🧹 Edge-AI Cleanup and Ecosystem Validation

[mkcomer]

Description

ECOSYSTEM VALIDATION: Final cleanup and validation of complete HVE ecosystem migration with thoughtful learning standards applied.
DEPENDENCY: Execute after federated discovery (Task 4.1) operational and learning standards applied in Customer-Zero (Task 2.3).

Edge-AI Repository Cleanup

• Review and update Edge-AI documentation for HVE ecosystem integration
• Clean up any duplicate content that should reference Customer-Zero
• Update getting-started guides to reference Customer-Zero fundamentals with proper learning progressions
• Validate all cross-references and navigation paths between technical and business learning domains
• Remove universal katas from Edge-AI (ai-assisted-engineering fundamentals, general prompt engineering)
• Keep Edge-AI specific katas (edge deployment, IoT Operations, manufacturing scenarios)
• Configure Edge-AI dev container to reference HVE-Core
• Ensure Edge-AI technical progression complements Customer-Zero business progression

Learning Standards Integration Testing

• Test learning path transitions: Customer-Zero business fundamentals → Edge-AI technical implementation
• Validate competency framework compatibility between customer success and technical domains
• Test assessment continuity across repositories (business skills → technical skills)
• Verify learning progression makes sense for complete user journey

Complete Ecosystem Testing

• Test complete learning journey: Customer-Zero learning standards → Edge-AI technical deployment
• Validate HVE Copilot discovery across both repositories with learning standards metadata
• Test registry infrastructure functionality with domain-appropriate content organization
• Verify cross-repo content synchronization maintains learning standards consistency
• Test user workflows across HVE ecosystem with appropriate skill progressions

Documentation and Communication

• Update ecosystem documentation with learning standards implementation results
• Create ecosystem overview showing learning progression across domains
• Document cross-repo learning path recommendations with competency transitions
• Document how Customer-Zero learning standards complement Edge-AI technical focus
• Prepare ecosystem launch communication highlighting thoughtful learning design

Definition of Done

• Edge-AI focused exclusively on edge-specific learning content
• Edge-AI consuming HVE-Core dev container successfully
• Clean separation between universal (Customer-Zero) and edge-specific (Edge-AI) content
• Learning standards properly implemented and tested across repositories
• Competency framework transitions work seamlessly between business and technical domains
• Complete HVE ecosystem operational with coherent learning progression
• All learning paths tested and validated with appropriate skill building
• Documentation complete and user-ready with learning standards guidance

Priority: Medium (final validation)
Dependencies: Task 4.1 (Federation) AND Task 2.3 (Customer-Zero learning standards applied)
Estimate: 8 story points (increased for learning standards validation)