rabbitmq-expert

rabbitmq-expert is a skill for Claude Code, Codex from martinholovsky/claude-skills-generator. It costs 53 tokens per session (9,749 once invoked), scanned A, original, Unlicense.

A RabbitMQ expert for designing and operating message queues. RabbitMQ is a message broker that lets applications send work or events to one another without needing to run at the same time.

In plain words
What is it for?
Use it to design exchanges, queues, and publish-subscribe flows; configure retries, acknowledgements, dead-letter handling, clustering, security, and monitoring; and test message processing.
Why use it?
It helps prevent lost messages and unreliable processing when systems are busy, disconnected, or partially failing.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: model in frontmatter.

Good fit Use it to design exchanges, queues, and publish-subscribe flows; configure retries, acknowledgements, dead-letter handling, clustering, security, and monitoring; and test message processing.

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Install with agentmods
npx agentmods add skills/martinholovsky/claude-skills-generator/rabbitmq-expert
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add martinholovsky/claude-skills-generator --skill rabbitmq-expert
Clone the repo
git clone --depth 1 https://github.com/martinholovsky/claude-skills-generator

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for rabbitmq-expert

README.md
[![agentmods](https://agentmods.dev/badge/skills/martinholovsky/claude-skills-generator/rabbitmq-expert/github.svg)](https://agentmods.dev/skills/martinholovsky/claude-skills-generator/rabbitmq-expert)
Your own site
<a href="https://agentmods.dev/skills/martinholovsky/claude-skills-generator/rabbitmq-expert"><img src="https://agentmods.dev/badge/skills/martinholovsky/claude-skills-generator/rabbitmq-expert/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for rabbitmq-expert

Your own site · 80×15
<a href="https://agentmods.dev/skills/martinholovsky/claude-skills-generator/rabbitmq-expert"><img src="https://agentmods.dev/badge/skills/martinholovsky/claude-skills-generator/rabbitmq-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 9,749 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 Mar 2026
  • Snyk fail 15 Feb 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.09749
Opus 5 $0.00026 $0.04875
Sonnet 5 $0.00011 $0.01950
Haiku 4.5 $0.00005 $0.00975

Measured 11d ago against content hash 3a1f7f1c8d93, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

rabbitmq-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/rabbitmq-expert/SKILL.md · 1,556 lines

How it starts

The opening of the file, as written. The whole thing — 1,556 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RabbitMQ Message Broker Expert

1. Overview

You are an elite RabbitMQ engineer with deep expertise in:


2. Core Principles

  1. TDD First - Write tests before implementation; verify message flows with test consumers
  2. Performance Aware - Optimize prefetch, batching, and connection pooling from the start
  3. Reliability Obsessed - No message loss through durability, confirms, and proper acks
  4. Security by Default - TLS everywhere, no default credentials, proper isolation
  5. Observable Always - Monitor queue depth, throughput, latency, and cluster health
  6. Design for Failure - Dead letter exchanges, retries, circuit breakers

3. Implementation Workflow (TDD)

Step 1: Write Failing Test First

# tests/test_message_queue.py
import pytest
import pika
import json
import time
from unittest.mock import MagicMock, patch

class TestOrderProcessor:
    """Test order message processing with RabbitMQ"""

    @pytest.fixture
    def mock_channel(self):
        """Create mock channel for unit tests"""
        channel = MagicMock()
        channel.basic_qos = MagicMock()
        channel.basic_consume = MagicMock()
        channel.basic_ack = MagicMock()
        channel.basic_nack = MagicMock()
        return channel

    @pytest.fixture
    def rabbitmq_connection(self):
        """Create real connection for integration tests"""
        try:
            connection = pika.BlockingConnection(
                pika.ConnectionParameters(
                    host='localhost',
                    connection_attempts=3,
                    retry_delay=1
                )
            )
            yield connection
            connection.close()
        except pika.exceptions.AMQPConnectionError:
            pytest.skip("RabbitMQ not available")

    def test_message_acknowledged_on_success(self, mock_channel):
        """Test that successful processing sends ack"""
        from app.consumers import OrderConsumer

        consumer = OrderConsumer(mock_channel)
        message = json.dumps({"order_id": 123, "status": "pending"})

        # Create mock method with delivery tag
        method = MagicMock()
        method.delivery_tag = 1

        # Process message
        consumer.process_message(mock_channel, method, None, message.encode())

        # Verify ack was called
        mock_channel.basic_ack.assert_called_once_with(delivery_tag=1)
        mock_channel.basic_nack.assert_not_called()

    def test_message_rejected_to_dlx_on_failure(self, mock_channel):
        """Test that failed processing sends to DLX"""
        from app.consumers import OrderConsumer

        consumer = OrderConsumer(mock_channel)
        invalid_message = b"invalid json"

        method = MagicMock()
        method.delivery_tag = 2

        # Process invalid message
        consumer.process_message(mock_channel, method, None, invalid_message)

        # Verify nack was called without requeue (sends to DLX)
        mock_channel.basic_nack.assert_called_once_with(
            delivery_tag=2,
            requeue=False
        )

    def test_prefetch_count_configured(self, mock_channel):
        """Test that prefetch count is properly set"""
        from app.consumers import OrderConsumer

        consumer = OrderConsumer(mock_channel, prefetch_count=10)
        consumer.setup()

        mock_channel.basic_qos.assert_called_once_with(prefetch_count=10)

    def test_publisher_confirms_enabled(self, rabbitmq_connection):
        """Integration test: verify publisher confirms work"""
        channel = rabbitmq_connection.channel()
        channel.confirm_delivery()

        # Declare test queue
        channel.queue_declare(queue='test_confirms', durable=True)

        # Publish with confirms - should not raise
        channel.basic_publish(
            exchange='',
            routing_key='test_confirms',
            body=b'test message',
            properties=pika.BasicProperties(delivery_mode=2)
        )

        # Cleanup
        channel.queue_delete(queue='test_confirms')

    def test_dlx_receives_rejected_messages(self, rabbitmq_connection):
        """Integration test: verify DLX receives rejected messages"""
        channel = rabbitmq_connection.channel()

        # Setup DLX
        channel.exchange_declare(exchange='test_dlx', exchange_type='fanout')
        channel.queue_declare(queue='test_dead_letters')
        channel.queue_bind(exchange='test_dlx', queue='test_dead_letters')

        # Setup main queue with DLX
        channel.queue_declare(
            queue='test_main',
            arguments={'x-dead-letter-exchange': 'test_dlx'}
        )

        # Publish and reject message
        channel.basic_publish(
            exchange='',
            routing_key='test_main',
            body=b'will be rejected'
        )

        # Get and reject message
        method, props, body = channel.basic_get('test_main')
        if method:
            channel.basic_nack(delivery_tag=method.delivery_tag, requeue=False)

        # Wait for DLX delivery
        time.sleep(0.1)

        # Verify message arrived in DLX queue
        method, props, body = channel.basic_get('test_dead_letters')
        assert body == b'will be rejected'

        # Cleanup
        channel.queue_delete(queue='test_main')
        channel.queue_delete(queue='test_dead_letters')
        channel.exchange_delete(exchange='test_dlx')

Read the full file on GitHub · 1,556 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 1,556 lines · 53 tokens per session scan A 3a1f7f1c8d93

Subscribe to this mod's changes

rabbitmq-expert is a skill published in the GitHub repository martinholovsky/claude-skills-generator (45 stars, last pushed 9mo ago), licensed Unlicense. It adds 53 tokens to every session and 9,749 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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