Skill · Architecture
Cqrs implementation
Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.
How to use it
- Start your plan and connect your AI once
- Add the skill to your AI, one time only: press Copy skill and paste it into your AI's instructions: a project in ChatGPT, Claude or Grok, a Gem in Gemini, an agent in Microsoft 365 Copilot, or a rule in Cursor. It stays there for every new chat. In Kimi, paste it at the start of a new chat.
- Ask for the task in your own words, or say it directly:
Use the Cqrs implementation skill to help me with this.SKILL.md2 files in this skill
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.mdfor detailed CQRS patterns and templates.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.