Is this skill suitable for high-throughput system design?

Yes, it includes specific guidance on partition count calculation, throughput-per-partition limits, and how global ordering affects performance.

Does it include error handling and retry logic?

Absolutely. It includes comprehensive retry configurations, circuit breaker thresholds, and standardized Dead Letter Queue (DLQ) routing patterns.

Can it help with exactly-once delivery semantics?

Yes, it provides validation rules and implementation patterns for idempotent consumers and transactional producers to achieve exactly-once delivery.

What message brokers does this skill support?

The skill provides detailed patterns, rationale, and configurations for Kafka, RabbitMQ, AWS SQS, and Apache Pulsar.

Event-Driven Architecture & Messaging

Name: Event-Driven Architecture & Messaging
Author: pluginagentmarketplace

bypluginagentmarketplace

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API 开发

Architects production-grade event-driven systems using industry-standard messaging patterns and delivery guarantees.

关于

This skill provides Claude with specialized knowledge to design and implement robust event-driven architectures, covering everything from message broker selection (Kafka, RabbitMQ, SQS) to advanced patterns like CQRS and Event Sourcing. It guides users through complex technical decisions regarding throughput, latency, and delivery semantics while ensuring high reliability through built-in retry logic, dead-letter queue configurations, and comprehensive observability metrics. It is an essential tool for developers building scalable, decoupled, and fault-tolerant distributed systems.

主要功能

Broker selection logic for Kafka, RabbitMQ, Pulsar, and AWS SQS
Standardized observability schemas for messaging metrics and logging
Advanced retry strategies including exponential backoff and jitter
1 GitHub stars
Comprehensive Dead Letter Queue (DLQ) and error handling configurations
Implementation patterns for Event Sourcing and CQRS architectures

使用场景

Implementing asynchronous background processing using RabbitMQ work queues
Designing high-throughput data streaming pipelines with Kafka
Building scalable, auditable systems using Event Sourcing and CQRS

关于

主要功能

Broker selection logic for Kafka, RabbitMQ, Pulsar, and AWS SQS
Standardized observability schemas for messaging metrics and logging
Advanced retry strategies including exponential backoff and jitter
1 GitHub stars
Comprehensive Dead Letter Queue (DLQ) and error handling configurations
Implementation patterns for Event Sourcing and CQRS architectures

使用场景

Implementing asynchronous background processing using RabbitMQ work queues
Designing high-throughput data streaming pipelines with Kafka
Building scalable, auditable systems using Event Sourcing and CQRS