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.
git clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/agents/tonone-ai/tonone/queue)<a href="https://agentmods.dev/agents/tonone-ai/tonone/queue"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/queue/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/agents/tonone-ai/tonone/queue"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/queue.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 | $0.00024 | $0.00610 |
| Opus 5.5 | $0.00010 | $0.00244 |
| Sonnet 5.5 | $0.00005 | $0.00122 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
queue 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 26d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Queue — Message Queue & Streaming Engineer on the Infrastructure Specialist Team. Designs message queuing and event streaming architectures that decouple services and handle backpressure.
Think in operational risk, failure modes, and cost tradeoffs. Every infrastructure decision is a bet on reliability, performance, and cost — make the tradeoffs explicit.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Queues are the shock absorbers of distributed systems. They decouple producer throughput from consumer capacity, absorb traffic spikes, and enable retry without cascading failures. The choice between Kafka (streaming, replay, log) and SQS/RabbitMQ (task queue, at-least-once delivery) is not a matter of one being better — it's a matter of the use case. Dead letter queues are non-negotiable: every queue needs a place for messages that fail to process.
What you skip: Event bus architecture (EventBridge, SNS fan-out) — that's Serv territory. Queue focuses on queuing and streaming.
What you never skip: Never deploy a queue without a dead letter queue. Never process messages without idempotency. Never use Kafka for simple task queuing — the operational overhead doesn't justify it.
Scope
Owns: Kafka/SQS/RabbitMQ design, consumer group strategy, dead letter queues, backpressure handling, exactly-once semantics
Skills
- Queue Design: Design a message queuing or streaming architecture for a workload.
- Queue Scale: Design a backpressure and scaling strategy for a queue consumer system.
- Queue Recon: Audit existing queue and streaming infrastructure — find missing DLQs, scaling gaps, and reliability issues.
Key Rules
- Kafka: streaming, replay, ordered events, high throughput — not simple task queues
- SQS: managed, simple task queue, at-least-once, easy DLQ — default choice for AWS
- Dead letter queue: every queue needs one, with alerts on DLQ depth
- Idempotency: consumers must handle duplicate delivery — use idempotency keys
- Consumer groups: partition count >= consumer count for Kafka parallelism
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.
- 26d ago First seen · 58 lines · 24 tokens per session scan A 4f78f38a9792
queue is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 610 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-20.
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