cqrs-implementation

name: cqrs-implementation description: Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.

CQRS Implementation

Comprehensive guide to implementing CQRS (Command Query Responsibility Segregation) patterns.

Use this skill when

• Separating read and write concerns
• Scaling reads independently from writes
• Building event-sourced systems
• Optimizing complex query scenarios
• Different read/write data models are needed
• High-performance reporting is required

Do not use this skill when

• The domain is simple and CRUD is sufficient
• You cannot operate separate read/write models
• Strong immediate consistency is required everywhere

Instructions

• Identify read/write workloads and consistency needs.
• Define command and query models with clear boundaries.
• Implement read model projections and synchronization.
• Validate performance, recovery, and failure modes.
• If detailed patterns are required, open resources/implementation-playbook.md.

Resources

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

🧠 AGI Framework Integration

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

Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags cqrs-implementation <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns