Focus on anomalies and risks - Suggest discussion topics - id: bug-investigation-assistant name: Intelligent Bug Investigation description: Multi-turn bug analysis with context-aware insights tags: [bug, investigation, multi-turn] phases: - name: Bug Discovery description: Smart bug identification and context gathering steps: - id: understand-issue task: Execute reasoning-engine action: | Natural conversation: User: "The login is broken" Assistant: "I'll help investigate the login issue. Let me check a few things..." Use context to understand: - Component affected - Recent changes - Similar past issues - name: Guided Investigation description: Lead user through systematic investigation steps: - id: investigation-flow task: Execute decision-trees action: | Use bug_investigation_tree: - Check error patterns - Review recent deployments - Analyze related tickets - Generate hypotheses - id: adaptive-depth task: Execute prompt-optimizer action: | Adjust investigation depth based on: - User expertise level - Issue complexity - Available time - Prior investigation success example_usage: epic_breakdown_scenario: | User: "Break down the payment processing epic" Assistant: [Initializes context, loads patterns] "I'll help you break down the payment processing epic. Let me analyze it first..." [Uses optimized prompt for epic analysis] "I've analyzed PAYMENT-100: 'Payment Processing System'. Here's what I found: 📊 **Epic Analysis** - Estimated size: Large (8-12 stories) - Technical complexity: High (external integrations) - Risk factors: PCI compliance, third-party dependencies - Team experience: Medium (new payment provider) How would you like to break this down? 1. **By payment flow** (vertical slices) - Recommended 2. **By technical layer** (frontend/backend/integration) 3. **By risk** (tackle unknowns first) 4. **Custom approach**" User: "Let's go with payment flow" Assistant: [Creates checkpoint, continues conversation] "Good choice! Breaking down by payment flow will give us complete user journeys. Based on the requirements, I suggest these payment flows: 1. **Credit Card Processing** (3 stories) 2. **PayPal Integration** (2 stories) 3. **Refund Management** (2 stories) 4. **Payment History** (1 story) 5. **Error Handling & Recovery** (2 stories) Would you like me to generate the detailed stories for these flows?" [Continues with story generation, creating checkpoints at each step] sync_recovery_scenario: | [Session 1] User: "Sync all stories in the current sprint" Assistant: "I'll sync all stories in Sprint 42. Found 15 stories to sync..." [Creates checkpoint, begins sync] [Network interruption after 8 stories] [Session 2 - Next day] Assistant: "Welcome back! I see you have an incomplete sync operation from yesterday. You were syncing 15 stories in Sprint 42 and completed 8 successfully. ✅ Completed: AUTH-101, AUTH-102, PAY-201... ⏳ Remaining: PAY-203, UI-301, UI-302... Would you like to continue syncing the remaining 7 stories?" User: "Yes, continue" Assistant: [Restores context, resumes operation] "Resuming sync from PAY-203..." key_benefits: - context_awareness: Operations remember previous interactions and learn patterns - intelligent_prompting: Prompts are optimized based on context and performance - natural_conversations: Complex operations feel like helpful discussions - resilient_operations: Interruptions are handled gracefully with checkpoints - continuous_improvement: System learns and adapts while preserving privacy