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 arbazkhan971/godmode --skill queuegit clone --depth 1 https://github.com/arbazkhan971/godmodeWrote 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/arbazkhan971/godmode/queue)<a href="https://agentmods.dev/skills/arbazkhan971/godmode/queue"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/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/skills/arbazkhan971/godmode/queue"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/queue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 151 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00026 | $0.01364 |
| Opus 5 | $0.00013 | $0.00682 |
| Sonnet 5 | $0.00005 | $0.00273 |
| Haiku 4.5 | $0.00003 | $0.00136 |
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 7d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activate When
/godmode:queue, "add background jobs", "set up queue"- "retry failed jobs", "dead letter queue", "job stuck"
- "rate limit processing", "backpressure"
Workflow
1. Requirements
grep -r "bullmq\|celery\|sidekiq\|kafkajs\|sqs" \
package.json requirements.txt 2>/dev/null
Use case: job processing | event streaming | pub/sub
Volume: <msg/sec>, Payload: <avg size>
Ordering: strict FIFO | partition | best-effort
Delivery: exactly-once | at-least-once | at-most-once
Latency: <100ms | <1s | <30s
Existing infra: Redis | PostgreSQL | AWS | none
2. Technology Selection
SQS: AWS-native, managed, unlimited throughput
BullMQ: Node.js+Redis, 10-50K/sec, great dashboard
Celery: Python+Redis/RabbitMQ, complex workflows
Kafka: millions/sec, partition-ordered, replayable
RabbitMQ: complex routing, 10-50K/sec
Redis Streams: lightweight, 100K+/sec
PG SKIP LOCKED: no new infra, <1K jobs/sec
IF AWS simple: SQS. IF Node.js+Redis: BullMQ. IF event streaming: Kafka. IF low volume+PG: SKIP LOCKED.
3. Architecture
Producers -> Broker
-> [high-priority] -> Worker Pool A (concurrency 10)
-> [default] -> Worker Pool B (concurrency 20)
-> [bulk] -> Worker Pool C (concurrency 5)
-> [dead-letter] -> DLQ Processor
4. Retry Strategy & Dead Letters
Retry: 0s -> 1s -> 4s -> 16s -> 60s (cap) -> DLQ
Formula: min(base * 2^attempt + jitter, max_delay)
Retryable: network timeout, 5xx, DB connection, 429
Non-retryable: 4xx, auth, deserialization, biz logic
DLQ: <original>-dlq, retention 30 days, alert >100
Options: replay | replay with fix | skip | escalate
5. Delivery Guarantees & Idempotency
- At-most-once: ack before process (metrics/logs)
- At-least-once: ack after process + DLQ (most tasks)
- Exactly-once: transactional + idempotency keys (payments, financial, orders)
Idempotency: check Redis key, acquire lock (NX+TTL), process, store result (TTL 24h), release lock.
6. Priority & Rate Limiting
P0 critical: password reset, payment (SLA <10s)
P1 high: welcome email, order confirm (SLA <60s)
P2 normal: notifications, image proc (SLA <5m)
P3 low: reports, exports (SLA <1h)
P4 background: cleanup, analytics (SLA <24h)
Rate limit: token bucket or BullMQ limiter
{ max: 100, duration: 60000 }.
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.
- 7d ago First seen · 163 lines · 26 tokens per session scan A a4e3acc2943f
queue is a skill published in the GitHub repository arbazkhan971/godmode (26 stars, last pushed 13d ago), licensed MIT. It adds 26 tokens to every session and 1,364 once invoked, about $0.0001 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 2026-09-03.
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