skills/cqrs-pattern-implementation/SKILL.md
To separate read and write operations into different models, allowing each to be optimized independently for performance, scalability, and security. Use when: In complex domains where the read model significantly differs from the write model; When high performance is required for reads (e.g., complex dashboards); When using Event Sourcing or distributed systems.
npx skillsauth add jyjeanne/ai-setup-forge cqrs-pattern-implementationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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To separate read and write operations into different models, allowing each to be optimized independently for performance, scalability, and security.
Commands represent the intent to change state.
// commands/create-user.command.ts
export class CreateUserCommand {
constructor(
public readonly email: string,
public readonly name: string
) {}
}
The handler performs the actual state change.
// handlers/create-user.handler.ts
export class CreateUserHandler {
async handle(command: CreateUserCommand) {
// 1. Validate logic
// 2. Persist to "Write Database"
// 3. (Optional) Publish event to sync Read Model
console.log(`Creating user: ${command.email}`);
}
}
Queries represent the request to retrieve data.
// queries/get-user-stats.query.ts
export class GetUserStatsQuery {
constructor(public readonly userId: string) {}
}
The query handler fetches data from an optimized read model (e.g., a flattened SQL table or Redis).
// handlers/get-user-stats.handler.ts
export class GetUserStatsHandler {
async handle(query: GetUserStatsQuery) {
// 1. Query optimized "Read Database"
return { loginCount: 10, lastSeen: new Date() };
}
}
A simple dispatcher to route commands/queries to their handlers.
export class MessageBus {
private handlers = new Map();
register(type: any, handler: any) {
this.handlers.set(type, handler);
}
async execute(message: any) {
const handler = this.handlers.get(message.constructor);
if (!handler) throw new Error('No handler registered');
return await handler.handle(message);
}
}
An architecture where the application's side-effects (Commands) and data retrieval (Queries) are handled by distinct, optimized paths.
development
Generate breadboard circuit mockups and visual diagrams using HTML5 Canvas drawing techniques. Use when asked to create circuit layouts, visualize electronic component placements, draw breadboard diagrams, mockup 6502 builds, generate retro computer schematics, or design vintage electronics projects. Supports 555 timers, W65C02S microprocessors, 28C256 EEPROMs, W65C22 VIA chips, 7400-series logic gates, LEDs, resistors, capacitors, switches, buttons, crystals, and wires.
development
Apply lean thinking to UX: hypothesis-driven design, collaborative sketching, and rapid experiments instead of heavy deliverables. Use when the user mentions "Lean UX", "design hypothesis", "UX experiment", "collaborative design", or "outcome over output". Covers hypothesis statements, MVPs for UX, and cross-functional collaboration. For Build-Measure-Learn, see lean-startup. For usability audits, see ux-heuristics.
development
Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", or "test assumptions". Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done.
tools
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.