本文へ移動
cccskills
無料GitHub で公開

cqrs-event-sourcing-architect

CQRS pattern, event stores, projections, eventual consistency. Activate on: CQRS, event sourcing, event store, read model, projection, aggregate, domain event, command handler. NOT for: message broker setup (use event-driven-architecture-expert), database optimization (use data-warehouse-optimizer).

インストール方法を見る

含まれるファイル(1)

  • SKILL.md5.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

CQRS & Event Sourcing Architect

Design systems that separate read/write models and derive state from immutable event logs using EventStoreDB, Marten, or custom stores.

Activation Triggers

Activate on: "CQRS", "event sourcing", "event store", "read model", "projection", "aggregate", "domain event", "command handler", "EventStoreDB", "Marten"

NOT for: Message broker infrastructure → event-driven-architecture-expert | Database query optimization → data-warehouse-optimizer | API design → api-architect

Quick Start

  1. Model aggregates — identify consistency boundaries in your domain
  2. Define domain events — past-tense facts: OrderPlaced, ItemShipped
  3. Implement command handlers — validate, then emit events
  4. Build event store — append-only log (EventStoreDB, PostgreSQL, DynamoDB)
  5. Create projections — materialize read models from event streams

Core Capabilities

DomainTechnologies
Event StoresEventStoreDB 24.x, Marten 7.x, custom PostgreSQL
FrameworksNestJS CQRS, Axon (JVM), Eventuous, Commanded (Elixir)
ProjectionsIn-memory, PostgreSQL views, Elasticsearch, Redis
SerializationJSON with versioned schemas, Protobuf
TestingGiven-When-Then event test pattern

Architecture Patterns

CQRS Separation

Command Side                    Query Side
─────────────                   ──────────
POST /orders                    GET /orders/:id
     ↓                               ↑
Command Handler                 Read Model (Postgres/ES)
     ↓                               ↑
Aggregate.apply(event)          Projection Handler
     ↓                               ↑
Event Store (append)  ──────→  Event Subscription

Aggregate with Event Replay

class OrderAggregate {
  private status: 'draft' | 'placed' | 'shipped' = 'draft';
  private items: OrderItem[] = [];
  private uncommitted: DomainEvent[] = [];

  place(command: PlaceOrderCommand): void {
    if (this.status !== 'draft') throw new Error('Order already placed');
    this.apply(new OrderPlaced({ orderId: command.orderId, items: command.items }));
  }

  private apply(event: DomainEvent): void {
    this.when(event);           // mutate state
    this.uncommitted.push(event); // track for persistence
  }

  private when(event: DomainEvent): void {
    if (event instanceof OrderPlaced) {
      this.status = 'placed';
      this.items = event.items;
    }
  }

  static rehydrate(events: DomainEvent[]): OrderAggregate {
    const agg = new OrderAggregate();
    events.forEach(e => agg.when(e));
    return agg;
  }
}

Projection Rebuild Strategy

1. Stop projection subscription
2. Drop/truncate read model table
3. Replay all events from position 0
4. Resume subscription at current position

Key: projections are disposable — the event log is the source of truth

Anti-Patterns

  1. CRUD in disguise — if you just save/load entities without events, you have CRUD with extra steps
  2. Anemic events — events like OrderUpdated are useless; capture what changed specifically
  3. Querying the event store — never query raw events for read use cases; build a projection
  4. Deleting events — event stores are append-only; use compensating events for corrections
  5. Ignoring eventual consistency — read models lag behind writes; design UIs to handle this gracefully

Quality Checklist

  • Aggregates enforce invariants before emitting events
  • Events are immutable, past-tense, and contain all needed data
  • Event store is append-only with optimistic concurrency
  • Projections can be rebuilt from scratch without data loss
  • Event versioning strategy defined (upcasting or schema registry)
  • Given-When-Then tests cover all aggregate state transitions
  • Snapshot strategy defined for aggregates with long event streams (>1000 events)
  • Eventual consistency communicated clearly in API contracts
  • Idempotent projection handlers (replaying events produces same read model)

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

日本語の概要は準備中です。原文の説明を表示しています。

curiositech/port-daddy22026年10月12日 更新

curiositech のスキルをすべて見る

このスキルの問題を報告する