NEW Browse AI tools across categories — updated daily. See what's new →

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.

Authorwshobson
Version1.0.0
LicenseMIT
Token count~719
UpdatedMay 27, 2026

Install

Quick install

via npx skills · works with 57+ agents
npx skills add https://github.com/wshobson/agents/tree/main/plugins/backend-development/skills/cqrs-implementation
Or pick agent:
npx skills add wshobson/agents --skill cqrs-implementation --agent claude-code
npx skills add wshobson/agents --skill cqrs-implementation --agent cursor
npx skills add wshobson/agents --skill cqrs-implementation --agent codex
npx skills add wshobson/agents --skill cqrs-implementation --agent opencode
npx skills add wshobson/agents --skill cqrs-implementation --agent github-copilot
npx skills add wshobson/agents --skill cqrs-implementation --agent windsurf
More install options

Shorthand — useful for multi-skill repos:

npx skills add wshobson/agents --skill cqrs-implementation

Manual — clone the repo and drop the folder into your agent's skills directory:

git clone https://github.com/wshobson/agents.git
cp -r agents/plugins/backend-development/skills/cqrs-implementation ~/.claude/skills/
How to use: Once installed, ask your agent to "use the cqrs-implementation skill" or describe what you want (e.g. "Implement Command Query Responsibility Segregation for scalable architectures. U"). Requires Node.js 18+.

CQRS Implementation

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

When to Use This Skill

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

Core Concepts

1. CQRS Architecture

                    ┌─────────────┐
                    │   Client    │
                    └──────┬──────┘
                           │
              ┌────────────┴────────────┐
              │                         │
              ▼                         ▼
       ┌─────────────┐          ┌─────────────┐
       │  Commands   │          │   Queries   │
       │    API      │          │    API      │
       └──────┬──────┘          └──────┬──────┘
              │                         │
              ▼                         ▼
       ┌─────────────┐          ┌─────────────┐
       │  Command    │          │   Query     │
       │  Handlers   │          │  Handlers   │
       └──────┬──────┘          └──────┬──────┘
              │                         │
              ▼                         ▼
       ┌─────────────┐          ┌─────────────┐
       │   Write     │─────────►│    Read     │
       │   Model     │  Events  │   Model     │
       └─────────────┘          └─────────────┘

2. Key Components

| Component | Responsibility |
| ------------------- | ------------------------------- |
| Command | Intent to change state |
| Command Handler | Validates and executes commands |
| Event | Record of state change |
| Query | Request for data |
| Query Handler | Retrieves data from read model |
| Projector | Updates read model from events |

Templates and detailed worked examples

Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.

Best Practices

Do's

  • Separate command and query models - Different needs
  • Use eventual consistency - Accept propagation delay
  • Validate in command handlers - Before state change
  • Denormalize read models - Optimize for queries
  • Version your events - For schema evolution

Don'ts

  • Don't query in commands - Use only for writes
  • Don't couple read/write schemas - Independent evolution
  • Don't over-engineer - Start simple
  • Don't ignore consistency SLAs - Define acceptable lag

SKILL.md source

---
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.

## When to Use This Skill

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

## Core Concepts

### 1. CQRS Architecture

```
                    ┌─────────────┐
                    │   Client    │
                    └──────┬──────┘
                           │
              ┌────────────┴────────────┐
              │                         │
              ▼                         ▼
       ┌─────────────┐          ┌─────────────┐
       │  Commands   │          │   Queries   │
       │    API      │          │    API      │
       └──────┬──────┘          └──────┬──────┘
              │                         │
              ▼                         ▼
       ┌─────────────┐          ┌─────────────┐
       │  Command    │          │   Query     │
       │  Handlers   │          │  Handlers   │
       └──────┬──────┘          └──────┬──────┘
              │                         │
              ▼                         ▼
       ┌─────────────┐          ┌─────────────┐
       │   Write     │─────────►│    Read     │
       │   Model     │  Events  │   Model     │
       └─────────────┘          └─────────────┘
```

### 2. Key Components

| Component           | Responsibility                  |
| ------------------- | ------------------------------- |
| **Command**         | Intent to change state          |
| **Command Handler** | Validates and executes commands |
| **Event**           | Record of state change          |
| **Query**           | Request for data                |
| **Query Handler**   | Retrieves data from read model  |
| **Projector**       | Updates read model from events  |

## Templates and detailed worked examples

Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates.

## Best Practices

### Do's

- **Separate command and query models** - Different needs
- **Use eventual consistency** - Accept propagation delay
- **Validate in command handlers** - Before state change
- **Denormalize read models** - Optimize for queries
- **Version your events** - For schema evolution

### Don'ts

- **Don't query in commands** - Use only for writes
- **Don't couple read/write schemas** - Independent evolution
- **Don't over-engineer** - Start simple
- **Don't ignore consistency SLAs** - Define acceptable lag

Related skills 6

running-claude-code-via-litellm-copilot

★ Featured

Use when routing Claude Code through a local LiteLLM proxy to GitHub Copilot, reducing direct Anthropic spend, configuring ANTHROPIC_BASE_URL or ANTHROPIC_MODEL overrides, or troubleshooting Copilot proxy setup failures such as model-not-found, no localhost traffic, or GitHub 401/403 auth errors.

xixu-me 155k
AI & ML

skills-cli

★ Featured

Use when users ask to discover, install, list, check, update, remove, back up, restore, sync, or initialize Agent Skills, mention `bunx skills`, `npx skills`, `skills.sh`, or `skills-lock.json`, ask "find a skill for X", or want help extending agent capabilities with installable skills.

xixu-me 155k
AI & ML

repo-intake-and-plan

★ Featured

Narrow RigorPilot helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.

lllllllama 127k
AI & ML

image-to-video

★ Featured

Animate any still image on RunComfy — this skill is a smart router that matches the user's intent to the right i2v model in the RunComfy catalog. Picks HappyHorse 1.0 I2V (Arena #1, native audio, identity preservation) for general animations, Wan 2.7 with `audio_url` for custom-voiceover lip-sync, or Seedance 2.0 Pro for multi-modal animation from image + reference video + reference audio. Bundles each model's documented prompting patterns so the caller gets sharper output without burning ite...

agentspace-so 121k
AI & ML

video-edit

★ Featured

Edit existing video on RunComfy — this skill is a smart router that matches the user's intent to the right edit model in the RunComfy catalog. Picks Wan 2.7 Edit-Video (general restyle / background swap / packaging swap, identity + motion preservation), Kling 2.6 Pro Motion Control (transfer precise motion from a reference video to a target character), or Lucy Edit Restyle (lightweight identity-stable restyle / outfit swap). Bundles each model's documented prompting patterns so the skill gets...

agentspace-so 121k
AI & ML

nano-banana-2

★ Featured

Generate images with Google Nano Banana 2 (Gemini-family flash-tier text-to-image) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Nano Banana 2's strengths (rapid iteration, in-image typography rendering, predictable framing, optional web-grounded context), the resolution-tier pricing, the safety-tolerance dial, and when to route to Nano Banana Pro / GPT Image 2 / Flux 2 / Seedream in...

agentspace-so 121k
AI & ML