Which message brokers does this skill support?

This skill supports Kafka for high-volume streaming, RabbitMQ for complex routing, Redis Streams for simple real-time messaging, and Postgres for moderate-volume task queues.

What is the recommended Python library for these patterns?

The skill recommends FastStream for a unified API and automatic Pydantic validation, though it also provides code for aiokafka, aio-pika, and redis-py.

Can I use this for real-time data processing?

Absolutely. It includes specific patterns for high-throughput Kafka streaming and Redis Streams, which are ideal for real-time data pipelines.

Does it include dead-letter queue (DLQ) implementations?

Yes, the skill provides production-ready patterns for handling failures, including retry counts and routing failed messages to dead-letter queues for manual inspection.

How do I choose between Kafka and RabbitMQ?

The skill includes a decision flowchart: choose Kafka for high-volume (>50k msg/s) event streaming and RabbitMQ for complex routing needs like headers or topic exchanges.

Message Queue Patterns

Name: Message Queue Patterns
Author: yonatangross

byyonatangross

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API Development

Implements robust asynchronous communication and event-driven architectures using Kafka, RabbitMQ, Redis, and Postgres.

This skill provides specialized guidance for designing and implementing distributed messaging systems within Claude Code. It offers production-ready patterns for a variety of message brokers, including Kafka for high-throughput streaming, RabbitMQ for complex routing, Redis Streams for lightweight pub/sub, and Postgres for simpler queueing needs. By leveraging unified APIs like FastStream and low-level drivers like aiokafka and aio-pika, developers can build decoupled, scalable microservices with reliable delivery guarantees, dead-letter handling, and optimal retry logic.

Key Features

01Production-grade producer and consumer implementations with proper acknowledgments

02Multi-broker support for Kafka, RabbitMQ, Redis Streams, and Postgres

03Integration with FastStream for unified API and Pydantic validation

04Reliable messaging patterns including dead-letter queues and retries

0569 GitHub stars

06Decision framework for choosing brokers based on throughput and persistence

Use Cases

01Building high-throughput event streaming pipelines for real-time data processing

02Implementing background task distribution and work queues for resource-intensive jobs

03Decoupling microservices to improve system scalability and fault tolerance

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit message-queues

For use in Claude.ai and ChatGPT

Download Skill