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.
npx skills add ancoleman/ai-design-components --skill using-message-queuesgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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.
[](https://agentmods.dev/skills/ancoleman/ai-design-components/using-message-queues)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/using-message-queues"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/using-message-queues/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.
<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/using-message-queues"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/using-message-queues.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.02885 |
| Opus 5.5 | $0.00030 | $0.01154 |
| Sonnet 5.5 | $0.00015 | $0.00577 |
| Haiku 4.5 | $0.00007 | $0.00288 |
Grade A, and why
using-message-queues scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
await fetch(job.data.url, { How it starts
The opening of the file, as written. The whole thing — 413 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Message Queues
Implement asynchronous communication patterns for event-driven architectures, background job processing, and service decoupling.
When to Use This Skill
Use message queues when:
- Long-running operations block HTTP requests (report generation, video processing)
- Service decoupling required (microservices, event-driven architecture)
- Guaranteed delivery needed (payment processing, order fulfillment)
- Event streaming for analytics (log aggregation, metrics pipelines)
- Workflow orchestration for complex processes (multi-step sagas, human-in-the-loop)
- Background job processing (email sending, image resizing)
Broker Selection Decision Tree
Choose message broker based on primary need:
Event Streaming / Log Aggregation
→ Apache Kafka
- Throughput: 500K-1M msg/s
- Replay events (event sourcing)
- Exactly-once semantics
- Long-term retention
- Use: Analytics pipelines, CQRS, event sourcing
Simple Background Jobs
→ Task Queues
- Python → Celery + Redis
- TypeScript → BullMQ + Redis
- Go → Asynq + Redis
- Use: Email sending, report generation, webhooks
Complex Workflows / Sagas
→ Temporal
- Durable execution (survives restarts)
- Saga pattern support
- Human-in-the-loop workflows
- Use: Order processing, AI agent orchestration
Request-Reply / RPC Patterns
→ NATS
- Built-in request-reply
- Sub-millisecond latency
- Cloud-native, simple operations
- Use: Microservices RPC, IoT command/control
Complex Message Routing
→ RabbitMQ
- Exchanges (direct, topic, fanout, headers)
- Dead letter exchanges
- Message TTL, priorities
- Use: Multi-consumer patterns, pub/sub
Already Using Redis
→ Redis Streams
- No new infrastructure
- Simple consumer groups
- Moderate throughput (100K+ msg/s)
- Use: Notification queues, simple job queues
Performance Comparison
| Broker | Throughput | Latency (p99) | Best For |
|---|---|---|---|
| Kafka | 500K-1M msg/s | 10-50ms | Event streaming |
| NATS JetStream | 200K-400K msg/s | Sub-ms to 5ms | Cloud-native microservices |
| RabbitMQ | 50K-100K msg/s | 5-20ms | Task queues, complex routing |
| Redis Streams | 100K+ msg/s | Sub-ms | Simple queues, caching |
What ships with it
19 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/bullmq-webhook-processor/README.md 3.6 KB
- examples/celery-image-processing/README.md 3.7 KB
- examples/kafka-python/consumer.py 7.4 KB runs code
- examples/kafka-python/producer.py 5.1 KB runs code
- examples/kafka-python/README.md 2.6 KB
- examples/temporal-order-processing/activities.py 5.6 KB runs code
- examples/temporal-order-processing/workflow.py 8.5 KB runs code
- examples/temporal-order-saga/README.md 5.2 KB
- outputs.yaml 21 KB
- references/bullmq.md 7.6 KB
- references/celery.md 8.3 KB
- references/event-patterns.md 18 KB
- references/kafka.md 16 KB
- references/nats.md 13 KB
- references/rabbitmq.md 16 KB
- references/redis-streams.md 15 KB
- references/temporal-workflows.md 21 KB
- scripts/kafka_producer_consumer.py 5.1 KB runs code
- scripts/validate_message_schema.py 5.6 KB runs code
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.
- 1mo ago First seen · 413 lines · 74 tokens per session scan A 9f7929b3d22a
using-message-queues is a skill published in the GitHub repository ancoleman/ai-design-components (526 stars, last pushed 10mo ago), licensed MIT. It adds 74 tokens to every session and 2,885 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
form-strategy
Design forms that convert, validate well, resist spam, and integrate cleanly with downstream systems. Use this skill when designing or auditing any form (contact, signup, checkout, multi-step, embedded), planning validation logic, fighting spam, choosing form tooling, or improving form conversion. Triggers on form…
mcp-server-builder
Use to build, wire up, or debug an MCP (Model Context Protocol) server that gives Claude access to an internal API, database, or SaaS tool. Trigger when the user wants Claude to connect to their own system, asks for an MCP server, needs custom tools for Claude Code or the Claude app, or an existing server's tools are…
api-endpoint-scaffolder
Generate REST API endpoints with proper structure, validation, error handling, and types. Use when creating new API routes, endpoints, or backend services.
netlify-development
Best practices for building and deploying Netlify sites, covering serverless and edge functions, Netlify Blobs storage, the Image CDN, and build configuration. Use when writing Netlify Functions or Edge Functions, configuring netlify.toml or redirects/headers, managing environment variables across deploy contexts…
api-security-best-practices
Implement secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities.
apollo-graphql
Guidelines for developing GraphQL APIs and React applications using Apollo Client for state management, data fetching, and caching.