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 litestar-org/litestar-skills --skill litestar-queuesgit clone --depth 1 https://github.com/litestar-org/litestar-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/litestar-org/litestar-skills/litestar-queues)<a href="https://agentmods.dev/skills/litestar-org/litestar-skills/litestar-queues"><img src="https://agentmods.dev/badge/skills/litestar-org/litestar-skills/litestar-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/litestar-org/litestar-skills/litestar-queues"><img src="https://agentmods.dev/badge/skills/litestar-org/litestar-skills/litestar-queues.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.05985 |
| Opus 5 | $0.00037 | $0.02993 |
| Sonnet 5 | $0.00015 | $0.01197 |
| Haiku 4.5 | $0.00007 | $0.00598 |
Grade A, and why
litestar-queues 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 — 610 lines — stays where its author put it; the contents beside it link to each section on GitHub.
litestar-queues
litestar-queues 0.9.0 is the first-party Litestar worker abstraction for task registration, durable queue state, worker lifecycle, schedules, uniqueness, bounded maintenance, execution dispatch, and application-facing task events.
Keep persistence and placement separate:
- A queue backend stores task records, identities, maintenance coordination, and optional event history.
- An execution backend decides where a claimed task runs.
- Worker wakeups are delivery hints. Persisted queue records remain the source of truth.
Code Style Rules
- Use
QueuePluginto wire lifecycle, application state, DI, task discovery, schedules, workers, and CLI commands. - Put worker settings under
QueueConfig(worker=WorkerConfig(...)). - Inject
QueueServicewithNamedDependency[QueueService]; never use a module-level service from handlers. - Import public core types from
litestar_queues. Import optional backend configuration from its backend submodule (.sqlspec,.advanced_alchemy,.redis,.valkey) and execution configs fromlitestar_queuesor their respective submodules. - Keep persistent-backend arguments and metadata JSON-serializable. Pass stable object IDs instead of large payloads.
- Use PEP 604 unions (
T | None) and async I/O. Prefermsgspecfor event/client DTOs unless the project already uses Pydantic.
Quick Reference
Minimal Plugin Setup
from litestar import Litestar, post
from litestar.di import NamedDependency
from litestar_queues import QueueConfig, QueuePlugin, QueueService, WorkerConfig, task
@task("accounts.sync", queue="accounts", retries=3, timeout=300)
async def sync_account(account_id: str) -> dict[str, str]:
return {"account_id": account_id, "status": "synced"}
@post("/accounts/{account_id:str}/sync")
async def create_sync_job(
account_id: str,
queue_service: NamedDependency[QueueService],
) -> dict[str, str]:
result = await queue_service.enqueue(sync_account, account_id)
return {"task_id": str(result.id), "status": result.status or "queued"}
app = Litestar(
route_handlers=[create_sync_job],
plugins=[
QueuePlugin(
QueueConfig(worker=WorkerConfig(placement="server")),
),
],
)
What ships with it
2 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.
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 · 610 lines · 74 tokens per session scan A 4568f147bb90
litestar-queues is a skill published in the GitHub repository litestar-org/litestar-skills (14 stars, last pushed 22d ago), licensed MIT. It adds 74 tokens to every session and 5,985 once invoked, about $0.0004 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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