rabbitmq

Included with Lifetime

$97 forever

RabbitMQ message broker with AMQP protocol. Covers exchanges, queues, bindings, and messaging patterns. Use for reliable message delivery and complex routing scenarios. USE WHEN: user mentions "rabbitmq", "amqp", "exchanges", "routing patterns", "topic exchange", "fanout", asks about "message routing", "work queues", "request/reply", "flexible routing" DO NOT USE FOR: high-throughput streaming - use `kafka` or `pulsar`; cloud-native - use `nats`; AWS-native - use `sqs`; JMS required - use `activemq`; simple pub/sub - use `redis-pubsub`

Backend & APIs

What this skill does

# RabbitMQ Core Knowledge

> **Full Reference**: See [advanced.md](advanced.md) for producer patterns (Node.js, Java, Python, Go), consumer patterns, security configuration, and high availability setup.

> **Deep Knowledge**: Use `mcp__documentation__fetch_docs` with technology: `rabbitmq` for comprehensive documentation.

## Quick Start (Docker)

```yaml
services:
  rabbitmq:
    image: rabbitmq:3-management
    ports:
      - "5672:5672"   # AMQP
      - "15672:15672" # Management UI
    environment:
      - RABBITMQ_DEFAULT_USER=admin
      - RABBITMQ_DEFAULT_PASS=admin
```

## Core Concepts

| Concept | Description |
|---------|-------------|
| **Exchange** | Routes messages to queues |
| **Queue** | Buffer that stores messages |
| **Binding** | Rule linking exchange to queue |
| **Routing Key** | Message attribute for routing |

## Architecture

```
Producer ──▶ Exchange ──binding──▶ Queue ──▶ Consumer
                │
                ├── direct   (exact routing key match)
                ├── topic    (pattern matching)
                ├── fanout   (broadcast to all)
                └── headers  (header-based routing)
```

## Exchange Types

| Type | Routing | Use Case |
|------|---------|----------|
| **direct** | Exact key match | Task queues, RPC |
| **topic** | Pattern (*.#) | Event routing |
| **fanout** | Broadcast all | Notifications |
| **headers** | Header match | Complex routing |

```
# Topic exchange patterns
order.*        → matches order.created, order.updated
order.#        → matches order.created, order.item.added
*.created      → matches order.created, user.created
```

## Queue Configuration

| Argument | Description |
|----------|-------------|
| `x-message-ttl` | Message expiration (ms) |
| `x-max-length` | Max queue size |
| `x-dead-letter-exchange` | DLX for rejected messages |
| `x-dead-letter-routing-key` | DLQ routing key |
| `x-queue-type` | classic, quorum, stream |

## When NOT to Use This Skill

- Event streaming with replay - Kafka provides persistent log
- Ultra-high throughput (>100k msg/s) - Kafka or Pulsar scale better
- AWS-native architecture - SQS integrates better
- Lightweight messaging - NATS has simpler operations
- JMS compliance required - ActiveMQ provides JMS API

## Anti-Patterns

| Anti-Pattern | Why It's Bad | Solution |
|--------------|--------------|----------|
| No prefetch limit | Consumer overwhelmed | Set `prefetch` to 10-50 |
| Classic queues in HA | Data loss on node failure | Use quorum queues |
| No DLX configured | Poison messages loop forever | Configure dead letter exchange |
| Automatic acks | Message loss on crash | Use manual acks |
| No publisher confirms | Silent message loss | Enable confirms for critical messages |

## Quick Troubleshooting

| Issue | Likely Cause | Fix |
|-------|--------------|-----|
| Messages piling up | Slow/dead consumer | Check consumer count and processing |
| High memory usage | Too many unacked messages | Reduce prefetch, add consumers |
| Messages not routed | Wrong routing key | Check exchange-queue bindings |
| Duplicate messages | Consumer crashed before ack | Implement idempotent processing |
| Publisher confirms timeout | Broker overloaded | Reduce publish rate |

## Production Checklist

- [ ] TLS/SSL enabled
- [ ] Users with least privilege
- [ ] Virtual hosts for isolation
- [ ] Quorum queues for HA
- [ ] Dead letter exchanges configured
- [ ] Message TTL set
- [ ] Prefetch limits configured
- [ ] Publisher confirms enabled
- [ ] Monitoring dashboards
- [ ] Alerting configured

## Reference Documentation

Available topics: `basics`, `exchanges`, `queues`, `consumers`, `clustering`, `production`

Files: 2

Size: 11.4 KB

Complexity: 26/100

Category: Backend & APIs

Source: https://github.com/claude-dev-suite/claude-dev-suite/tree/main/skills/messaging/rabbitmq

Related in Backend & APIs

jfrog

Included

Interact with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.

Backend & APIsscripts

cupynumeric-migration-readiness

Included

Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.

Backend & APIsscripts

alibabacloud-data-agent-skill

Included

Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".

Backend & APIsscripts

token-optimizer

Included

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

Backend & APIsscripts

resend-cli

Included

Use this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.

Backend & APIsscripts

alibabacloud-odps-maxframe-coding

Included

Use this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.

Backend & APIsscripts