cqrs-event-sourcing
About
This skill provides CQRS and Event Sourcing patterns to build scalable, auditable systems by separating read and write models. It's ideal for applications requiring full audit trails, temporal queries, or handling complex domain logic at high scale. Key capabilities include complete historical tracking, event replay, and optimized independent model projections.
Quick Install
Claude Code
Recommended/plugin add https://github.com/majiayu000/claude-skill-registrygit clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/cqrs-event-sourcingCopy and paste this command in Claude Code to install this skill
Documentation
CQRS and Event Sourcing Patterns
Expert guidance for implementing Command Query Responsibility Segregation (CQRS) and Event Sourcing patterns to build scalable, auditable systems with complete historical tracking and optimized read/write models.
When to Use This Skill
- Building systems requiring complete audit trails and compliance
- Implementing temporal queries ("show me the state at time T")
- Designing high-scale applications with complex domain logic
- Creating systems with significantly different read and write patterns
- Building event-driven architectures with historical replay capability
- Implementing systems requiring multiple read model projections
- Designing applications where understanding "what happened" is critical
- Building collaborative systems with conflict resolution needs
Core Principles
1. Command Query Separation
Separate operations that change state (commands) from operations that read state (queries).
| Commands (Write) | Queries (Read) |
|---|---|
| Express intent (CreateOrder, UpdatePrice) | Return data, never change state |
| Can be rejected (validation failures) | Can be cached and optimized |
| Return success/failure, not data | Multiple models for different needs |
| Change system state | Eventually consistent with writes |
2. Events as Source of Truth
Store state changes as immutable events rather than current state snapshots.
Traditional: Store what IS → UPDATE users SET email = 'new@email.com'
Event Sourcing: Store what HAPPENED → APPEND UserEmailChanged event
Result: Complete history, temporal queries, audit trail
3. Eventual Consistency
Accept temporary inconsistency between write and read models for scalability.
4. Domain-Driven Design Integration
- Aggregates enforce business invariants
- Events represent domain facts
- Commands express domain operations
- Bounded contexts define consistency boundaries
Quick Reference
| Task | Load reference |
|---|---|
| CQRS implementation patterns | skills/cqrs-event-sourcing/references/cqrs-patterns.md |
| Event sourcing & snapshots | skills/cqrs-event-sourcing/references/event-sourcing.md |
| EventStoreDB & Axon Framework | skills/cqrs-event-sourcing/references/event-store-tech.md |
| Consistency patterns | skills/cqrs-event-sourcing/references/consistency-patterns.md |
| Best practices checklist | skills/cqrs-event-sourcing/references/best-practices.md |
Workflow
- Identify if CQRS/ES is appropriate (high audit, temporal, or scale needs)
- Design commands expressing user intent
- Define events as immutable facts (past tense naming)
- Implement aggregates as consistency boundaries
- Create projections optimized for specific query needs
- Handle eventual consistency across bounded contexts
Common Mistakes
- Using CQRS for simple CRUD applications (overkill)
- Large aggregates that span multiple consistency boundaries
- Modifying or deleting events after publication
- Skipping command validation before aggregate processing
- Missing idempotency in event handlers
- No versioning strategy for event schema evolution
- Tight coupling between aggregates (use ID references only)
Resources
- Books: "Implementing Domain-Driven Design" (Vernon), "Event Sourcing & CQRS" (Betts et al)
- Sites: cqrs.wordpress.com, eventstore.com/blog, axoniq.io/resources
- Tools: EventStoreDB, Axon Framework, Marten, Eventuous
- Patterns: Event Sourcing, CQRS, Process Manager, Saga, Snapshot
GitHub Repository
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