Projection Patterns

Projection Patterns

Comprehensive guide to building projections and read models for event-sourced systems.

Use this skill when

• Building CQRS read models
• Creating materialized views from events
• Optimizing query performance
• Implementing real-time dashboards
• Building search indexes from events
• Aggregating data across streams

Do not use this skill when

• The task is unrelated to projection patterns
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Resources

• resources/implementation-playbook.md for detailed patterns and examples.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior Architecture Decision Records (ADRs), trade-off analyses, and system design rationale. Critical for maintaining consistency across long-running projects.

# Check for prior architecture/design context before starting
python3 execution/memory_manager.py auto --query "architecture decisions and trade-off analysis for Projection Patterns"

Storing Results

After completing work, store architecture/design decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Architecture: event-driven microservices with CQRS, Pulsar for messaging, Qdrant for semantic search" \
  --type decision --project <project> \
  --tags projection-patterns architecture

Multi-Agent Collaboration

Broadcast architecture decisions to ALL agents so implementation stays aligned with the chosen patterns.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Completed architecture review — ADR documented, trade-offs analyzed, team aligned" \
  --project <project>

Control Tower Coordination

Register architecture tasks in the Control Tower so all agents across machines know the current system design and constraints.