243 lines
8.4 KiB
Markdown
243 lines
8.4 KiB
Markdown
# Create Comprehensive PR
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## Task Overview
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**Agent:** po (Product Owner - Business Context Owner)
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**Action Type:** pr-creation-with-context
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**Duration:** 5-8 minutes
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**LLM-Optimized:** Business-driven PR with comprehensive context
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## Purpose
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Generate pull request with business summary, technical changes, learning extraction, and validation evidence for streamlined review and delivery.
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## Inputs
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- Story implementation file with complete context
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- Commit information and PR context from commit-and-prepare-pr
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- Learning review results and team consensus
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- All validation evidence and quality metrics
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- Epic completion status and retrospective context
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## Outputs
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- GitHub PR created with comprehensive description
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- PR linked to story and epic context
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- Review assignments based on learning items
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- Story file updated with PR information
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## Execution Steps
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### Step 1: Generate PR Title (1 minute)
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```
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PR_TITLE_FORMAT:
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[Epic{epic_number}.{story_number}] {business_focused_title}
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Examples:
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- [Epic1.Story3] Add batch priority selector for dispatch optimization
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- [Epic2.Story1] Implement mobile scanning workflow for operations
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- [Epic3.Story2] Enhance order validation with QR code integration
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```
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### Step 2: Create PR Description (4-6 minutes)
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Generate comprehensive PR description:
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```markdown
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# Epic {epic_number}.{story_number}: {story_title}
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## 🎯 Business Summary
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**Epic:** {epic_title}
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**Epic Progress:** {epic_completion_percentage}% complete ({completed_stories}/{total_stories} stories)
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**Business Value:** {primary_business_value}
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**User Impact:** {user_impact_description}
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**Success Metrics:** {success_criteria}
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**Epic Status:** {IN_PROGRESS/COMPLETE}
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### Key Business Outcomes
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- ✅ {business_outcome_1}
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- ✅ {business_outcome_2}
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- ✅ {business_outcome_3}
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## 🔧 Technical Changes
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**Type:** {feature/enhancement/fix/refactor}
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**Complexity:** {SIMPLE/MODERATE/COMPLEX}
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**Architecture Impact:** {HIGH/MEDIUM/LOW/NONE}
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### Implementation Summary
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- **{component_1}:** {change_description} | Impact: {HIGH/MEDIUM/LOW}
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- **{component_2}:** {change_description} | Impact: {HIGH/MEDIUM/LOW}
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- **{component_3}:** {change_description} | Impact: {HIGH/MEDIUM/LOW}
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### Files Changed
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- `{file_1}` - {change_type} ({line_count} lines)
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- `{file_2}` - {change_type} ({line_count} lines)
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- `{file_3}` - {change_type} ({line_count} lines)
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**Total:** {file_count} files, {total_lines} lines changed
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## 📚 Learning Extraction & Actions
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### 🚨 Immediate Actions (Current Sprint)
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- [ ] **{urgent_action_1}** - @{owner} - Due: {date}
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- [ ] **{urgent_action_2}** - @{owner} - Due: {date}
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### 📋 Next Sprint Integration
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- [ ] **{next_action_1}** - @{owner} - Sprint Planning Item
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- [ ] **{next_action_2}** - @{owner} - Sprint Planning Item
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### 🚀 Future Epic Candidates
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- **{epic_candidate_1}** - Priority: {HIGH/MEDIUM/LOW} - Est: {effort}
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- **{epic_candidate_2}** - Priority: {HIGH/MEDIUM/LOW} - Est: {effort}
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### 🎉 Epic Completion Status
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**Epic Progress:** {epic_completion_percentage}% complete
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**Epic Retrospective:** {TRIGGERED/PENDING}
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{epic_completion_section}
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### 🔧 Architecture Improvements
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- **{arch_improvement_1}** - Timeline: {current/next/backlog}
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- **{arch_improvement_2}** - Timeline: {current/next/backlog}
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## ✅ Validation Evidence
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### Quality Gates
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- **Tests:** {test_count} added, {test_coverage}% coverage
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- **Linting:** ✅ PASS
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- **Type Safety:** ✅ PASS
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- **Build:** ✅ PASS
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- **E2E Tests:** ✅ PASS ({test_count} scenarios)
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### Review Process
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- **Pre-Review Validation:** ✅ COMPLETE
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- **Round 1 Reviews:** ✅ COMPLETE ({review_count} reviewers)
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- **Feedback Consolidation:** ✅ COMPLETE ({feedback_items} items)
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- **Fix Implementation:** ✅ COMPLETE
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- **Final Validation:** ✅ COMPLETE
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### Story DoD Compliance
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- **Business Requirements:** ✅ MET
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- **Technical Requirements:** ✅ MET
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- **Quality Standards:** ✅ MET
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- **Documentation:** ✅ COMPLETE
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- **Learning Extraction:** ✅ COMPLETE
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## 🔍 Test Coverage & Scenarios
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### New Tests Added
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- `{test_file_1}` - {test_count} tests - {coverage_area}
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- `{test_file_2}` - {test_count} tests - {coverage_area}
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### E2E Scenarios Covered
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- ✅ {scenario_1} - PASS
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- ✅ {scenario_2} - PASS
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- ✅ {scenario_3} - PASS
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### Edge Cases Tested
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- ✅ {edge_case_1} - PASS
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- ✅ {edge_case_2} - PASS
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## 📖 Documentation Updates
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- **Story File:** Updated with complete implementation context
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- **Epic Progress:** Updated with story completion
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- **Architecture Docs:** {updated/not_applicable}
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- **API Documentation:** {updated/not_applicable}
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- **User Documentation:** {updated/not_applicable}
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- **Epic Retrospective:** {SCHEDULED/NOT_APPLICABLE}
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## 🔗 Related Links
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- **Epic:** [Epic {epic_number}](../prd/epic{epic_number}.md)
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- **Story:** [Story {epic_number}.{story_number}](../stories/epic{epic_number}.story{story_number}.story.md)
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- **Commit:** {commit_hash}
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---
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**Story Status:** Done → Ready for Delivery
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**Epic Status:** {epic_completion_percentage}% complete
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**Epic Retrospective:** {TRIGGERED/PENDING}
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**Implementation Time:** {actual_time} (Est: {estimated_time})
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**Quality Score:** {quality_score}/10
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**Learning Items:** {learning_count} captured
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{epic_completion_celebration}
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🤖 Generated with [Claude Code](https://claude.ai/code)
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```
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### Step 3: Create PR with GitHub CLI (1-2 minutes)
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```bash
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gh pr create --title "[Epic{epic_number}.Story{story_number}] {business_title}" --body "$(cat <<'EOF'
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{comprehensive_pr_description_from_step_2}
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EOF
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)"
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```
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### Step 4: Assign Reviewers Based on Learning Items (1 minute)
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```bash
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# Auto-assign reviewers based on learning categories
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gh pr edit --add-reviewer {architect_username} # For ARCH_CHANGE items
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gh pr edit --add-reviewer {po_username} # For FUTURE_EPIC items
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gh pr edit --add-reviewer {dev_team_username} # For URGENT_FIX items
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gh pr edit --add-reviewer {sm_username} # For PROCESS_IMPROVEMENT items
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```
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### Step 5: Update Story File with PR Information (1 minute)
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```markdown
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## Pull Request Created
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**PO:** {po_name} | **Date:** {YYYY-MM-DD} | **PR:** #{pr_number}
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### PR Details
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- **Title:** [Epic{epic_number}.Story{story_number}] {business_title}
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- **URL:** {pr_url}
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- **Reviewers:** {reviewer_list}
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- **Status:** Open → Ready for Review
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### PR Content Summary
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- Business summary: ✅ COMPLETE
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- Epic completion status: ✅ COMPLETE
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- Technical changes: ✅ COMPLETE
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- Learning extraction: ✅ COMPLETE
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- Validation evidence: ✅ COMPLETE
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- Review assignments: ✅ COMPLETE
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- Epic retrospective context: ✅ COMPLETE (MANDATORY if epic 100% complete)
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**Final Status:** Story Implementation → PR Ready for Delivery
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**Epic Retrospective Status:** {MANDATORY_TRIGGERED/NOT_APPLICABLE}
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```
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## Success Criteria
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- [ ] PR created with comprehensive business and technical context
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- [ ] Epic completion status prominently displayed
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- [ ] Epic retrospective context included (if triggered)
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- [ ] Learning items prominently featured with action assignments
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- [ ] Validation evidence clearly documented
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- [ ] Appropriate reviewers assigned based on learning categories
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- [ ] Story file updated with PR information
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- [ ] PR ready for efficient review and merge
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## PR Description Guidelines
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- **Business-First:** Lead with business value and user impact
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- **Epic-Context:** Prominently display epic completion status
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- **Learning-Prominent:** Highlight learnings and future actions
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- **Evidence-Based:** Include objective validation proof
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- **Action-Oriented:** Clear next steps and ownership
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- **Comprehensive:** All context needed for informed review
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- **Celebration:** Highlight epic completion if applicable
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## Reviewer Assignment Logic
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```
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REVIEWER_MAPPING:
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- ARCH_CHANGE items → @architect (technical review)
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- FUTURE_EPIC items → @po (business validation)
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- URGENT_FIX items → @dev-team (technical validation)
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- PROCESS_IMPROVEMENT → @sm (process review)
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- TOOLING items → @infra-devops (infrastructure review)
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- KNOWLEDGE_GAP → @sm + @po (team development)
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```
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## Integration Points
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- **Input from:** commit-and-prepare-pr (commit and context)
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- **Output to:** update-epic-progress (epic tracking)
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- **Handoff:** "PR created and ready for review. Epic progress tracking initiated."
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## LLM Optimization Notes
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- Business-first structure prioritizes stakeholder understanding
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- Learning extraction prevents knowledge loss
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- Evidence-based validation reduces review overhead
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- Action-oriented format drives immediate value
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- Comprehensive context enables faster review cycles
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- Token-efficient format maintains readability while providing complete information |