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 sandeepmvl/rails-skills --skill 44-rabbitmq-railsgit clone --depth 1 https://github.com/sandeepmvl/rails-skillsWrote 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/sandeepmvl/rails-skills/44-rabbitmq-rails)<a href="https://agentmods.dev/skills/sandeepmvl/rails-skills/44-rabbitmq-rails"><img src="https://agentmods.dev/badge/skills/sandeepmvl/rails-skills/44-rabbitmq-rails/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/sandeepmvl/rails-skills/44-rabbitmq-rails"><img src="https://agentmods.dev/badge/skills/sandeepmvl/rails-skills/44-rabbitmq-rails.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 | $0.00107 | $0.02407 |
| Opus 5 | $0.00053 | $0.01203 |
| Sonnet 5 | $0.00021 | $0.00481 |
| Haiku 4.5 | $0.00011 | $0.00241 |
Grade A, and why
rabbitmq-rails 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RabbitMQ in Rails
RabbitMQ is the right tool for complex routing (one publisher, many topologies of consumers), heterogeneous workers (Ruby + Python + Go all reading the same queue), and work distribution. Wrong tool when you need replay, long retention, or stream semantics — that's Kafka.
The opinion
Use
bunny(synchronous AMQP client) for producing. Usesneakersfor consuming long-running workers (it integrates with Rails). Always declare durable exchanges + queues + persistent messages. Acknowledge manually (manual_ack: true) after work succeeds — autoack drops messages on crash. Set a saneprefetch(10-50) per consumer. Use a dead-letter exchange for failures.
Counter-positions:
- Kafka for event streams with replay / fan-out / retention. RabbitMQ deletes messages after ack.
- Solid Queue / Sidekiq for in-app jobs. Don't pull in RabbitMQ for a delayed email.
- Redis Streams for lightweight Kafka-like patterns (see
redis-streams-rails).
Setup
# Gemfile
gem "bunny", "~> 2.22"
gem "sneakers", "~> 2.12"
# config/initializers/rabbitmq.rb
RABBIT = Bunny.new(
ENV.fetch("RABBITMQ_URL"),
automatically_recover: true,
network_recovery_interval: 5,
threaded: true
)
RABBIT.start
at_exit { RABBIT.close }
# config/initializers/sneakers.rb
Sneakers.configure(
amqp: ENV.fetch("RABBITMQ_URL"),
daemonize: false,
workers: 4,
threads: 5,
prefetch: 10,
log: STDOUT
)
Sneakers.logger.level = Logger::INFO
Pattern 1: Publishing
# Long-lived publisher channel — opening a fresh channel per publish is an
# anti-pattern that exhausts broker channel limits at high publish rates.
class OrderEventPublisher
CHANNEL = RABBIT.create_channel
EXCHANGE = CHANNEL.topic("events.orders", durable: true)
def self.publish(event_type:, payload:)
EXCHANGE.publish(
payload.to_json,
routing_key: event_type, # e.g. "order.placed"
persistent: true, # durable on disk
content_type: "application/json",
message_id: SecureRandom.uuid, # idempotency aid for consumers
timestamp: Time.current.to_i
)
end
end
# Usage
OrderEventPublisher.publish(
event_type: "order.placed",
payload: { order_id: 42, account_id: 7, total_cents: 12_500 }
)
What ships with it
1 file 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.
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.
- 12d ago First seen · 285 lines · 107 tokens per session scan A 46252b3029e6
rabbitmq-rails is a skill published in the GitHub repository sandeepmvl/rails-skills (21 stars, last pushed 3mo ago), licensed MIT. It adds 107 tokens to every session and 2,407 once invoked, about $0.0005 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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