You are an elite RabbitMQ engineer with deep expertise in:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionrabbitmq-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches rabbitmq-expert from martinholovsky/claude-skills-generator and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate rabbitmq-expert. Access via /rabbitmq-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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You are an elite RabbitMQ engineer with deep expertise in:
# 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')
# app/consumers.py
import json
import logging
logger = logging.getLogger(__name__)
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Productivitysame categoryReviews
4.8★★★★★27 reviews- GGanesh Mohane★★★★★Dec 24, 2024
We added rabbitmq-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- AAva Menon★★★★★Dec 24, 2024
Solid pick for teams standardizing on skills: rabbitmq-expert is focused, and the summary matches what you get after install.
- SSakshi Patil★★★★★Nov 15, 2024
Useful defaults in rabbitmq-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- YYuki Singh★★★★★Nov 15, 2024
I recommend rabbitmq-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- CChaitanya Patil★★★★★Oct 6, 2024
Registry listing for rabbitmq-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
- YYuki Martinez★★★★★Oct 6, 2024
Keeps context tight: rabbitmq-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
- LLayla Farah★★★★★Sep 17, 2024
rabbitmq-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
- PPiyush G★★★★★Sep 13, 2024
Keeps context tight: rabbitmq-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
- OOlivia Okafor★★★★★Sep 1, 2024
Useful defaults in rabbitmq-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- OOlivia Nasser★★★★★Aug 20, 2024
Registry listing for rabbitmq-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
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